CN116818768A - System and method for confirming influencing factors of diseased plants - Google Patents
System and method for confirming influencing factors of diseased plants Download PDFInfo
- Publication number
- CN116818768A CN116818768A CN202310719478.9A CN202310719478A CN116818768A CN 116818768 A CN116818768 A CN 116818768A CN 202310719478 A CN202310719478 A CN 202310719478A CN 116818768 A CN116818768 A CN 116818768A
- Authority
- CN
- China
- Prior art keywords
- disease
- plant
- processing module
- environmental
- detection module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 24
- 201000010099 disease Diseases 0.000 claims abstract description 307
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 307
- 238000001514 detection method Methods 0.000 claims abstract description 150
- 230000007613 environmental effect Effects 0.000 claims abstract description 128
- 238000012545 processing Methods 0.000 claims abstract description 121
- 239000002689 soil Substances 0.000 claims description 51
- 239000000428 dust Substances 0.000 claims description 48
- 238000005286 illumination Methods 0.000 claims description 36
- 238000007781 pre-processing Methods 0.000 claims description 18
- 244000052616 bacterial pathogen Species 0.000 claims description 13
- 238000003066 decision tree Methods 0.000 claims description 8
- 241000894006 Bacteria Species 0.000 claims description 7
- 238000012790 confirmation Methods 0.000 claims description 6
- 238000012549 training Methods 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 2
- 239000003550 marker Substances 0.000 claims 1
- 241000196324 Embryophyta Species 0.000 description 166
- 244000052769 pathogen Species 0.000 description 28
- 230000001717 pathogenic effect Effects 0.000 description 28
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 21
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 20
- 230000003902 lesion Effects 0.000 description 20
- 238000004458 analytical method Methods 0.000 description 14
- 238000001228 spectrum Methods 0.000 description 14
- 230000012010 growth Effects 0.000 description 12
- 241000244206 Nematoda Species 0.000 description 11
- 210000001519 tissue Anatomy 0.000 description 11
- 229910052757 nitrogen Inorganic materials 0.000 description 10
- 235000015097 nutrients Nutrition 0.000 description 10
- 230000029553 photosynthesis Effects 0.000 description 9
- 238000010672 photosynthesis Methods 0.000 description 9
- 239000007789 gas Substances 0.000 description 8
- 230000002829 reductive effect Effects 0.000 description 8
- 208000035473 Communicable disease Diseases 0.000 description 7
- 235000013399 edible fruits Nutrition 0.000 description 6
- 230000008635 plant growth Effects 0.000 description 6
- 238000003860 storage Methods 0.000 description 6
- 208000024891 symptom Diseases 0.000 description 6
- 241000233866 Fungi Species 0.000 description 5
- 230000008859 change Effects 0.000 description 5
- 239000000463 material Substances 0.000 description 5
- 241000700605 Viruses Species 0.000 description 4
- 230000003698 anagen phase Effects 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 4
- 230000015572 biosynthetic process Effects 0.000 description 4
- 230000034994 death Effects 0.000 description 4
- 208000015181 infectious disease Diseases 0.000 description 4
- 230000004060 metabolic process Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 239000000126 substance Substances 0.000 description 4
- 238000003786 synthesis reaction Methods 0.000 description 4
- 208000031888 Mycoses Diseases 0.000 description 3
- 239000003181 biological factor Substances 0.000 description 3
- 239000003795 chemical substances by application Substances 0.000 description 3
- 229930002875 chlorophyll Natural products 0.000 description 3
- 235000019804 chlorophyll Nutrition 0.000 description 3
- ATNHDLDRLWWWCB-AENOIHSZSA-M chlorophyll a Chemical compound C1([C@@H](C(=O)OC)C(=O)C2=C3C)=C2N2C3=CC(C(CC)=C3C)=[N+]4C3=CC3=C(C=C)C(C)=C5N3[Mg-2]42[N+]2=C1[C@@H](CCC(=O)OC\C=C(/C)CCC[C@H](C)CCC[C@H](C)CCCC(C)C)[C@H](C)C2=C5 ATNHDLDRLWWWCB-AENOIHSZSA-M 0.000 description 3
- 239000003086 colorant Substances 0.000 description 3
- 230000006378 damage Effects 0.000 description 3
- 230000007423 decrease Effects 0.000 description 3
- 230000007812 deficiency Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000012377 drug delivery Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 230000002458 infectious effect Effects 0.000 description 3
- 230000007774 longterm Effects 0.000 description 3
- 244000005700 microbiome Species 0.000 description 3
- 239000002245 particle Substances 0.000 description 3
- 230000000243 photosynthetic effect Effects 0.000 description 3
- 230000029058 respiratory gaseous exchange Effects 0.000 description 3
- 230000005068 transpiration Effects 0.000 description 3
- 208000035143 Bacterial infection Diseases 0.000 description 2
- 241000238631 Hexapoda Species 0.000 description 2
- 206010062701 Nematodiasis Diseases 0.000 description 2
- 208000031662 Noncommunicable disease Diseases 0.000 description 2
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 2
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 2
- 241000209140 Triticum Species 0.000 description 2
- 235000021307 Triticum Nutrition 0.000 description 2
- 230000002411 adverse Effects 0.000 description 2
- 238000012271 agricultural production Methods 0.000 description 2
- 230000006806 disease prevention Effects 0.000 description 2
- 235000016709 nutrition Nutrition 0.000 description 2
- 230000035764 nutrition Effects 0.000 description 2
- 231100000915 pathological change Toxicity 0.000 description 2
- 230000036285 pathological change Effects 0.000 description 2
- 229910052698 phosphorus Inorganic materials 0.000 description 2
- 239000011574 phosphorus Substances 0.000 description 2
- 239000011591 potassium Substances 0.000 description 2
- 229910052700 potassium Inorganic materials 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 238000003892 spreading Methods 0.000 description 2
- 230000007480 spreading Effects 0.000 description 2
- 230000003612 virological effect Effects 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 206010058314 Dysplasia Diseases 0.000 description 1
- 102000004190 Enzymes Human genes 0.000 description 1
- 108090000790 Enzymes Proteins 0.000 description 1
- 244000252337 Epipremnum pinnatum Species 0.000 description 1
- 206010021143 Hypoxia Diseases 0.000 description 1
- 206010061217 Infestation Diseases 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 241001430197 Mollicutes Species 0.000 description 1
- 241000469320 Nematanthus gregarius Species 0.000 description 1
- 206010028980 Neoplasm Diseases 0.000 description 1
- 208000012868 Overgrowth Diseases 0.000 description 1
- 208000030852 Parasitic disease Diseases 0.000 description 1
- 241000592344 Spermatophyta Species 0.000 description 1
- 241000519995 Stachys sylvatica Species 0.000 description 1
- 229920002472 Starch Polymers 0.000 description 1
- 206010042496 Sunburn Diseases 0.000 description 1
- 241001052560 Thallis Species 0.000 description 1
- 241000607479 Yersinia pestis Species 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 229930002877 anthocyanin Natural products 0.000 description 1
- 235000010208 anthocyanin Nutrition 0.000 description 1
- 239000004410 anthocyanin Substances 0.000 description 1
- 150000004636 anthocyanins Chemical class 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000001580 bacterial effect Effects 0.000 description 1
- 238000009395 breeding Methods 0.000 description 1
- 230000001488 breeding effect Effects 0.000 description 1
- 235000014633 carbohydrates Nutrition 0.000 description 1
- 150000001720 carbohydrates Chemical class 0.000 description 1
- 230000004663 cell proliferation Effects 0.000 description 1
- 235000013339 cereals Nutrition 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000002845 discoloration Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000009746 freeze damage Effects 0.000 description 1
- 230000002538 fungal effect Effects 0.000 description 1
- 239000003292 glue Substances 0.000 description 1
- 230000007954 hypoxia Effects 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 230000036244 malformation Effects 0.000 description 1
- 239000000618 nitrogen fertilizer Substances 0.000 description 1
- 102000039446 nucleic acids Human genes 0.000 description 1
- 108020004707 nucleic acids Proteins 0.000 description 1
- 150000007523 nucleic acids Chemical class 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 239000005416 organic matter Substances 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 230000003071 parasitic effect Effects 0.000 description 1
- 230000036961 partial effect Effects 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 150000003904 phospholipids Chemical class 0.000 description 1
- 230000035479 physiological effects, processes and functions Effects 0.000 description 1
- 230000035790 physiological processes and functions Effects 0.000 description 1
- 231100000614 poison Toxicity 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 239000000843 powder Substances 0.000 description 1
- 230000002062 proliferating effect Effects 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 239000010453 quartz Substances 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 230000002786 root growth Effects 0.000 description 1
- 238000009394 selective breeding Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 1
- 210000004872 soft tissue Anatomy 0.000 description 1
- 235000019698 starch Nutrition 0.000 description 1
- 239000008107 starch Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000001954 sterilising effect Effects 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 230000008961 swelling Effects 0.000 description 1
- 230000019432 tissue death Effects 0.000 description 1
- 239000003440 toxic substance Substances 0.000 description 1
- 239000003053 toxin Substances 0.000 description 1
- 231100000765 toxin Toxicity 0.000 description 1
- SWGJCIMEBVHMTA-UHFFFAOYSA-K trisodium;6-oxido-4-sulfo-5-[(4-sulfonatonaphthalen-1-yl)diazenyl]naphthalene-2-sulfonate Chemical compound [Na+].[Na+].[Na+].C1=CC=C2C(N=NC3=C4C(=CC(=CC4=CC=C3O)S([O-])(=O)=O)S([O-])(=O)=O)=CC=C(S([O-])(=O)=O)C2=C1 SWGJCIMEBVHMTA-UHFFFAOYSA-K 0.000 description 1
- 230000002792 vascular Effects 0.000 description 1
- 235000013311 vegetables Nutrition 0.000 description 1
- 230000017260 vegetative to reproductive phase transition of meristem Effects 0.000 description 1
- 210000003462 vein Anatomy 0.000 description 1
- 238000004383 yellowing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/188—Vegetation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/10—Image acquisition
- G06V10/12—Details of acquisition arrangements; Constructional details thereof
- G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
- G06V10/141—Control of illumination
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P60/00—Technologies relating to agriculture, livestock or agroalimentary industries
- Y02P60/14—Measures for saving energy, e.g. in green houses
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P60/00—Technologies relating to agriculture, livestock or agroalimentary industries
- Y02P60/20—Reduction of greenhouse gas [GHG] emissions in agriculture, e.g. CO2
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P60/00—Technologies relating to agriculture, livestock or agroalimentary industries
- Y02P60/20—Reduction of greenhouse gas [GHG] emissions in agriculture, e.g. CO2
- Y02P60/21—Dinitrogen oxide [N2O], e.g. using aquaponics, hydroponics or efficiency measures
Abstract
The invention relates to a system and a method for confirming influencing factors of a disease plant, wherein the system comprises a processing module for data processing, a disease detection module for detecting whether the plant has the disease, and an environment detection module for detecting environment parameters, wherein the disease detection module and the environment detection module are connected with the processing module, and the processing module acquires first area information of the plant with the disease and second area information of the plant without the disease based on the disease detection module; the processing module judges influence factors of plant diseases based on whether the environmental parameters of the first area fed back by the environment detection module are within the fluctuation range of the environmental parameters of the second area; the method is based on the system and combines the environmental influence factors and biological germ factors to judge the specific influence factors of the disease plants, so that the problem that the influence factors of the diseases, which are obtained by simply analyzing the disease causes of the plants according to the expression traits of the disease plants, cannot be accurately confirmed is avoided.
Description
Technical Field
The invention relates to the technical field of plant disease detection, in particular to a system and a method for confirming influencing factors of disease plants.
Background
Under the long-term natural and artificial selection of crops, the biological characteristics of the population are formed, the crop has a certain application range for environmental factors around the crop, and a certain growth-eliminating relationship with other biological populations is maintained. If the environmental conditions change drastically, which affects beyond the limits of adaptation inherent in such crops, the normal metabolism of the crop is disturbed and destroyed, and the physiological function or tissue structure undergoes a series of pathological changes, so that it appears morphologically as a disease, which is called onset. The cause of plant disease is known as pathogen, and its pathogenic agents are biological and abiotic. Abiotic factors including climate, soil, cultivation environment, etc., such as too little or too much soil moisture, leading to drought and waterlogging; too low a temperature, causing freeze injury. Biological factors include a variety of microorganisms such as fungi, bacteria, etc., which naturally cannot produce nutrients and require the uptake of nutrients from other living organisms or inanimate organic matter for survival.
Plants are affected by adverse environmental conditions or by pathogenic agents, are disturbed and destroyed in metabolism, produce a series of pathological changes in physiology and tissue structure, show pathological states in external or internal forms, cause plants to fail to grow and develop normally, even lead to death of partial or whole plants, and cause losses to agricultural production, called plant diseases. Plant diseases are formed by the process that a host and a pathogen interact under the influence of external conditions, and a series of changes lead to occurrence of the diseases. It is distinguished from mechanical damage to plants by wind, hail, insects, higher animals, etc. Plant diseases cannot be called as plant tissue death does not have gradual and continuous change process.
The expression traits after plant disease will change to some extent, and there are generally five disease traits: discoloration, spotting, rot, physiological wilting, and deformity. The color change means that chlorophyll in green parts of plants is inhibited to fade or be destroyed and yellow, and certain chlorophyll is inhibited to form and anthocyanin is excessively formed, so that leaves turn red or purple red to form red leaves, and certain leaves are yellow-green to form flower leaves alternately; the spots are various lesions formed by death of cells and tissues of plants due to damage, the lesions have different colors, such as brown spots, black spots, gray spots, white spots and the like, the lesions have different shapes, such as circles, ellipses, fusiform, wheel patterns, irregular shapes and the like, the lesions are limited by veins to form angular spots, the lesions develop along mesophyll to form stripes or streaks, the lesions have obvious edges around the lesions, and the lesions can be enlarged to form larger lesions; the rotting means that the tissue cells of the plant can be rotted by being destroyed and decomposed by the pathogen, and soft tissues with more moisture content are formed by root rot, stem basal rot, spike rot, tuber rot, root rot and the like, and the middle glue layer among the cells is decomposed by enzymes secreted by the pathogen, so that the cells are separated, the tissues collapse to cause soft rot or wet rot, the water is scattered after the rot to become dry rot, the root or stem of the seedling is rotted, the seedling is dead vertically, and the seedling is poured out; the physiological wilting is that the root or vascular bundle of the plant is invaded by a pathogen, a large amount of thalli block a catheter or generate toxin, and prevent or influence moisture transportation, so that leaf withering and withering are caused, the plant dies, the plant rapidly wilts and the leaf is still green, and the leaf is called wilt; deformity means that after a plant is damaged, a proliferative lesion, overgrowth and development, tissue cell proliferation and swelling of a diseased part can occur to generate a tumor; branches or roots are excessively branched, and secondary branches, roots, etc. are generated. Also can generate inhibitory lesions, dysplasia, dwarfing plants or organs, shrinkage, and the like. In addition, the tissue of the diseased part is not balanced in development, and malformation, leaf rolling, fern leaf and the like can be presented; in a plant disease site, various mold, powder, pus, etc. having different colors and shapes are often formed, and these are bacterial cells produced on the surface of a disease part by pathogenic bacteria, and are one of the markers of plant infectious diseases.
In the prior art, as proposed in patent document with publication number CN109801235A, in order to realize automatic identification of scindapsus aureus leaf diseases, single diseased leaves are selected for classification and statistics, meanwhile, leaf spots and anthracnose are classified and counted, characteristic value extraction of leaf colors is carried out through RGB and YCbCr color spaces, whether leaf images to be detected have disease spots or not is judged in a disease spot image area, and the range of the disease spot image area is calculated.
In the prior art, as proposed in patent document CN110135481B, a method and a device for detecting lesions of crops, wherein the detection method comprises a lesion classification step and a lesion identification step; the lesion classification step is used for completing training of a decision tree classifier according to the lesion type labels and the image characteristics of each sample image; generating a plurality of characteristic template images according to the decision tree classifier; the lesion recognition step comprises the steps of calculating the matching degree between the image to be detected and each characteristic template image by using a semi-naive Bayes classifier; and obtaining the lesion type and the lesion possibility data of the crop to be detected according to the matching degree between the image to be detected and each characteristic template image.
The prior art only can detect and analyze disease characteristics caused by fungi, bacteria, viruses, nematodes and other microorganisms to judge the type of diseases, but cannot reasonably judge the transmission path of pathogenic organisms, the sources of pathogenic organisms and the like, and cannot reasonably judge whether the plant diseases are plant diseases caused by pathogenic organisms or non-pathogenic biological plant physiological diseases caused by poor physical or chemical factors, so that inaccurate specific disease sources are found when the plant diseases are prevented, the diseases cannot be subjected to symptomatic drug delivery, idle work which cannot accurately prevent the diseases is possibly performed, waste of manpower and material resources is caused, and even an optimal disease prevention period is missed, so that irrecoverable losses are caused.
Furthermore, there are differences in one aspect due to understanding to those skilled in the art; on the other hand, as the inventors studied numerous documents and patents while the present invention was made, the text is not limited to details and contents of all that are listed, but it is by no means the present invention does not have these prior art features, the present invention has all the prior art features, and the applicant remains in the background art to which the rights of the related prior art are added.
Disclosure of Invention
The technical scheme proposed by the prior art cannot reasonably judge whether the plant disease is caused by pathogenic organisms or non-pathogenic biological plant physiological diseases caused by bad physical or chemical factors, so that inaccurate specific disease sources are found when the plant disease is prevented, the plant disease cannot be subjected to symptomatic drug delivery, idle work which cannot accurately prevent the disease can be performed, waste of manpower and material resources is caused, even the optimal disease prevention period is missed, irrecoverable loss is caused, and therefore, equipment and a method capable of judging the influence factors of the plant disease are needed.
The application provides an influence factor confirmation system of a disease plant, which comprises a processing module for data processing, a disease detection module for detecting whether the plant has the disease, and an environment detection module for detecting environment parameters, wherein the disease detection module and the environment detection module are connected with the processing module, and the processing module acquires first area information of the plant with the disease and second area information of the plant without the disease through the disease detection module; the processing module judges the influence factor of the plant disease based on whether the environmental parameter of the first area fed back by the environmental detection module is within the expected range of the environmental parameter fluctuation of the second area.
The first area information refers to the area of the affected area, the shape of the affected area and the specific position information of the affected area, and the second area information refers to the selected area and the specific position information of the non-affected area. In addition, the environmental factors refer to factors such as temperature, humidity, light, etc., which affect plant growth, and the environmental parameters refer to specific values of the environmental factors, for example, specific temperature parameters of temperature, specific humidity parameters of humidity, etc. The environment parameters of the first area detected by the environment detection module are marked as first environment parameters, and the specific values of dust density, temperature, humidity, illumination intensity, soil content and the like of the first area are at least included in the first environment parameters and are respectively marked as first dust density, first temperature, first humidity, first illumination intensity and first soil content. The environmental parameters of the second area are denoted as second environmental parameters, and the second environmental parameters also at least comprise dust density, temperature, humidity, illumination intensity, soil content and the like of the second area, which are denoted as second dust density, second temperature, second humidity, second illumination intensity, second soil content, respectively.
Preferably, the processing module calculates an expected range of the fluctuation of the second environmental parameter based on the fluctuation values of the second environmental parameters of the plurality of second areas, and the processing module calculates a difference value of the first environmental parameter of the first area and the average value of the second environmental parameters based on the average value of the plurality of second environmental parameters. The expected range at least comprises a dust density fluctuation range, a temperature fluctuation range, a humidity fluctuation range, an illumination intensity fluctuation range and a soil content fluctuation range, and the processing module is based on fluctuation values among a plurality of second dust densities detected by the first detection unit of the environment detection module; the processing module is based on a fluctuation value among a plurality of second temperatures detected by a second detection unit of the environment detection module; the processing module is based on a fluctuation value among a plurality of second humidity detected by a third detection unit of the environment detection module; the processing module is based on a fluctuation value between the second illumination intensities detected by the fourth detection unit of the environment detection module; the processing module is based on the fluctuation value fluctuation range of the soil content between the second soil contents detected by the fifth detection unit of the environment detection module, and mainly detects the contents of three elements of nitrogen, phosphorus and potassium for the fluctuation range of the soil content. Specifically, the fluctuation range is calculated according to the fluctuation value by integrating and counting the second environmental parameters of the plurality of second areas through a statistical method to obtain a numerical distribution situation of the plurality of second environmental parameters, wherein a difference value between a maximum value and a minimum value is taken as the fluctuation range of the second environmental parameters, a specific value (namely, a second temperature) of the temperature of the plurality of second areas is taken as an example of the temperature, the maximum value and the minimum value are screened out, the temperature fluctuation range is calculated according to the difference value between the maximum value and the minimum value, and therefore whether the difference value (namely, the difference value, such as the second difference value) of the average value of the first environmental parameters (such as the first temperature) and the second environmental parameters (such as the second temperature) is within an expected range (such as the temperature fluctuation range) is judged.
Preferably, the processing module judges the influence factor of the plant disease based on whether the difference value is within an expected range: if the difference value is in the expected range, the processing module judges the influence factors of the plant diseases to be biological germs, and the disease detection module is combined to further analyze the type of the biological germs; if the difference value is out of the expected range, the processing module judges that the influence factors of the plant diseases at least comprise environmental factors and further analyzes specific factors of the influence factors of the plant diseases.
Preferably, the environment detection module comprises at least a first detection unit for detecting dust content, a second detection unit for detecting ambient temperature, a third detection unit for detecting ambient humidity, a fourth detection unit for detecting ambient illumination intensity, and a fifth detection unit for detecting soil environment.
Preferably, the processing module calculates at least a first difference value of dust density, a second difference value of temperature, a third difference value of humidity, a fourth difference value of illumination intensity and a fifth difference value of soil content based on environmental parameters fed back by the plurality of detection units.
Preferably, the processing module screens out one or more environmental factors which may be a disease source in the environment based on whether the first difference value, the second difference value, the third difference value, the fourth difference value and the fifth difference value are within the corresponding expected range, and the processing module marks the screened environmental factors. The processing module obtains a dust density difference value between the first area and the second area based on the first dust density and the second dust density, and records the dust density difference value as a first difference value; the processing module obtains a temperature difference value between the first area and the second area based on the first temperature and the second temperature, and records the temperature difference value as a second difference value; the processing module obtains a humidity difference value between the first area and the second area based on the first humidity and the second humidity, and marks the humidity difference value as a third difference value; the processing module obtains an illumination intensity difference value between the first area and the second area based on the first illumination intensity and the second illumination intensity, and records the illumination intensity difference value as a fourth difference value; the processing module obtains a soil content difference value between the first area and the second area based on the first soil content and the second soil content, and marks the difference value as a fifth difference value.
Preferably, the processing module further analyzes whether the disease plant has a biological germ disease based on the performance characteristics of the marking environmental factors, wherein the processing module performs performance judgment of the biological germ after excluding the performance characteristics of the marking environmental factors from the performance characteristics of the disease plant, and judges that the influencing factors of the disease plant include the marking environmental factors and the biological germ if the performance characteristics of the biological germ disease still exist in the performance characteristics of the disease plant after excluding the performance characteristics of the marking environmental factors; if the characteristic of the disease plant excluding the characteristic of the marked environmental factor does not exist in the expression character of the biological germ disease, judging that the influencing factor of the disease plant is the marked environmental factor.
Preferably, the processing module stores a plurality of types of disease plant images in advance and sets corresponding disease type labels; the processing module is used for preprocessing each type of disease plant image and extracting the characteristics, and the extracted characteristics of the disease plant image are used as the basis for judging the type of plant disease; the processing module completes training of the decision tree classifier according to the disease type labels and the image characteristics of each type to obtain the image characteristics corresponding to each plant disease type; and the processing module generates characteristic template images of all types according to the decision tree classifier.
Preferably, the disease detection module at least comprises a collection unit and a preprocessing unit, wherein the collection unit is used for collecting images of plants to be detected and generating images to be detected; the preprocessing unit is used for preprocessing the image to be detected and extracting the characteristics; the preprocessing unit calculates the matching degree between the image to be detected and each characteristic template image; and the preprocessing unit judges whether the plant is diseased or not according to the matching degree between the image to be detected and each characteristic template image.
Based on the technical scheme, the system can combine the environmental influence factors and biological germ factors to judge the specific influence factors of the plant with diseases, avoid the problem that the influence factors of the plant with diseases cannot be accurately confirmed because the disease causes of the plant are simply analyzed through the expression traits of the plant with diseases, especially the situation that the environmental influence factors exist and the biological germ causes the plant diseases, and the influence of the environmental influence factors on the plant is very probably ignored only when the analysis and judgment are carried out through the expression traits of the plant diseases, and the system eliminates and confirms the factors possibly influencing the plant diseases step by step, finally confirms the influence factors of the plant diseases through layer by layer judgment, and gives a corresponding control scheme or solution based on the confirmed influence factors of the plant diseases.
The application also provides a method for confirming the influencing factors of the diseased plant, providing a processing module for data processing, a disease detection module for detecting whether the plant has disease, and the method is characterized in that the method also provides an environment detection module for detecting the environment parameters, the disease detection module and the environment detection module are connected with the processing module, wherein,
the processing module acquires first area information of the plant suffering from the disease and second area information of the plant not suffering from the disease based on the disease detection module;
the processing module judges the influence factor of the plant disease based on whether the environmental parameter of the first area fed back by the environmental detection module is within the expected range of the environmental parameter fluctuation of the second area.
Drawings
FIG. 1 is a simplified schematic diagram of a system for confirming the influence factors of a diseased plant according to the present application;
fig. 2 is a simplified schematic diagram of the execution flow of the influence factor confirmation method of the disease plant of the present application.
List of reference numerals
100: a processing module; 200: a disease detection module; 300: an environment detection module; 210: an acquisition unit; 220: a preprocessing unit; 310: a first detection unit; 320: a second detection unit; 330: a third detection unit; 340: a fourth detection unit; 350: a fifth detecting unit; 360: and a spore detection unit.
Detailed Description
The present application will be described in detail with reference to fig. 1-2.
Fig. 1 is a simplified overall relationship structure diagram of a disease plant influence factor confirming system according to the present application, which at least includes a disease detection module 200 for detecting whether a disease occurs in a plant and an environment detection module 300 for detecting a plant growing environment, wherein the disease detection module 200 and the environment detection module 300 are both connected to a processing module 100 of the system, and the processing module 100 is capable of analyzing influence factors of a disease plant based on detection parameters fed back by the disease detection module 200 and the environment detection module 300 and outputting a corresponding control scheme based on the analysis results.
In an actual environment, the appearance of diseases caused by biological germs and the appearance of diseases caused by environmental reasons are virtually slightly different, but specific influencing factors of the diseases are difficult to accurately distinguish by naked eyes even for people skilled in the art, the detection of the disease types in the prior art usually distinguishes the type of the diseases caused by the biological germs, and the influence of environmental factors which can cause the diseases is ignored, the prior art usually distinguishes the type of the diseases caused by the biological germs according to the appearance of the diseases, and the slight difference of the appearance of the diseases caused by the environmental factors is usually ignored when distinguishing, for example, the nematode diseases are similar to the anagen diseases at the early stage of disease occurrence, but the anagen diseases are often misdiagnosed as the nematode diseases due to the fact that the prior art rarely carries out judgment of whether the plant growth environments are anagen diseases, so that the detection results are inaccurate, and then in a follow-up prevention and treatment scheme, the diseases caused by the anagen diseases are regarded as nematode diseases to be prevented and controlled, and even the death of plants can be missed due to the improper treatment scheme.
Therefore, the system combines the disease detection module 200 and the environment detection module 300, gradually analyzes the cause of the disease, accurately judges the cause of the plant disease, and provides a control and solution scheme for symptomatic drug delivery.
Preferably, the processing module 100 is capable of sending the growth environment parameters for detecting the diseased region and the non-diseased region to the environment detection module 300 based on at least the diseased region and the non-diseased region fed back by the disease detection module 200, and the environment detection module 300 feeds back the diseased environment parameters of the diseased region and the non-diseased environment parameters of the non-diseased region to the processing module 100.
Preferably, the processing module 100 performs a comparative analysis on the diseased environment parameter and the non-diseased environment parameter to obtain a difference value between the diseased environment parameter and the non-diseased environment parameter, and if the difference value meets the expected range, it can primarily determine that the cause of the disease is not caused by environmental factors; if the discrepancy value is outside the expected range, the specific cause that caused the discrepancy value is further analyzed.
Specifically, in the system, a first area represents a diseased area, and a second area represents an unhelpful area; further, the first environmental parameter is used for representing the environmental parameter under the diseased region, and the second environmental parameter is used for representing the environmental parameter under the non-diseased region; the expected range refers to the fluctuation range of the average parameter difference value between the environmental parameters of several non-diseased areas.
Preferably, the specific cause of the difference value is further analyzed by analyzing the difference values of different parameters fed back by different detection units of the environment detection module 300.
Preferably, the environment detection module 300 includes at least a first detection unit 310 for detecting dust concentration within an environment, a second detection unit 320 for detecting an ambient temperature, a third detection unit 330 for detecting an ambient humidity, a fourth detection unit 340 for detecting an ambient illumination intensity, and a fifth detection unit 350 for detecting a soil content.
Preferably, the processing module 100 drives the environmental detection module 300 to detect the environmental parameters of the plurality of second areas based on the plurality of second area information without disease fed back by the disease detection module 200 to obtain a plurality of second environmental parameters, and obtains the expected range of the difference value based on the difference value of the plurality of second environmental parameters. The second environmental parameters include a second dust density, a second temperature, a second humidity, a second light intensity, a second soil content of the non-diseased region. Further, the expected range includes at least a dust density fluctuation range, a temperature fluctuation range, a humidity fluctuation range, an illumination intensity fluctuation range, and a soil content fluctuation range. Still further, the processing module 100 is based on the difference value between the plurality of second dust densities detected by the first detecting unit 310 of the environment detecting module 300; the processing module 100 is based on a temperature fluctuation range of a difference value between the plurality of second temperatures detected by the second detecting unit 320 of the environment detecting module 300; the processing module 100 is based on the difference value between the plurality of second humidity values detected by the third detecting unit 330 of the environment detecting module 300; the processing module 100 is based on the difference value between the second illumination intensities detected by the fourth detection unit 340 of the environment detection module 300; the processing module 100 mainly detects contents of three elements of nitrogen, phosphorus and potassium for a fluctuation range of the soil content based on a difference value between the second soil contents detected by the fifth detection unit 350 of the environment detection module 300.
Preferably, the processing module 100 drives the environmental module to detect the environmental parameter of the first area based on the first area information of the disease fed back by the disease detection module 200 to obtain the first environmental parameter, so as to determine whether the first environmental parameter is within the expected range, if the first environmental parameter is within the expected range, the processing module 100 determines that the disease source of the area is a non-environmental parameter influence; if the first environmental parameter is outside the expected range, the processing module 100 considers that the disease source of the area is possibly affected by the environmental parameter, and the processing module 100 performs further analysis and judgment. Specifically, the first environmental parameter includes a first dust density, a first temperature, a first humidity, a first illumination intensity, and a first soil content.
Preferably, the processing module 100 screens out one or more parameters of the environment that may be a source of disease based on the first environmental parameter and the second environmental parameter.
Specifically, the processing module 100 obtains a dust density difference value between the first area and the second area based on the first dust density and the second dust density, and records the dust density difference value as a first difference value, and the processing module 100 further judges whether the first difference value is within a dust density fluctuation range, and if the first difference value is within the dust density fluctuation range, the possibility of dust causing diseases is eliminated; if the first difference value is outside the range of fluctuation of the dust density, the dust is marked as a cause of possible disease.
Specifically, the processing module 100 obtains a temperature difference value between the first area and the second area based on the first temperature and the second temperature, and records the temperature difference value as a second difference value, and the processing module 100 further judges whether the second difference value is within a temperature fluctuation range, and if the second difference value is within the temperature fluctuation range, the possibility that the temperature causes diseases is eliminated; if the second difference value is outside the temperature fluctuation range, the temperature is marked as a cause that may cause diseases.
Specifically, the processing module 100 obtains a humidity difference value between the first area and the second area based on the first humidity and the second humidity, and marks the difference value as a third difference value, and the processing module 100 further judges whether the third difference value is within a humidity fluctuation range, and if the third difference value is within the humidity fluctuation range, the possibility that the humidity causes diseases is eliminated; if the third difference value is outside the humidity fluctuation range, the humidity is marked as a cause of possible diseases.
Specifically, the processing module 100 obtains an illumination intensity difference value between the first area and the second area based on the first illumination intensity and the second illumination intensity, and marks the illumination intensity difference value as a fourth difference value, and the processing module 100 further judges whether the fourth difference value is within an illumination intensity fluctuation range, and if the fourth difference value is within the illumination intensity fluctuation range, the possibility that the illumination intensity causes diseases is eliminated; if the fourth difference value is outside the fluctuation range of the illumination intensity, the illumination intensity is marked as the cause possibly causing diseases.
Specifically, the processing module 100 obtains a soil content difference value between the first area and the second area based on the first soil content and the second soil content, and marks the difference value as a fifth difference value, and the processing module 100 further judges whether the fifth difference value is within a fluctuation range of the soil content, and if the fifth difference value is within the fluctuation range of the soil content, the possibility that the soil content causes diseases is eliminated; if the fifth difference value is outside the fluctuation range of the soil content, the soil content is marked as a cause of possible disease.
Preferably, the processing module 100 further judges whether the disease performance trait caused by the environmental cause is identical to the actual disease performance trait of the disease plant based on the one or more environmental causes marked as likely to cause the disease in combination with the disease performance trait of the disease plant obtained by the disease detection module 200, and if the traits are identical, determines that the disease is caused by the one or more environmental causes; if the character expression is different, it is determined that the disease is caused by biological germs and environmental reasons. The processing module 100 outputs a corresponding disease source report based on the final judgment result and gives a corresponding solution in combination with the means for treating diseases conventional in the art.
Preferably, the processing module 100 gives a corresponding solution based on the specific type of environmental factor determined that causes the disease and the specific parameters. For example, there may be a case where plant diseases are caused by both temperature and humidity, specifically, for example, a case where temperature is higher than that in a normal environment and humidity is lower than that in a normal environment, in which case, disease treatment is preferably performed by increasing humidity, because in the case of increasing humidity, there is a certain degree of decrease in temperature, and if both reach normal levels at the same time, the disease is solved; if the temperature is still higher after the humidity reaches the normal level, independently cooling until the humidity reaches the normal level; if the humidity does not reach the normal level and the temperature has decreased to the normal level, the temperature is appropriately raised while the humidification process is performed. Similarly, humidity and dust density have the same relationship, and for condensable dust, the dust density is reduced when the humidity is increased, so when the humidity and the dust density affect plant diseases together, the humidity treatment is preferentially performed, and then the treatment is performed by adopting dust removing equipment such as a dust remover. Accordingly, the temperature and the illumination intensity have a certain relation, and the illumination intensity can be processed preferentially. For environmental parameters affecting diseases alone, such as soil content, it is usually sufficient to perform a means of supplementing, for example, when the N content is insufficient, application of nitrogen fertilizer, or the like.
Preferably, the processing module 100 further analyzes whether the disease plant has a disease of biological germ based on the characteristic of the marked environmental factor, wherein the processing module 100 performs the characteristic judgment of the biological germ after excluding the characteristic of the marked environmental factor from the characteristic of the disease plant, and judges that the influencing factors of the disease plant include the marked environmental factor and the biological germ if the characteristic of the disease plant still exists in the characteristic of the marked environmental factor; if the characteristic of the disease plant excluding the characteristic of the marked environmental factor does not exist in the expression character of the biological germ disease, judging that the influencing factor of the disease plant is the marked environmental factor. Specifically, the processing module 100 extracts and stores the characteristic value of each trait in a local or cloud in advance, and each characteristic value corresponds to a disease factor that may cause the characteristic value of the disease shape, that is, one or more disease factors can be obtained according to the characteristic value, and then, whether the biological germ factor exists is determined according to the logic.
Preferably, the disease detection module 200 is capable of determining at least whether a plant is diseased, and particularly whether a plant is diseased based on a performance trait analysis of an image of a growth state of the ingested plant. When the plant is diseased, the disease expression character is obvious, and the specific reference can be made to the follow-up description.
Preferably, plant diseases caused by biological pathogens mainly include fungal diseases, bacterial diseases, and viral and nematode diseases. From the appearance of symptoms on the affected part, fungal diseases often can be seen in symptoms such as mildews, powdery matters, small black spots (grains) and the like; sticky substances (pus) can be seen when bacterial diseases are moist; viral and nematode diseases have no symptoms, but have special symptoms such as flowers and leaves, shrinkage, dwarfing, clubroot and the like.
Preferably, the environmental parameters of the resulting plant disease mainly include dust density, temperature, humidity, illumination intensity and soil content.
In particular, dust in the environment may clog plant pores, causing hypoxia to the plant leaves; dust may shade light irradiated to plants, affect photosynthesis of the plants, and cause poor growth and even death of the plants; some dust even contains toxic substances, which can directly cause harm to plant cells; the dust contains a large amount of microorganisms, and bacteria are easy to breed on the surface of plants, so that plant diseases are caused. Therefore, the difference value of the dust content is an important detection parameter.
Specifically, the temperature is an important environmental parameter for normal growth of plants, and the influence of the temperature on the growth is comprehensive, so that the growth of the plants can be influenced by influencing the metabolic processes of photosynthesis, respiration, transpiration and the like, and also by influencing the metabolic processes of synthesis, transportation and the like of organic matters. At high temperature, the respiration of the plant body is strong, so that the photosynthesis can be inhibited, the synthesis of organic nutrition can be reduced, the respiration can be accelerated, a large amount of organic nutrition is consumed by decomposition, and a large amount of curling symptoms appear on the leaves; under high temperature of strong light, the transpiration of plants is large, especially the temperatures of leaves and fruits are high, and if water is not timely supplied, sunburn symptoms can be directly caused, the leaves are withered and yellow, and the fruit surfaces are dry, white and thin skin.
In particular, the photosynthesis requirements of crops are that air, relative humidity and soil humidity are suitable. Most crops have a suitable air humidity for photosynthesis of 60% -85%. When the air relative humidity is less than 40% or more than 90%, photosynthesis is hindered, thereby adversely affecting the growth and development. The photosynthesis of vegetable crops requires the relative water content of soil, which is generally 70% -95% of the maximum water holding capacity in the field, and overdry or overdry is unfavorable for photosynthesis. Serious insufficient water content is easy to cause wilting, leaf scorching and other phenomena. The plant is She Zixiao, the mechanical tissue is more formed, the fruit expansion speed is low, the quality is poor and the yield is reduced due to the long-term insufficient water. Insufficient water in the flowering period causes flower and fruit dropping. When the water is excessive, the root system chokes, changes color and decays due to the lack of oxygen in the soil, the overground part can become yellow, and the whole plant dies when serious. The high humidity and high temperature can easily cause the plant to overgrow, and the high humidity and low temperature can easily induce retting root, so that the plant dies.
In particular, the effect of light on the leaves is mainly photosynthesis. Under weak light, the leaf area is large and thin, and the increase of the leaf area seems to compensate the decrease of the photosynthetic rate of the unit leaf area; the leaves grown under intense light conditions are smaller and thicker. The influence of illumination on plant growth is also shown on the influence on the root cap ratio, which is an index for measuring the growth correlation of the overground part and the underground part of the plant, namely the ratio of the weight of the underground part to the weight of the overground part; the illumination is enhanced within a certain range, the accumulation of photosynthetic products is increased, and the carbohydrate supply of the underground part is improved, so that the growth of roots is promoted, and the root cap ratio is increased; when the illumination is insufficient, photosynthetic products downwards conveyed by the overground parts are reduced, root growth is affected, and the growth of the overground parts is relatively less affected, so that the root cap ratio is reduced; under strong light, the relative humidity in the air is reduced, the transpiration of the overground part of the plant is increased, the water potential in the tissue is reduced, and the growth of stem leaves is inhibited, so that the root-cap ratio is increased.
Specifically, soil nutrient detection is an important step in the current agricultural production, and the growth of crops needs to absorb enough nutrients from the soil, if the nutrients in the soil are insufficient, the crops cannot absorb enough nutrients, the yield of the crops also decreases, and long-term insufficient nutrients can even cause diseases. Soil nutrient detection essentially detects the content of nutrient elements in the soil. Specifically, taking the nitrogen content in soil as an example, during nitrogen deficiency, the synthesis of substances such as protein, nucleic acid, phospholipid and the like is hindered, the plant growth is short, branches and tillers are few, leaves are small and thin, flowers and fruits are few, and the fruits are easy to fall off; the nitrogen deficiency also affects the synthesis of chlorophyll, so that branches and leaves turn yellow, leaves are early-aged and even dry, the yield is reduced, and due to the large mobility of nitrogen in plants, the nitrogen in old leaves can be transported to tender tissues for recycling after being decomposed, so that the leaves gradually start to turn yellow upwards from lower leaves when nitrogen deficiency occurs. When the nitrogen is excessive, the leaves are large and dark green, are soft and are scattered, and the plants are overgrown; in addition, when the nitrogen is too much, the sugar content in the plant body is relatively insufficient, the mechanical tissue in the stalk is undeveloped, and the stalk is easy to lodge and be damaged by diseases and insect pests.
Example 2
The embodiment is improved and supplemented on the basis of embodiment 1, and repeated contents are not repeated.
The embodiment also provides a method for confirming the influence factors of the disease plants by using the system.
A method for confirming influencing factors of a disease plant, which provides a processing module 100 for data processing and a disease detection module 200 for detecting whether the plant has the disease, is characterized in that the method also provides an environment detection module 300 for detecting environment parameters, the disease detection module 200 and the environment detection module 300 are connected with the processing module 100, wherein the processing module 100 acquires first area information of the plant having the disease and second area information of the plant not having the disease based on the disease detection module 200; the processing module 100 judges the influence factor of the plant disease based on whether the environmental parameter of the first region fed back by the environment detection module 300 is within the environmental parameter fluctuation range of the second region.
Preferably, according to the flowchart of the influence factor confirmation method of the disease plant of the present application shown in fig. 2, the influence factor confirmation method of the disease plant of the present application is performed as follows:
s100: the disease detection module 200 acquires information of a plurality of second areas without diseases;
S200: the environment detection module 300 acquires second environment parameter information of a plurality of second areas;
s300: the processing module 100 obtains an expected range of difference values of environmental parameters of different second areas based on the plurality of second environmental parameter information;
s400: the disease detection module 200 obtains information of a first region suffering from a disease;
s500: the environment detection module 300 acquires first environment parameter information of a first area;
s600: the processing module 100 calculates a difference value between the first environmental parameter information and the second environmental parameter information, and compares whether the difference value is within an expected range; if the current value is within the expected range, the process proceeds to step S700; if not, go to step S800;
s700: the processing module 100 analyzes the type of specific biological germs based on the plant disease expression trait image of the first region fed back by the disease detection module 200;
s800: the processing module 100 further analyzes the difference value between the first environmental parameter information and the second environmental parameter information, marks the environmental parameters possibly causing plant lesions, and compares the marked one or more environmental parameters with the actual disease expression;
S900: the processing module 100 outputs a disease type and/or disease source report based on the analysis result and gives a corresponding control scheme on the report.
Preferably, in step S700, the analysis result is given as the type of biological pathogen, and the analysis result of step S700 is performed under the condition that it is confirmed that the non-environmental factor affects the plant disease.
Preferably, in step S800, the marked environmental parameter is one of the causes of the plant disease, but it cannot be confirmed that only the environmental factor causes the plant disease, and it is necessary to analyze the expression traits of the plant disease further. The specific analysis logic is that the actual expression character of the plant disease is compared with the expected expression character caused by the marking environment factor, and if the actual expression character of the plant disease accords with the expected expression character caused by the marking environment factor, the influence factor of the plant disease can be judged to be the marking environment factor; if the actual expression trait of the plant disease does not accord with the expected expression trait caused by the marking environmental factor, and the actual expression trait of the plant disease exists both the expected expression trait of the plant disease caused by the pathogenic bacteria and the expected expression trait caused by the marking environmental factor, judging that the plant disease is caused by the pathogenic bacteria and the environmental factor together.
Specifically, taking a disease plant with nematodiasis and hypocrellinia as an example, the disease manifestation of the nematodiasis and the hypocrellinia is mainly plant growth weakness, dwarf yellowing is not detected by the fifth detection unit 350, and whether the soil content is hypocrellinia can not be judged; the application detects whether the plant lacks the element, marks the element lack as an environmental factor possibly causing the disease under the condition of the element lack, compares the actual performance character of the plant disease with the expected performance character of the element lack disease, carefully compares the actual performance character of the plant disease with the expected performance character of the element lack disease, and takes wheat as an example to find out the difference between the nematode disease and the element lack disease, the seeds of the wheat with the nematode disease can not form starch, and the seed gall can be formed, so that the system judges that the plant with the disease has the nematode disease and the element lack disease at the same time, and gives out an analysis result and a corresponding solution.
Based on the technical scheme, the system can combine the environmental influence factors and biological germ factors to judge the specific influence factors of the plant with diseases, avoid the problem that the influence factors of the plant with diseases cannot be accurately confirmed because the disease causes of the plant are simply analyzed through the expression traits of the plant with diseases, especially the situation that the environmental influence factors exist and the biological germ causes the plant diseases, and the influence of the environmental influence factors on the plant is very probably ignored only when the analysis and judgment are carried out through the expression traits of the plant diseases, and the system eliminates and confirms the factors possibly influencing the plant diseases step by step, finally confirms the influence factors of the plant diseases through layer by layer judgment, and gives a corresponding control scheme or solution based on the confirmed influence factors of the plant diseases.
Preferably, the disease detection module 200 determines whether a disease occurs in the plants in the area in the following manner:
the method comprises the steps that image acquisition is carried out on plants to be detected through an acquisition unit 210 of a disease detection module 200, and an image to be detected is generated;
the preprocessing unit 220 of the disease detection module 200 performs preprocessing operation on the image to be detected to improve the image quality of the image to be detected;
the preprocessing unit 220 of the disease detection module 200 performs feature extraction operation on the image to be detected;
the preprocessing unit 220 of the disease detection module 200 calculates the matching degree between the image to be detected and each characteristic template image by using a semi-naive bayes classifier;
the preprocessing unit 220 of the disease detection module 200 determines whether a plant is diseased or not according to the degree of matching between the image to be detected and each characteristic template image.
Preferably, the environment detection module 300 obtains the environmental parameters of the first area and/or the second area by using at least the first detection unit 310, the second detection unit 320, the third detection unit 330, the fourth detection unit 340 and the fifth detection unit 350, and specifically, the first environmental parameters of the first area include a first dust density, a first temperature, a first humidity, a first illumination intensity and a first soil content; the second environmental parameters of the second zone include a second dust density, a second temperature, a second humidity, a second illumination intensity, and a second soil content.
Specifically, the first detection unit 310 may be configured as a dust concentration sensor, the second detection unit 320 may be configured as a temperature sensor, the third detection unit 330 may be configured as a humidity sensor, the fourth detection unit 340 may be configured as an illumination intensity sensor, and the fifth detection unit 350 may be configured as a soil nutrient detector.
Preferably, the specific analysis mode of the disease image fed back by the disease detection module by the processing module 100 is as follows:
the processing module 100 collects a plurality of disease plant images in advance and sets disease type tags for the respective disease plant images;
the processing module 100 performs a preprocessing operation on each type of disease plant image to improve image quality;
the processing module 100 performs feature extraction operation on each type of disease plant image, and the extracted features of the disease plant image are used as the basis for judging the type of plant disease;
the processing module 100 completes training of the decision tree classifier according to the disease type labels and the image characteristics of each type to obtain the image characteristics corresponding to each plant disease type;
the processing module 100 generates a plurality of characteristic template images according to the decision tree classifier, wherein each characteristic template image corresponds to each disease type;
The processing module 100 performs comparison analysis on the disease images related to the foregoing step S700 and step S800 based on the feature template image.
It is to be noted that the plant diseases may be classified into non-infectious diseases (also called non-infectious or physiological diseases) and infectious diseases (also called infectious or parasitic diseases) according to the nature of the plant diseases caused by non-biological factors and biological factors, and the disease caused by the biological pathogen according to the present application may be called infectious disease, and after confirming the main influencing factor of the disease, if the disease caused by the biological pathogen, it is necessary to further judge the propagation path of the disease caused by the biological pathogen.
For non-infectious diseases, the diseases are mainly caused by environmental factors, are not infectious, and can be prevented and treated by pertinently adjusting environmental parameters of the environmental factors after the environmental factors affecting plant diseases are found.
For infectious diseases, which are caused by pathogenic organisms, pathogenic agents causing infectious diseases include fungi, bacteria, viruses, mycoplasmas, nematodes, parasitic seed plants, and the like. Important diseases occurring in agriculture are mainly caused by fungi, bacteria, viruses and nematodes, wherein the diseases caused by fungi are the most widespread. The pathogens produce a large number of propagules and require efficient mediators or power to spread them in a short period of time, causing disease epidemics. The air flow and the running water have a great relation with disease epidemic. Spores of pathogens possibly filled in the air can be attached to the leaf surfaces of different plants for growth when the gases flow, so that diseases are generated, and if the diseases are not prevented and controlled in time, the diseases can spread rapidly; in addition, the running water can spread the pathogenic matters widely in the field, and the pathogenic matters can also be spread through the soil. The means of spreading the above-mentioned pathogens are easily detected and easily blocked, and the prevention and control of the pathogen spreading of the running water and the soil are conventional means for those skilled in the art, but the detection of spores of pathogens scattered in the air is difficult.
In air, agricultural airborne fungal diseases are in the category of bioaerosols, spores are the main breeding means, the number of produced is large, the particle size ranges from 5 μm to 30 μm, the mass is usually picogram (10 -12 g) The primary or secondary infestation can be easily accomplished by air flow. Such airborne fungal spores often do not need to adhere to dust or droplets like bacteria or viruses, but can exist in the air independently in the form of smaller particles. And thus is necessary for detection of pathogenic spores in air.
Preferably, the environment detection module 300 is further capable of deciding whether to activate the spore detection unit 360 based on the analysis result of the processing module 100. The spore detection unit 360 can detect whether or not there are pathogenic spores exceeding a normal concentration in the air environment, excluding dust, to determine whether or not the pathogenic can be transmitted through the air. Specifically, in the case where the processing module 100 judges that the disease-affecting factor is an environmental-affecting factor, further judges the type of the environmental factor, and in the case where it is confirmed that there is a trait in the affecting factor that is caused by dust factors and that the disease plant exhibits the trait having the pathogen disease, the environmental detection module 300 activates the spore detection unit 360. Accordingly, under the condition that other environmental factors and pathogenic bacteria are influenced together, the pathogen detection of the conventional mode is carried out on flowing water flowing through plants and soil of the plants, corresponding reports and solutions are given, and the detection mode of the flowing water and the soil is simple for those skilled in the art and is not repeated here.
Preferably, the spore detection unit 360 mainly detects the spore content in the air.
Preferably, the spore detection unit 360 obtains air samples in the second areas based on the information of the second areas fed back by the processing module 100, and estimates the spore content in the air through the terahertz technology. The difference between the spore and the common dust is that the germ spore generally has a certain water content of about 60% -80%, the pollen of the plant has a water content of about 15% -30%, the common dust does not contain water, the water in the particle objects is not easy to measure by a humidity sensor, the terahertz has better sensitivity to weak change of the water, and the terahertz spectrum intensity value in the air can be measured, so that the content of the germ spore is judged.
Preferably, the spore detection unit 360 obtains terahertz spectrum intensity values in the air in a plurality of second areas, the processing module 100 calculates a spectrum average value of terahertz intensity and a fluctuation range based on the terahertz spectrum intensity values in the second areas fed back by the spore detection unit 360, the spore detection unit 360 obtains the terahertz spectrum intensity values in the air in the first areas, the processing module 100 compares the terahertz spectrum intensity values in the first areas with the spectrum average value based on the terahertz spectrum intensity values in the first areas to obtain difference values of the terahertz spectrum intensity values and the spectrum average value, the difference values can be recorded as sixth difference values, whether the sixth difference values are in the fluctuation range is judged, and if the sixth difference values are in the fluctuation range, the germ spore content in the air in the first areas is judged to be normal; if the pathogen in the first area is out of the fluctuation range, judging that the pathogen in the first area can at least spread through air, and sterilizing the air environment.
Preferably, the spore detection unit 360 includes at least a sample gas storage chamber for storing the sampled air, the sample gas storage chamber is made of a material that does not absorb or absorbs less terahertz waves, such as quartz, and the accuracy of detecting the moisture data in the air by the terahertz spectrum is ensured due to the fact that the sample gas storage chamber itself absorbs little terahertz waves. One end of the sample gas storage cavity is provided with a terahertz spectrum transmitting end, and the other end of the sample gas storage cavity opposite to the transmitting end is provided with a terahertz spectrum receiving end. The receiving end is used for acquiring terahertz spectrum information of air in the sample gas storage cavity, and further effective detection of the water content of the gas is achieved, wherein the receiving end can transmit the information to the processing module 100 for processing analysis.
Based on the technical scheme of the application, whether to start the corresponding pathogen detection unit is determined on the premise of confirming the influencing factors, pathogen detection is avoided under the condition that the pathogen is not infectious disease, and the possible transmission path of the pathogen is detected in a targeted mode, especially the air transmission path, so that manpower and material resources are saved, and particularly, a great deal of manpower and material resources and time cost are required for detecting the water flow, soil or air transmission path.
It should be noted that the above-described embodiments are exemplary, and that a person skilled in the art, in light of the present disclosure, may devise various solutions that fall within the scope of the present disclosure and fall within the scope of the present disclosure. It should be understood by those skilled in the art that the present description and drawings are illustrative and not limiting to the claims. The scope of the invention is defined by the claims and their equivalents.
Claims (10)
1. A disease plant influence factor confirmation system includes a processing module (100) for data processing, a disease detection module (200) for detecting whether a plant has a disease,
it is characterized in that the method comprises the steps of,
the system further comprises an environment detection module (300) for detecting an environment parameter, the disease detection module (200) and the environment detection module (300) being connected to the processing module (100), wherein,
the processing module (100) acquires first area information of the plant suffering from the disease and second area information of the plant not suffering from the disease through the disease detection module (200);
the processing module (100) can drive the environment detection module (300) to acquire the environment parameters of the first area and the second area based on the first area information and the second area information, and the processing module (100) can judge the influence factors of the plant diseases based on whether the environment parameters of the first area fed back by the environment detection module (300) are within the expected range of the environment parameter fluctuation of the second area.
2. The system according to claim 1, wherein the processing module (100) calculates an expected range of fluctuation of the second environmental parameter based on the fluctuation values of the second environmental parameters of the plurality of second areas, and wherein the processing module (100) calculates a difference value between the first environmental parameter of the first area and the second environmental parameter of the second area based on an average value of the plurality of second environmental parameters, and further judges whether the difference value is within the expected range.
3. The influence factor determination system of a diseased plant according to claim 1 or 2, characterized in that the processing module (100) judges an influence factor of a plant disease based on whether the difference value is within the expected range:
if the difference value is within the expected range, the processing module (100) judges the influence factors of the plant diseases to be biological germs, and the disease detection module (200) is combined to further analyze the type of the biological germs;
if the difference value is outside the expected range, the processing module (100) judges that the influence factors of the plant diseases at least comprise environmental factors and further analyzes specific factors of the influence factors of the plant diseases.
4. A disease plant influencing factor confirmation system according to any one of claims 1-3, wherein the environment detection module (300) comprises at least a first detection unit (310) for detecting dust content, a second detection unit (320) for detecting ambient temperature, a third detection unit (330) for detecting ambient humidity, a fourth detection unit (340) for detecting ambient illumination intensity, and a fifth detection unit (350) for detecting soil environment.
5. The system according to any one of claims 1 to 4, wherein the difference values calculated by the processing module (100) based on the environmental parameters fed back by the plurality of detecting units include at least a first difference value of dust density, a second difference value of temperature, a third difference value of humidity, a fourth difference value of illumination intensity, and a fifth difference value of soil content.
6. The system for confirming an influencing factor for a diseased plant according to any one of claims 1 to 5, wherein the processing module (100) screens out one or more environmental factors which may be a source of disease in the environment based on whether the first, second, third, fourth and fifth difference values are within a corresponding expected range, and the processing module (100) marks the screened out environmental factors.
7. The system for confirming influence factors of a disease plant according to any one of claims 1 to 6, wherein the processing module (100) further analyzes whether the disease plant has a biological germ disease based on the behavior of the marker environmental factors, wherein,
the processing module (100) excludes the characteristic of the expression of the marking environmental factors from the expression of the disease plant and then carries out the expression judgment of biological bacteria,
if the disease plant has the characteristic of biological germ disease in the expression character after the characteristic of the environmental factor is excluded, judging that the influencing factors of the disease plant comprise the marking environmental factor and biological germ;
if the characteristic of the disease plant excluding the characteristic of the marked environmental factor does not exist in the expression character of the biological germ disease, judging that the influencing factor of the disease plant is the marked environmental factor.
8. The system for confirming influence factors of diseased plants according to any one of claims 1 to 7, characterized in that the processing module (100) stores several types of diseased plant images in advance and sets corresponding disease type tags;
the processing module (100) preprocesses each type of disease plant image and extracts characteristics, and the extracted characteristics of the disease plant image are used as the basis for judging the type of plant disease;
The processing module (100) completes training of the decision tree classifier according to disease type labels and image characteristics of each type to obtain image characteristics corresponding to each plant disease type;
the processing module (100) generates feature template images of various types according to the decision tree classifier.
9. The system for confirming influence factors of diseased plants according to any of claims 1 to 8, characterized in that the disease detection module (200) comprises at least a collection unit (210) and a preprocessing unit (220), wherein,
the acquisition unit (210) acquires images of plants to be detected and generates images to be detected;
the preprocessing unit (220) is used for preprocessing an image to be detected and extracting characteristics;
the preprocessing unit (220) calculates the matching degree between the image to be detected and each characteristic template image;
the preprocessing unit (220) judges whether the plant is diseased or not according to the matching degree between the image to be detected and each characteristic template image.
10. A method for confirming influencing factors of a diseased plant, comprising providing a processing module (100) for data processing, a disease detection module (200) for detecting whether a plant has disease, characterized in that the method further provides an environment detection module (300) for detecting an environment parameter, the disease detection module (200) and the environment detection module (300) being connected to the processing module (100), wherein,
The processing module (100) acquires first area information of a plant suffering from a disease and second area information of the plant not suffering from the disease based on the disease detection module (200);
the processing module (100) judges an influence factor of the plant disease based on whether or not the environmental parameter of the first area fed back by the environmental detection module (300) is within an expected range of fluctuation of the environmental parameter of the second area.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2022109203839 | 2022-08-01 | ||
| CN202210920383 | 2022-08-01 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116818768A true CN116818768A (en) | 2023-09-29 |
Family
ID=87488090
Family Applications (13)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310649723.3A Pending CN116584267A (en) | 2022-08-01 | 2023-06-02 | Light source arrangement method for promoting growth of overground part or underground part of plant |
| CN202310649702.1A Pending CN116897723A (en) | 2022-08-01 | 2023-06-02 | Light source arrangement system and method based on plant photosensitive gene regulation |
| CN202310685565.7A Pending CN116548203A (en) | 2022-08-01 | 2023-06-09 | Light source adjusting system and method based on plant light energy absorptivity |
| CN202310685548.3A Pending CN116746390A (en) | 2022-08-01 | 2023-06-09 | Plant growth regulation system and method based on plant reflection spectrum |
| CN202310722977.3A Pending CN116569826A (en) | 2022-08-01 | 2023-06-16 | Plant cultivation system and method containing root microorganisms |
| CN202310719478.9A Pending CN116818768A (en) | 2022-08-01 | 2023-06-16 | System and method for confirming influencing factors of diseased plants |
| CN202310751919.3A Pending CN116779029A (en) | 2022-08-01 | 2023-06-25 | Breeding screening system and method based on phenotype expression |
| CN202310753288.9A Pending CN116820002A (en) | 2022-08-01 | 2023-06-25 | Screening system and method for directional cultivation of plants |
| CN202310795551.0A Pending CN116868878A (en) | 2022-08-01 | 2023-06-30 | Aeroponic cultivation system and method based on air circulation |
| CN202310795550.6A Pending CN116616164A (en) | 2022-08-01 | 2023-06-30 | Aeroponic culture device |
| CN202310812494.2A Pending CN116569712A (en) | 2022-08-01 | 2023-07-04 | Seeding device and method for adjusting seed landfill depth |
| CN202310883688.1A Pending CN117011706A (en) | 2022-08-01 | 2023-07-18 | System and method for studying and judging growth condition of plant tissue |
| CN202310903074.5A Pending CN116671431A (en) | 2022-08-01 | 2023-07-21 | Plant full-growth period regulation and control system and method |
Family Applications Before (5)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310649723.3A Pending CN116584267A (en) | 2022-08-01 | 2023-06-02 | Light source arrangement method for promoting growth of overground part or underground part of plant |
| CN202310649702.1A Pending CN116897723A (en) | 2022-08-01 | 2023-06-02 | Light source arrangement system and method based on plant photosensitive gene regulation |
| CN202310685565.7A Pending CN116548203A (en) | 2022-08-01 | 2023-06-09 | Light source adjusting system and method based on plant light energy absorptivity |
| CN202310685548.3A Pending CN116746390A (en) | 2022-08-01 | 2023-06-09 | Plant growth regulation system and method based on plant reflection spectrum |
| CN202310722977.3A Pending CN116569826A (en) | 2022-08-01 | 2023-06-16 | Plant cultivation system and method containing root microorganisms |
Family Applications After (7)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310751919.3A Pending CN116779029A (en) | 2022-08-01 | 2023-06-25 | Breeding screening system and method based on phenotype expression |
| CN202310753288.9A Pending CN116820002A (en) | 2022-08-01 | 2023-06-25 | Screening system and method for directional cultivation of plants |
| CN202310795551.0A Pending CN116868878A (en) | 2022-08-01 | 2023-06-30 | Aeroponic cultivation system and method based on air circulation |
| CN202310795550.6A Pending CN116616164A (en) | 2022-08-01 | 2023-06-30 | Aeroponic culture device |
| CN202310812494.2A Pending CN116569712A (en) | 2022-08-01 | 2023-07-04 | Seeding device and method for adjusting seed landfill depth |
| CN202310883688.1A Pending CN117011706A (en) | 2022-08-01 | 2023-07-18 | System and method for studying and judging growth condition of plant tissue |
| CN202310903074.5A Pending CN116671431A (en) | 2022-08-01 | 2023-07-21 | Plant full-growth period regulation and control system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (13) | CN116584267A (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117063826B (en) * | 2023-10-17 | 2023-12-19 | 寿光市茂藤农业科技有限公司 | Plant stereoscopic water planting system based on plant morphology monitoring |
-
2023
- 2023-06-02 CN CN202310649723.3A patent/CN116584267A/en active Pending
- 2023-06-02 CN CN202310649702.1A patent/CN116897723A/en active Pending
- 2023-06-09 CN CN202310685565.7A patent/CN116548203A/en active Pending
- 2023-06-09 CN CN202310685548.3A patent/CN116746390A/en active Pending
- 2023-06-16 CN CN202310722977.3A patent/CN116569826A/en active Pending
- 2023-06-16 CN CN202310719478.9A patent/CN116818768A/en active Pending
- 2023-06-25 CN CN202310751919.3A patent/CN116779029A/en active Pending
- 2023-06-25 CN CN202310753288.9A patent/CN116820002A/en active Pending
- 2023-06-30 CN CN202310795551.0A patent/CN116868878A/en active Pending
- 2023-06-30 CN CN202310795550.6A patent/CN116616164A/en active Pending
- 2023-07-04 CN CN202310812494.2A patent/CN116569712A/en active Pending
- 2023-07-18 CN CN202310883688.1A patent/CN117011706A/en active Pending
- 2023-07-21 CN CN202310903074.5A patent/CN116671431A/en active Pending
Also Published As
| Publication number | Publication date |
|---|---|
| CN116868878A (en) | 2023-10-13 |
| CN116746390A (en) | 2023-09-15 |
| CN116671431A (en) | 2023-09-01 |
| CN116548203A (en) | 2023-08-08 |
| CN116584267A (en) | 2023-08-15 |
| CN116569712A (en) | 2023-08-11 |
| CN117011706A (en) | 2023-11-07 |
| CN116820002A (en) | 2023-09-29 |
| CN116569826A (en) | 2023-08-11 |
| CN116897723A (en) | 2023-10-20 |
| CN116779029A (en) | 2023-09-19 |
| CN116616164A (en) | 2023-08-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Nangare et al. | Growth, fruit yield and quality of tomato (Lycopersicon esculentum Mill.) as affected by deficit irrigation regulated on phenological basis | |
| James et al. | Integrated pest management in vegetable production: A guide for extension workers in West Africa | |
| US20230039763A1 (en) | System and method for real-time crop management | |
| Liu et al. | Corn (Zea mays L.) plant characteristics and grain yield response to N fertilization programs in no-tillage system | |
| Hairuddin et al. | Overview of image processing approach for nutrient deficiencies detection in Elaeis Guineensis | |
| Talebpour et al. | The role of precision agriculture in the promotion of food security | |
| CN116818768A (en) | System and method for confirming influencing factors of diseased plants | |
| Azevedo et al. | No-tillage and high-density planting for Tahiti acid lime grafted onto Flying Dragon trifoliate orange | |
| Putri et al. | Variability of rice yield with respect to crop health | |
| Haider et al. | Exploring morphological traits variation in Gomphrena globosa: A multivariate analysis | |
| de Paula Corrêdo et al. | Nitrogen variable rate in pastures using optical sensors | |
| Kaur et al. | Study of optimum time span for distinguishing Rumex spinosus in wheat crop through spectral reflectance characteristics | |
| Alatise et al. | Evaluation of drought from rainfall data for lokoja. A confluence of two major rivers | |
| Zavinon et al. | Pigeon pea [Cajanus cajan (L.) Millsp] cultivation, its major constraints and ethnobotanical status in Southern Benin | |
| Cugnetto et al. | Vineyard clusters monitored by means of Litterbag-NIRS and Foliar-NIRS spectroscopic methods | |
| Mikhailova et al. | Evaluation of the influence of biologically active substances on the physiological processes of soybean plants with the use of multispectral camera and unmanned aerial vehicle | |
| Madar et al. | Agronomic evaluation of different lettuce (Lactuca sativa L.) varieties under unheated plastic tunnel | |
| Ghazali et al. | Spectral analysis for phenological studies of leaves and fruits development in coffee plantation in Gunung Puntang: a preliminary result | |
| Karadavut et al. | Comparison of some mathmatical growth models for estimating of physiological growth characters of alfalfa (Medicago sativa L.) | |
| Jansen et al. | Image processing for bioassays | |
| Heyns | Estimation methods for date palm yield: a feasibility study | |
| Dvořák et al. | Preliminary technical time standards for harvesters working in premature and mature stands | |
| Azmi et al. | Early detection of plant disease infection using hyperspectral data and machine learning | |
| Bandara et al. | Image Sensing, A Unique Tool for Early Detection of Nitrogen Deficiency of Tomato Grown in Hot and Humid Greenhouses | |
| Bădulescu et al. | New valuable genotypes of tomato added in culture to INCDBH Stefanesti-Arges |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |