CN116758330A - Plant disease and insect pest image recognition system - Google Patents
Plant disease and insect pest image recognition system Download PDFInfo
- Publication number
- CN116758330A CN116758330A CN202310490382.XA CN202310490382A CN116758330A CN 116758330 A CN116758330 A CN 116758330A CN 202310490382 A CN202310490382 A CN 202310490382A CN 116758330 A CN116758330 A CN 116758330A
- Authority
- CN
- China
- Prior art keywords
- plant
- pest
- disease
- image recognition
- image
- 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
- 241000607479 Yersinia pestis Species 0.000 title claims abstract description 132
- 201000010099 disease Diseases 0.000 title claims abstract description 100
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract description 100
- 241000238631 Hexapoda Species 0.000 title claims abstract description 48
- 230000012010 growth Effects 0.000 claims abstract description 17
- 238000012545 processing Methods 0.000 claims description 13
- 238000005516 engineering process Methods 0.000 claims description 8
- 235000013399 edible fruits Nutrition 0.000 claims description 4
- 230000008635 plant growth Effects 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 3
- 230000017260 vegetative to reproductive phase transition of meristem Effects 0.000 claims description 3
- 239000003086 colorant Substances 0.000 claims description 2
- 230000005059 dormancy Effects 0.000 claims description 2
- 238000011161 development Methods 0.000 abstract description 3
- 230000009467 reduction Effects 0.000 abstract description 2
- 241000196324 Embryophyta Species 0.000 description 90
- 238000003745 diagnosis Methods 0.000 description 18
- 230000006378 damage Effects 0.000 description 9
- 238000012549 training Methods 0.000 description 8
- 238000000034 method Methods 0.000 description 7
- 235000013311 vegetables Nutrition 0.000 description 6
- 230000008569 process Effects 0.000 description 5
- 240000007651 Rubus glaucus Species 0.000 description 4
- 235000011034 Rubus glaucus Nutrition 0.000 description 4
- 235000009122 Rubus idaeus Nutrition 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000002349 favourable effect Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 210000003462 vein Anatomy 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000035784 germination Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006806 disease prevention Effects 0.000 description 1
- 238000012377 drug delivery Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 230000003902 lesion Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 244000052769 pathogen Species 0.000 description 1
- 230000001717 pathogenic effect Effects 0.000 description 1
- 238000011056 performance test Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000026676 system process Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Computing Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Image Processing (AREA)
Abstract
The invention relates to the technical field of image recognition, and discloses a plant disease and pest image recognition system. According to the invention, the moving frame is started to drive the image acquisition module to shoot images of plants at multiple angles and directions, so that the round trip distance of professionals can be reduced, the manpower reduction time can be reduced, the observation and judgment of the plants can be remotely completed, the professionals of plant planting and growth can be greatly improved, the advance avoidance of plant diseases and insect pests can be facilitated, and the professional development of agriculture can be promoted.
Description
Technical Field
The invention relates to the technical field of image recognition, in particular to a plant disease and insect pest image recognition system.
Background
The prior patent (bulletin number: CN 112699805A) and intelligent identification system for controlling vegetable diseases and insect pests, the cloud server of the patent obtains an identification result by utilizing a pre-trained intelligent identification model for controlling vegetable diseases and insect pests, and then combines the judgment of personnel of an expert platform, so that the identification result of the corresponding type of the vegetable diseases and insect pests can be obtained, and the corresponding treatment measure is stored to generate a diagnosis result for the disease and insect pests and fed back to a user terminal, so that the farmer can accurately identify the type of the existing disease and insect pests and treat the disease and insect pests by adopting the proper treatment measure, the identification rate is higher and more stable, the vegetable diseases and insect pests are extremely important to control, however, for the vegetable or plant planted in agriculture, the planting area is larger, the planting quantity is larger, the inspection is carried out manually, a great deal of manpower is needed to be consumed at first, the predicted judgment of the disease and insect pests is not needed, the image acquisition of the vegetable is difficult, meanwhile, only the analysis of the state of the plant is lacking, only part of the insect pests can be directly observed through images, the state and the color of the plant is often needed to be analyzed, and the judgment of the plant is lacking.
Therefore, we propose a plant disease and pest image recognition system, drive image acquisition module through the start motion line frame and carry out the image shooting of multi-angle and position to the plant, can reduce professional personnel and come and go the journey, also can reduce manpower and reduce time simultaneously, can accomplish the observation and the judgement to the plant in long-range, be favorable to improving the specialty that plant planted and grown greatly, be favorable to avoiding in advance plant disease and pest, be favorable to promoting the professional development of agriculture.
Disclosure of Invention
The invention aims to provide a plant disease and pest image recognition system which solves the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: a plant disease and pest image recognition system is characterized in that a moving line frame is built, an image acquisition module is enabled to carry out moving image shooting through the moving line frame, image data are uploaded to a disease and pest image recognition module, a disease and pest plant image library is built, an AI technology is utilized in the disease and pest image recognition module to learn the disease and pest plant image library, the image acquired by the image acquisition module is recognized and judged through the disease and pest image recognition module, a plant expert platform is built, and the image which cannot be recognized by the disease and pest image recognition module is uploaded to the plant expert platform.
As a preferred implementation mode of the invention, the moving row frame is built according to the scale of the plant garden, so that the image acquisition module moves in the moving row frame, and the plant in the plant garden is photographed in multiple angles and multiple directions through the moving row frame by the image acquisition module.
As a preferred embodiment of the invention, the growth of plants is divided into different growth stages, such as a dormant stage, a growing stage, a flowering stage, a fruiting stage and the like, and specific pest environmental characteristics are set in each growth stage, and the plants are classified according to the types of pests, so that the algorithm extracts the shape characteristics of the pests, plays a role in data support on the pest identification algorithm, stores the pest identification algorithm into a pest plant image library, and stores regional pest images into the pest plant image library.
As a preferred implementation mode of the invention, the plant disease and insect pest image recognition module recognizes the uploaded images by an algorithm through an AI technology, performs color comparison on the images at the same positions in different periods in a mode of analogy of the images at the same angles and the same directions, recognizes the images with obvious color difference through the plant disease and insect pest image recognition module, and analyzes the growth conditions of roots, stems, flowers and fruits in the growth period (germination period, seedling period, plant formation period, bud period and maturity period) of the plants.
As a preferred implementation mode of the invention, a processing module is built, after pest identification is completed through the pest image identification module, a complete processing scheme is realized through the processing module according to a manual for eliminating the pests, and personnel are reminded of implementing in time.
As a preferable implementation mode of the invention, the image with large color difference in the plant disease and insect pest image identification module but no specific plant disease and insect pest identification can be identified, and the image is uploaded to a plant expert platform.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, the moving frame and the image acquisition module are used for driving the image acquisition module to shoot images of plants at multiple angles and directions by starting the moving frame, so that the round trip distance of professionals can be reduced, the manpower reduction time can be reduced, and the observation and judgment of the plants can be remotely completed.
2. According to the invention, through the arrangement of the plant disease and pest image recognition module, the plant expert platform and the plant disease and pest image library, the pictures of plant disease and pest are uploaded to the plant disease and pest image library, algorithm learning is carried out through the AI technology in the plant disease and pest image recognition module, the acquired plant images are compared with the plant disease and pest image library in an analog mode, the acquired plant images are judged through AI, the result is uploaded, the result is checked by a technician, and the images are relatively unclear or the images with relatively large color differences in different periods are uploaded to the plant expert platform, so that the plant expert platform is used for judging, the plant planting and growing professionals are greatly improved, the plant disease and pest are avoided in advance, and the professional development of agriculture is promoted.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a schematic diagram of the overall structure of a plant disease and pest image recognition system according to the present invention;
Detailed Description
The invention is further described in connection with the following detailed description, in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the invention easy to understand.
Referring to fig. 1, the present invention provides a technical solution: a plant disease and pest image recognition system is characterized in that a moving line frame is built, an image acquisition module is enabled to carry out moving image shooting through the moving line frame, image data are uploaded to a disease and pest image recognition module, a disease and pest plant image library is learned through an AI technology in the disease and pest image recognition module, images acquired by the image acquisition module are recognized and judged through the disease and pest image recognition module, the acquired images on the same plant on similar dates are subjected to color and state analogy through the disease and pest image recognition module, a plant expert platform is built, and the disease and pest image recognition module cannot recognize and judge images and upload the images to the plant expert platform.
In the specific embodiment
In the process of large-scale plant cultivation of agriculture, relying on a large amount of professionals to patrol and examine is the waste of manpower resources, simultaneously because the planting area is great, personnel patrol time period is longer, and the problem of omission appears easily to look over for a long time simultaneously, and non-professional is not timely enough simultaneously, and the judgement to the plant state is not clear enough, lacks the contrast effect simultaneously, and the growth state change in the same plant week is unclear, and then leads to the disease prevention effect poor.
According to the scale of the plant garden, the moving line frame is built, the image acquisition module moves in the moving line frame, and the plant in the plant garden is photographed in multiple angles and multiple directions through the moving line frame by the image acquisition module.
The plant growth is divided into different growth stages, such as dormancy stage, growth stage, flowering stage, fruiting stage and the like, and specific disease and pest environment characteristics are set in each growth stage, and the plant is classified according to disease and pest types, so that the algorithm extracts shape characteristics of pests, plays a role in data support on the pest identification algorithm, stores the pest identification algorithm into a pest plant image library, and stores regional disease and pest images into the pest plant image library.
The plant disease and insect pest image recognition module recognizes the uploaded images by using an algorithm through an AI technology, compares the colors of the images at the same positions in different periods in a mode of analogy with the images at the same angles and the same directions, recognizes the images with obvious color differences through the plant disease and insect pest image recognition module, and analyzes the growth conditions of roots, stems, flowers and fruits of plants in the growth period.
And (3) constructing a processing module, after finishing pest identification through the pest image identification module, completely processing a scheme through the processing module according to a manual for eliminating the pests, and reminding personnel to implement in time.
And uploading the images which have larger color difference and can not identify specific diseases and insect pests in the disease and insect pest image identification module to a plant expert platform.
Because damage and control of plant diseases and insect pests to plant growth belong to different categories, the plant image library of the plant diseases and insect pests is divided into two parts, namely diseases and insect pests.
Wherein the disease database comprises basic information (disease name, hazard part, hazard shape, genus, distribution and hazard) and detailed information (pathogen, occurrence rule, control method and disease atlas) of diseases; the pest database includes pest basic information (pest names, latin, genus, pest plants, pest sites, pest status, distribution and damage) and detailed information (morphological characteristics, occurrence rules, pest control methods and pest atlases).
By establishing a disease and pest database, a rapid diagnosis function of disease and pest based on the database is provided, and diseases or pests which generate the harm can be rapidly positioned by inputting the names, the harm positions and the harm states of plants, and corresponding prevention measures are provided, so that timely symptomatic drug delivery is realized.
The plant disease and insect pest image recognition module needs to be trained by an algorithm to improve accuracy
In the acquired images, performing disease and pest type labeling and data preprocessing on the acquired images of the leaves to obtain real labels of the leaves, dividing the images of the leaves into a training set, a verification set and a test set, cutting the images by the training set, and normalizing the sizes to obtain a new training set; sending the manufactured data set into an AI database, outputting an actual value, calculating a loss function value according to the actual label and the actual value, updating parameters of the network model by using a gradient descent back propagation algorithm, verifying a result by using a verification set by using the network model updated each time, and obtaining a trained network model through a plurality of times of training;
then the test set is sent into a trained network model, and the network model detection accuracy is calculated through comparison with a real label; the plant disease and insect pest identification algorithm is designed by using a deep convolutional neural network algorithm, and the design of a stacked network module is utilized, so that the plant disease and insect pest identification algorithm has the characteristics of high precision and few parameters, the number of characteristic output layers of convolutional layers in the stacked module is small, in addition, the sizes of the convolutional kernels are 1 multiplied by 1 and 3 multiplied by 3, and the network model is connected to enable the transmission of characteristics and gradients to be more effective and easy to train.
The network modularization structure is realized by embedding the stacked network modules to increase the width and depth of the network, the stacked modules with different depths are fused to obtain the characteristics of different sizes and different spliced scales, the detection cost can be greatly reduced, the detection and monitoring time of the pest and disease damage can be shortened aiming at the current situation of the damage of a large number of current crops, the pest and disease damage control efficiency is improved, the pest and disease damage control efficiency is rapidly restrained under the condition of low explosion range, and the yield and income of the crops are effectively promoted.
The image acquisition module of plant diseases and insect pests needs to use an image sensor of a professional camera device, the collected image information of the plant diseases and insect pests is transmitted to an embedded microprocessor through a CSI camera, and an algorithm is executed by a raspberry group microcomputer in image processing.
In the processing process, a data memory in the raspberry group microcomputer stores various data in the crop disease and pest image recognition processing process, the raspberry group microcomputer transfers a crop disease and pest image recognition basic program in the SD card to a program memory in the raspberry group microcomputer for image recognition, a touch screen displays various processing results in the crop disease and pest image recognition process, and the whole system is organically combined, so that the system process of crop disease and pest image recognition is completed.
In the training process of the deep learning model, using 70% of pictures as training sets and 20% of pictures as test sets for each plant disease and insect pest data to be identified, and using the rest as verification sets, firstly reading image data of the training sets, and carrying out image enhancement, including picture overturning, rotation, color change, contrast enhancement, noise disturbance and the like; training according to the set model parameters, and performing model performance test after the neural network model converges and stabilizes; after the corresponding parameters are adjusted for a plurality of times, intelligent identification of the front-end submitted image is realized.
According to different growth stages of plants, the disease and pest diagnosis modes are different
Diagnosis of germination period
And according to the name, cotyledon condition, leaf color condition, leaf margin condition, seedling condition and radicle condition of crops, intelligently matching an optimal diagnosis result from a plant image library of plant diseases and insect pests, and calculating the credibility of the diagnosis result.
Diagnosis of seedling stage
According to the name, stem condition, petiole condition, leaf color condition, leaf margin condition and leaf vein condition of crops, the optimal diagnosis result is intelligently matched from the plant image library of the plant diseases and insect pests, and the credibility of the diagnosis result is calculated.
Adult stage diagnosis
According to the name, stem condition, petiole condition, leaf color condition, leaf margin condition and leaf vein condition of crops, the optimal diagnosis result is intelligently matched from the plant image library of the plant diseases and insect pests, and the credibility of the diagnosis result is calculated.
Bud phase diagnosis
According to the name, stem condition, petiole condition, leaf color condition, leaf margin condition, vein condition and flower condition of crops, the optimal diagnosis result is intelligently matched from the plant image library of the plant diseases and insect pests, and the credibility of the diagnosis result is calculated.
Fertilizing nutrition diagnosis
And intelligently matching an optimal diagnosis result from the plant image library of the plant diseases and insect pests according to the name of the crop, the plant condition, the leaf condition, the stem condition, the flower condition, the fruit condition and the lesion position, and calculating the credibility of the diagnosis result.
And the plant expert platform can invite an agricultural expert to the platform, uploads images of plant diseases and insect pests which cannot be truly detected by agricultural personnel, judges by means of the plant expert and gives a processing method.
While the fundamental and principal features of the invention and advantages of the invention have been shown and described, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (6)
1. A plant disease and insect pest image recognition system is characterized in that: setting up a moving line frame, enabling an image acquisition module to carry out moving image shooting through the moving line frame, uploading image data to a plant disease and insect pest image recognition module, through building a plant disease and insect pest plant image library, learning the plant disease and insect pest plant image library through an AI technology in the plant disease and insect pest image recognition module, carrying out recognition and judgment on images acquired by the image acquisition module through the plant disease and insect pest image recognition module, carrying out color and state analogy on the acquired images of the same plant on similar date through the plant disease and insect pest image recognition module, building a plant expert platform, and uploading images which cannot be recognized and judged by the plant disease and insect pest image recognition module to the plant expert platform.
2. A plant disease and pest image recognition system according to claim 1, wherein: according to the scale of the plant garden, the moving line frame is built, the image acquisition module moves in the moving line frame, and the plant in the plant garden is photographed in multiple angles and multiple directions through the moving line frame by the image acquisition module.
3. A plant disease and pest image recognition system according to claim 1, wherein: the plant growth is divided into different growth stages, such as dormancy stage, growth stage, flowering stage, fruiting stage and the like, and specific disease and pest environment characteristics are set in each growth stage, and the plant is classified according to disease and pest types, so that the algorithm extracts shape characteristics of pests, plays a role in data support on the pest identification algorithm, stores the pest identification algorithm into a pest plant image library, and stores regional disease and pest images into the pest plant image library.
4. A plant disease and pest image recognition system according to claim 1, wherein: the plant disease and insect pest image recognition module recognizes the uploaded images by using an algorithm through an AI technology, compares the colors of the images at the same positions in different periods in a mode of analogy with the images at the same angles and the same directions, recognizes the images with obvious color differences through the plant disease and insect pest image recognition module, and analyzes the growth conditions of roots, stems, flowers and fruits of plants in the growth period.
5. A plant disease and pest image recognition system according to claim 4, wherein: and (3) constructing a processing module, after finishing pest identification through the pest image identification module, completely processing a scheme through the processing module according to a manual for eliminating the pests, and reminding personnel to implement in time.
6. A plant disease and pest image recognition system according to claim 1, wherein: and uploading the images which have larger color difference and can not identify specific diseases and insect pests in the disease and insect pest image identification module to a plant expert platform.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310490382.XA CN116758330A (en) | 2023-05-04 | 2023-05-04 | Plant disease and insect pest image recognition system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310490382.XA CN116758330A (en) | 2023-05-04 | 2023-05-04 | Plant disease and insect pest image recognition system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116758330A true CN116758330A (en) | 2023-09-15 |
Family
ID=87952204
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310490382.XA Pending CN116758330A (en) | 2023-05-04 | 2023-05-04 | Plant disease and insect pest image recognition system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116758330A (en) |
-
2023
- 2023-05-04 CN CN202310490382.XA patent/CN116758330A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Shafi et al. | A multi-modal approach for crop health mapping using low altitude remote sensing, internet of things (IoT) and machine learning | |
| JP6638121B1 (en) | Server device of crop growth stage determination system, growth stage determination method and program | |
| WO2019179270A1 (en) | Plant planting data measuring method, working route planning method, device and system | |
| EP3369037A1 (en) | Method and information system for detecting at least one plant planted on a field | |
| CN111476149A (en) | Plant cultivation control method and system | |
| CN117036088A (en) | Data acquisition and analysis method for identifying growth situation of greening plants by AI | |
| CN113469112B (en) | Crop growth condition image identification method and system | |
| CN112116206A (en) | Intelligent agricultural system based on big data | |
| CN114743100A (en) | Agricultural product growth monitoring method and system | |
| CN111062699A (en) | Method and system for constructing intelligent service model for crop full growth period | |
| CN116300608A (en) | Intelligent agriculture remote monitoring system based on big data | |
| CN115294518A (en) | Intelligent monitoring method and system for accurate cultivation of horticultural plants in greenhouse | |
| WO2021198731A1 (en) | An artificial-intelligence-based method of agricultural and horticultural plants' physical characteristics and health diagnosing and development assessment. | |
| Araneta et al. | Controlled Environment for Spinach Cultured Plant with Health Analysis using Machine Learning | |
| CN116758330A (en) | Plant disease and insect pest image recognition system | |
| Widiyanto et al. | Monitoring the growth of tomatoes in real time with deep learning-based image segmentation | |
| CN115797764A (en) | Remote sensing big data interpretation method and system applied to farmland non-agronomy monitoring | |
| CN115690609A (en) | Method for monitoring growth vigor of crops in growth period | |
| Wang et al. | Research on application of smart agriculture in cotton production management | |
| DE202022102591U1 (en) | System for monitoring plant health in precision agriculture using image processing and convolutional neural network | |
| Feng et al. | A real-time monitoring and control system for crop | |
| CN115004994B (en) | Fruit tree plant diseases and insect pests prevention and control system based on digital map | |
| Suebsombut et al. | Rule based recommendation system to support crop lifecycle management | |
| US20230359889A1 (en) | Machine learning methods and systems for characterizing corn growth efficiency | |
| Xin et al. | Melon Growth Detection Strategy Using Artificial Intellegence in Greenhouse Cultivation |
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 |