CN102506938B - Detecting method for greenhouse crop growth information and environment information based on multi-sensor information - Google Patents
Detecting method for greenhouse crop growth information and environment information based on multi-sensor information Download PDFInfo
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Abstract
The invention belongs to the technical field of greenhouse crop growth information and environment information detection, and particularly discloses a detecting method for greenhouse crop growth information and environment information based on multi-sensor information. The detecting method includes the following steps: utilizing a spectrometer, a multispectral imager and a thermal imager to obtain the spectrums, the multispectral image and the canopy temperature information of the greenhouse crop; utilizing temperature, humidity, irradiance, CO2 density, EC and pH value sensors to obtain the temperature-light-moisture fertilizer environment information of the greenhouse; optimizing the spectrum, the image and the canopy temperature characteristics of the nutrition and moisture of the crop, so as to obtain the characteristic space of NPK nutrition and moisture; extracting the morphological characters of the spectrum and the image of the crop, so as to obtain the leaf area index, the stem diameter, the plant body and the fruit growth rate of the crop; and continuously monitoring and recording and formatting the obtained greenhouse environment information of the nutrition, the water, the growth vigor and the temperature-light-moisture fertilizer of the crop, so as to serve as the comprehensive detecting information of the growth and the environment of the greenhouse crop. The information obtained by means of the method can be used for the liquid manure management and environmental control and regulation according to the actual requirement of the greenhouse crop growth.
Description
Technical field
The invention belongs to chamber crop growth information and environmental information detection technique field, relate to a kind of growth of the chamber crop based on many heat transfer agents and environmental information detection method, refer in particular to multiple lossless detection technology such as utilizing spectrum, visual pattern, infrared temperature detection, in conjunction with temperature, humidity, illumination, the CO of greenhouse
2concentration and nutrient solution
eCthe detection of (conductivity), pH value, nitrogen, phosphorus, potassium, moisture and the leaf area index, the stem that obtain protected crop are thick, plant and fruit growth speed, and plant growth and the environment comprehensive information such as warm light aqueous vapor is fertile.The integrated information of utilizing the method to obtain, can realize the water and fertilizer management and the environmental control of greenhouse that carry out science according to the actual demand of plant growth.
Background technology
The growth informations such as the Non-Destructive Testing of chamber crop growth information mainly comprises that the nutrient detections such as crop n p k nutrition, moisture and leaf area index, stem are thick, plant height, fruit color quality, plant and fruit growth speed detect two aspects.Environmental information mainly refers to temperature, humidity, illumination, the CO in greenhouse
2concentration and nutrient solution
eCwith pH value information.
It is that crop is subject to the extrinsicfactor effects such as environment, nutrition, moisture that greenhouse growth course is studied carefully its essence, and the complicated dynamic process that it is transformed.In greenhouse, between large, the multiparameter of strong, the Spatial-Temporal Variability of the space distribution of crop growth environment parameter, influence each other, add the individual difference between variety classes crop and different plants, cause traditional cultivation and environment conditioning mode to be difficult to adapt to the growth needs of variety classes, different plants and different growing thereof.Therefore, on the basis that the external action such as environment and nutrition, the moisture factor of chamber crop growth is carried out accurately detecting, interactively between the extrinsicfactors such as research environment, nutrition, moisture and crop growing state, production run, set up based on chamber crop growth and environmental information overall evaluation system, and according to evaluation result, formulate optimum control strategy, this is to improving research level of China greenhouse technology, and realizing the high yield, high-quality of industrialized agriculture, efficient, low-carbon (LC) and sustainable development has very important theory significance and practical value.
The at present growth information Non-Destructive Testing of crop is mainly take spectral technique and image technique as main.At present, crop alimentary, more existing correlative studys in water content detection aspect.Aspect spectral detection, application number is 200510088935.0 application for a patent for invention, lossless detection method and the surveying instrument of a kind of portable plant nitrogen and moisture are disclosed, carry out the nutrient diagnosis of plant by detecting plant leaf at the spectral reflectance strength information of four characteristic wave strong points, utilize nitrogen and the water percentage information of the inverting of four wavelength vegetation indexs being obtained to plant.Application number is 200410048127.7 application for a patent for invention, a kind of method for predicting nitrogen content of cucumber leaf based on natural lighting reflectance spectrum is disclosed, can draw by cucumber leaves the reflection vegetation index of blade in the spectral reflectance intensity at specified wavelength place, and then judge its nitrogen level.In visual pattern context of detection, the patent of invention that application number is 200710069116.0, discloses a kind of method of quickly non-destructive measurement for nitrogen content of tea using multiple spectrum imaging technology.Application number is the application for a patent for invention of 200510062298.X and the utility application that application number is 200520134360.7, has announced a kind of multiple spactrum image diagnosis mothod of rape nitrogen nutrition and diagnostic system.Said system all adopts the multispectral camera system of 3CCD as vision collecting device, under computer control, gathers influences of plant crown multispectral image information by the multispectral camera system of 3CCD, can the nondestructive nitrogen nutritional status of diagnosing plant.In the growing way context of detection of crop, application number is the application for a patent for invention of 200610097576.X, a kind of embedded agricultural plant growth state monitor and method of work thereof are disclosed, can be thick to the ambient temperature and humidity of plant growth, stem, plant height, consistency of soil and potential of hydrogen survey, this system only, plant height thick by stem judges crop growthing state, and lack dynamic plant growth evaluation model, be therefore difficult to crop growthing state to be made the evaluation of overall scientific.It is a kind of for monitoring of crop growth and nutrient fertilization prescription generating apparatus and method that application number is that 200410014648.0 application for a patent for invention discloses, this invention adopts video camera to obtain the stem of crop, leaf, flower, really, skin image information, utilize nutritional labeling detector to obtain crops and soil nutrient information, because video camera only can obtain visible ray composograph, the n p k nutrition and the water characteristic that are difficult to crop carry out Accurate Analysis, although nutritional labeling detector can obtain the nutritional information of crop, but its sampling and detection mode can cause damage to crop.
In sum, the Non-Destructive Testing of the growth information of crop is at present mainly based on spectrum and image technique.The relation that spectral technique can more convenient acquisition nitrogen content, water percentage and spectral reflectivity or its are drilled raw amount; The color (gray scale) of visible ray or near-infrared vision image, texture, morphological feature also can characterize the information such as crop alimentary level, leaf area, stem fruit leaf, also significant correlation of hat-air Temperature Difference of crop and water stress to a certain extent.But only depend on the single detection method of spectrum, image and canopy surface temperature, obtain the isolated information such as nutrition or moisture or leaf area index, stem are thick, plant height, fruit color form, be difficult to crop growthing state make comprehensively, the judgement of system, science.And between nutrition, there is reciprocation between nutrition and moisture, testing process is subject to the impact of the envirment factors such as crop canopies structure, Soil Background spectrum and atmospheric window, humiture larger, therefore, only use spectral technique, or the single detective technical deficiency such as the canopy-air temperature difference of photopic vision image or near-infrared vision image or plant is to reflect accurately, all sidedly the growth information such as crop alimentary, moisture and growing way.And obtain rapidly and accurately growth and the environmental information of crop, be the prerequisite of the growth conditions of crop being carried out to scientific evaluation.
In sum, China's greenhouse production information detection at present there is no growth and the environmental information of method to protected crop and carries out comprehensively, detects accurately and resolve, cannot perception regulate and control really requirement with reflection plant growth, cause crop yield potentiality fully not excavate, the problem that operation energy consumption is bigger than normal.In view of above reason, need at present that a kind of comprehensive nutrition, moisture and leaf area index, stem that obtains chamber crop is thick, fruit and plant growth rate, and the method for the plant growth such as warm light aqueous vapor is fertile and environment comprehensive information, produce to instruct modern greenhouse, improve the yield and quality, reduce waste and pollution that excessive fertilization and traditional control methods cause, increase economic efficiency.
Summary of the invention
The object of this invention is to provide a kind of spectrum, visual pattern, multiple Dynamic Non-Destruction Measurement of infrared imaging of merging, in conjunction with chamber environment temperature, humidity, illumination, CO
2concentration and nutrient solution
eC, the plant growth such as pH value and environment comprehensive information accurate detection, and then growth and environmental information to crop carry out scientific evaluation, the information getting method that instructs greenhouse to regulate and control as required, for modern greenhouse environment conditioning and water and fertilizer management provide scientific basis.
For achieving the above object, the present invention is based on chamber crop growth and the environmental information detection method of many heat transfer agents, carry out according to following step:
(1) utilize spectrometer, multi-spectral imager and thermal imaging system directly to obtain visible ray-near-infrared reflection spectral information, multispectral image information and the canopy surface temperature information of chamber crop;
(2) utilize temperature sensor, humidity sensor, irradiance sensor, CO
2concentration sensor,
eCobtain temperature, humidity, illumination, the CO of greenhouse with pH value sensor
2concentration, nutrient solution conductivity (
eC) and pH value information;
(3) visible ray-near-infrared reflection spectrum and the multispectral image of the crop gathering are carried out to analyzing and processing, extract visible ray-near-infrared reflection spectral signature wavelength of crop nitrogen phosphorus potassium and color, texture, gray average and the fusion feature of multispectral image, and then visible ray-near-infrared reflection spectrum of the nitrogen obtaining, phosphorus, potassium and multispectral image feature are optimized, build reflectance spectrum and the image combining feature space of crop n p k nutrition;
(4) visible ray-near-infrared reflection spectrum of crop and the canopy surface temperature information of crop that gather are analyzed and processed, extract characteristic wavelength and the canopy surface temperature of visible ray-near-infrared reflection spectrum of crop water, combining environmental temperature, humidity information, obtain hat-air Temperature Difference and saturation vapour pressure, set up hat-air Temperature Difference and water stress index model; Build reflectance spectrum and the canopy surface temperature assemblage characteristic space of crop water by characteristic optimization;
(5) visible ray-near-infrared reflection spectral light spectrum information and the multispectral image information of crop gathering is analyzed and processed, extract the leaf area index of crop and stem is thick, plant height, fruit morphology feature; And according to Continuous Observation data, try to achieve plant growth rate and fruit growth speed;
(6) utilize that n p k nutrition, water and nutrient information and the leaf area index of the crop that obtains, stem are thick, plant height, plant growth rate, fruit growth speed growth information, and the temperature of greenhouse, humidity, illumination, CO
2concentration, nutrient solution
eCwith pH value information, computing machine carries out continuous monitoring record and format, as the growth of crop and the detection data of environmental information.
The analysis and processing method of the visible ray-near-infrared reflection spectrum adopting in wherein said step (3), (4), (5), carries out according to following step: first carry out filtering, carry out afterwards successive Regression and principal component analysis (PCA) and extract feature.
The analysis and processing method of the multispectral image adopting in wherein said step (3), (4), (5), carry out according to following step: first strengthen multispectral image and carry out pixel-level image fusion, cut apart background by super green feature and two-dimensional histogram afterwards, finally carry out color (gray scale) mean value computation, texture analysis and fusion feature analysis.
Effect of the present invention is (1) the present invention by the organically blending of the multiple lossless detection technology such as spectrum, image, infrared temperature, in conjunction with temperature, humidity, illumination, CO
2concentration, nutrient solution
eCdetect with pH value isothermal chamber environmental information, the comprehensive integrated information of obtaining plant growth and environment, not only obtained information quantity is larger, abundanter, and can more comprehensively, accurately hold the growth conditions of crop, and this does not all relate in file in the past; (2) the present invention, by visible ray-near-infrared reflection spectrum of crop and the fusion of multispectral image information, comprehensively judges the n p k nutrition level of crop; Judge the water stress state of crop by the information fusion of near infrared spectrum and canopy infrared temperature, by the visual pattern of chamber crop, extract its morphological feature and then judge the growing way of crop, this does not all relate in file in the past.
Accompanying drawing explanation
Fig. 1 is the required hardware composition of the method for the invention and information flow schematic diagram;
Fig. 2 is chamber crop growth and the environmental information detection method process flow diagram that the present invention is based on many heat transfer agents.
Embodiment
The present invention is based on the chamber crop growth of many heat transfer agents and the required hardware of environmental information detection method forms and information flow as shown in Figure 1.Its required hardware composition comprises spectrometer, multi-spectral imager, thermal imaging system, Temperature Humidity Sensor, irradiance sensor, CO
2concentration sensor,
eCwith pH value sensor, data collecting card and computing machine.Wherein spectrometer, multi-spectral imager, thermal imaging system are used for gathering the growth information such as crop alimentary, moisture, growing way; Irradiance sensor, Temperature Humidity Sensor, CO
2concentration sensor,
eCbe used for gathering greenhouse environment information with pH value sensor.The information that spectrometer, multi-spectral imager, thermal imaging system obtain reads and is transferred to computing machine; Temperature Humidity Sensor, irradiance sensor, CO
2concentration sensor,
eCafter carrying out A/D conversion, data collecting card uploads computing machine with the output signal of pH value sensor.
Below in conjunction with accompanying drawing 2, the embodiment to the method for the present invention is described.The chamber crop growth and the environmental information detection method that the present invention is based on many heat transfer agents comprise the following steps:
(1) first utilize spectrometer, multi-spectral imager and thermal imaging system directly to obtain visible ray-near-infrared reflection spectral information, multispectral image information and the canopy surface temperature information of chamber crop;
Under greenhouse, select cloudless fine day, implement this method, information acquisition selection of time is at 9:00～15:00; The spectrometer of selecting is the FieldSpec 3 type portable light spectrometers of ASD company of the U.S., its spectral measurement ranges 350-2500nm; Select the probe of 25 ° of visual fields, adopt irreflexive mode to sample, probe distance sample surface 2～3cm, spectral measurement is using 10 scanning mean values as 1 sampled point spectrum, each sample is chosen 3 sampled points, then spectral reflectance values using its mean value as crop.Multi-spectral imager selects the U.S. to produce the multispectral progression scanning digital of MS-3100 type formula camera, MS-3100 imaging spectral scope is 350-1100nm, overlook under visual field and R, G, B, NIR and RGB, CIR pattern, gather centre wavelength apart from crop sample canopy 70cm place and be respectively the near-infrared image that R, G, B image and the centre wavelength of 660nm, 560nm, 460nm are 810nm, and RGB, CIR composograph; Under the same pattern in side-looking visual field, apart from plant 50cm place, gather centre wavelength and be respectively visible ray-near infrared multispectral image of 660nm, 560nm, 460nm, 810nm, and RGB, CIR composograph.The TI50 infrared thermography of FLUKE company of the U.S. is selected in the measurement of crop canopy temperature, measurement range is-20～305 ℃, precision is 0.07 ℃, in order to eliminate solar azimuth and the impact of crop-planting direction on observed reading, instrument and ground are at 45 °, carry out sample measurement from 6 different directions, get the mean value of 6 measured values as the canopy surface temperature value of this sample at every turn.The specialty analysis software that the data that spectrometer, multi-spectral imager and thermal imaging system obtain are carried by it carries out data analysis and process.Wherein spectral analysis software adopts the ViewSpec Pro 4.05 carrying to carry out spectrum pre-service and derivation, adopts Chemical Measurement spectral analysis software NIRSA to carry out spectroscopic data processing; Multispectral image data acquisition carries out data acquisition with the Duncan software that carries, utilize ENVI and IDL software to multispectral image process, analysis and feature extraction; Thermal imaging system adopts it to carry software SmartView 1.0 and analyzes and process.
(2) utilize Temperature Humidity Sensor, irradiance sensor, CO
2concentration sensor,
eCobtain temperature, humidity, illumination, the CO of greenhouse with pH value sensor
2concentration, nutrient solution
eCwith pH value information; And the information exchange of the sensor collection is crossed to data collecting card and carry out uploading Computer Analysis after digitizing conversion;
Austrian EE08 type ambient temperature and humidity integrative sensor is selected in the collection of ambient temperature and humidity degree, temperature measurement range-40～80 ° C, and moisture measurement scope is 0～100%RH; The HD2021T type irradiance sensor of Italian Dealto company is selected in the collection of irradiance sensor, and measurement range is 0～100KLux; CO in greenhouse
2measurement of concetration is selected domestic CY8100 type CO
2concentration sensor, nutrient solution conductivity
eCmeasure the Cond3310 type of the German WTW of employing company
eCsensor, nutrient solution pH value is measured and is adopted BPH-200A type pH value sensor.Data collecting card is the NI USB-6251 type data collecting card of America NI company, and its AD precision is 16, has 8 tunnel difference BNC analog inputs, and single channel sampling rate is 1.25 MS/s.The output signal of environment temperature, humidity, irradiance, conductivity and pH value sensor is adopted to differential mode input data collecting card front end 5 tunnel difference input channels, after A/D conversion, upload computing machine by usb bus, computing machine adopts DELL580 type desk-top computer.Utilize the data acquisition software that data collecting card carries to process greenhouse environment information, the temperature of extraction environment, humidity, illumination, CO
2concentration, nutrient solution
eCwith pH value information;
(3) visible ray-near-infrared reflection spectrum and the multispectral image of the crop gathering are carried out to analyzing and processing, headed by the analysis and processing method of the visible ray-near-infrared reflection spectrum adopting, first carry out filtering, carry out afterwards successive Regression and principal component analysis (PCA) and extract feature; The analysis and processing method of multispectral image, for first strengthening multispectral image and carrying out pixel-level image fusion, is cut apart background by super green feature and two-dimensional histogram afterwards, finally carries out color (gray scale) mean value computation, texture analysis and fusion feature analysis.Computing machine extracts visible ray-near-infrared reflection spectral signature wavelength of crop nitrogen phosphorus potassium and color, texture, gray average and the fusion feature of multispectral image, and then visible ray-near-infrared reflection spectrum of the nitrogen obtaining, phosphorus, potassium and multispectral image feature are optimized, build reflectance spectrum and the image combining feature space of crop n p k nutrition;
(4) visible ray-near-infrared reflection spectrum of crop and the canopy surface temperature information of crop that gather are analyzed and processed, extract characteristic wavelength and the canopy surface temperature of visible ray-near-infrared reflection spectrum of crop water, combining environmental temperature, humidity information, obtain hat-air Temperature Difference and saturation vapour pressure, set up hat-air Temperature Difference and water stress index model; Build reflectance spectrum and the canopy surface temperature assemblage characteristic space of crop water by characteristic optimization;
(5) visible ray-near-infrared reflection spectral light spectrum information and the multispectral image information of crop gathering is analyzed and processed, extract the leaf area index of crop and stem is thick, plant height, fruit morphology feature; And according to Continuous Observation data, try to achieve plant growth rate and fruit growth speed;
(6) utilize that n p k nutrition, water and nutrient information and the leaf area index of the crop that obtains, stem are thick, plant height, plant growth rate, fruit growth speed growth information, and the temperature of greenhouse, humidity, illumination, CO
2concentration, nutrient solution
eCwith pH value information, computing machine carries out continuous monitoring record and format, as the growth of crop and the detection data of environmental information.
Claims (3)
1. the growth of the chamber crop based on many heat transfer agents and environmental information detection method, utilize multispectral instrument directly to obtain visible ray-near-infrared reflection spectral information of chamber crop, utilize temperature sensor, humidity sensor, irradiance sensor to obtain temperature, humidity, the illumination information of light box, it is characterized in that carrying out according to following step:
(1) utilize spectrometer and thermal imaging system directly to obtain multispectral image information and the canopy surface temperature information of chamber crop;
(2) utilize CO
2concentration sensor,
eCobtain the CO of greenhouse with pH value sensor
2concentration, nutrient solution conductivity and pH value information;
(3) visible ray-near-infrared reflection spectrum and the multispectral image of the crop gathering are carried out to analyzing and processing, extract visible ray-near-infrared reflection spectral signature wavelength of crop nitrogen phosphorus potassium and color, texture, gray average and the fusion feature of multispectral image, and then visible ray-near-infrared reflection spectrum of the nitrogen obtaining, phosphorus, potassium and multispectral image feature are optimized, build reflectance spectrum and the image combining feature space of crop n p k nutrition;
(4) visible ray-near-infrared reflection spectrum of crop and the canopy surface temperature information of crop that gather are analyzed and processed, extract characteristic wavelength and the canopy surface temperature of visible ray-near-infrared reflection spectrum of crop water, combining environmental temperature, humidity information, obtain hat-air Temperature Difference and saturation vapour pressure, set up hat-air Temperature Difference and water stress index model; Build reflectance spectrum and the canopy surface temperature assemblage characteristic space of crop water by characteristic optimization;
(5) visible ray-near-infrared reflection spectral light spectrum information and the multispectral image information of crop gathering is analyzed and processed, extract the leaf area index of crop and stem is thick, plant height, fruit morphology feature; And according to Continuous Observation data, try to achieve plant growth rate and fruit growth speed;
(6) utilize that n p k nutrition, water and nutrient information and the leaf area index of the crop that obtains, stem are thick, plant height, plant growth rate, fruit growth speed growth information, and the temperature of greenhouse, humidity, illumination, CO
2concentration, nutrient solution
eCwith pH value information, computing machine carries out continuous monitoring record and format, gets final product to obtain the growth of crop and the detection data of environmental information.
2. the growth of the chamber crop based on many heat transfer agents according to claim 1 and environmental information detection method, it is characterized in that the analysis and processing method of the visible ray-near-infrared reflection spectrum adopting in wherein said step (3), (4), (5), carry out according to following step: first carry out filtering, carry out afterwards successive Regression and principal component analysis (PCA) and extract feature.
3. the growth of the chamber crop based on many heat transfer agents according to claim 1 and environmental information detection method, it is characterized in that the analysis and processing method of the multispectral image adopting in wherein said step (3), (4), (5), carry out according to following step: first strengthen multispectral image and carry out pixel-level image fusion, cut apart background by super green feature and two-dimensional histogram afterwards, finally carry out the calculating of color gray average, texture analysis and fusion feature analysis.
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| CN110736809A (en) * | 2019-10-28 | 2020-01-31 | 沈阳农业大学 | sunlight greenhouse crop water stress diagnosis system |
| CN111084030A (en) * | 2019-12-30 | 2020-05-01 | 中国科学院西安光学精密机械研究所 | Container AI spectrum planting system simulating mars microgravity and control method thereof |
| CN111066535A (en) * | 2019-12-30 | 2020-04-28 | 中国科学院西安光学精密机械研究所 | Planting greenhouse using AI spectrum and planting method |
| CN111624165A (en) * | 2020-05-20 | 2020-09-04 | 农芯科技（北京）有限责任公司 | Synchronous measurement system and method for meteorological environment parameters and spectrums |
| CN112903787B (en) * | 2021-01-18 | 2023-04-07 | 北京农业智能装备技术研究中心 | Plant ion absorption multi-parameter detection device and system |
| CN116797601B (en) * | 2023-08-24 | 2023-11-07 | 西南林业大学 | Image recognition-based Huashansong growth dynamic monitoring method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101936882A (en) * | 2010-08-10 | 2011-01-05 | 江苏大学 | Nondestructive testing method and device for nitrogen and water of crops |
| CN102081039A (en) * | 2010-08-17 | 2011-06-01 | 江苏大学 | Environment-controllable hyperspectral image detecting device for crop nutrition and moisture |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003050996A (en) * | 2001-08-08 | 2003-02-21 | Hitachi Ltd | Image acquisition device for gene function analysis |
| JP3885046B2 (en) * | 2003-09-16 | 2007-02-21 | 株式会社日立製作所 | Plant growth analyzer |
-
2011
- 2011-11-17 CN CN201110363670.6A patent/CN102506938B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101936882A (en) * | 2010-08-10 | 2011-01-05 | 江苏大学 | Nondestructive testing method and device for nitrogen and water of crops |
| CN102081039A (en) * | 2010-08-17 | 2011-06-01 | 江苏大学 | Environment-controllable hyperspectral image detecting device for crop nutrition and moisture |
Non-Patent Citations (10)
| Title |
|---|
| 《作物生长多传感信息检测系统设计与应用》;张晓东等;《农业机械学报》;20090930;第40卷(第9期);第164-170页 * |
| 《基于多光谱视觉的油菜叶片氮素营养检测方法研究》;张元等;《农机化研究》;20091130(第11期);第83-85页 * |
| 《生菜叶片含水率光谱特征模型研究》;毛罕平等;《农业机械学报》;20110531;第42卷(第5期);第166-170页 * |
| 《黄瓜叶片叶绿素含量近红外光谱无损检测》;石吉勇等;《农业机械学报》;20110531;第42卷(第5期);第178-182、141页 * |
| JP特开2003-50996A 2003.02.21 |
| JP特开2005-87042A 2005.04.07 |
| 张元等.《基于多光谱视觉的油菜叶片氮素营养检测方法研究》.《农机化研究》.2009,(第11期), |
| 张晓东等.《作物生长多传感信息检测系统设计与应用》.《农业机械学报》.2009,第40卷(第9期), |
| 毛罕平等.《生菜叶片含水率光谱特征模型研究》.《农业机械学报》.2011,第42卷(第5期), |
| 石吉勇等.《黄瓜叶片叶绿素含量近红外光谱无损检测》.《农业机械学报》.2011,第42卷(第5期), |
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