CN102506938B - Detecting method for greenhouse crop growth information and environment information based on multi-sensor information - Google Patents
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Abstract
本发明基于多传感信息的温室作物生长和环境信息检测方法，属于温室作物生长信息和环境信息检测技术领域。利用光谱仪、多光谱成像仪和热成像仪获取温室作物的光谱、多光谱图像和冠层温度信息；利用温度、湿度、辐照度、CO2浓度、EC和pH值传感器获取温室的温光水气肥环境信息。对作物营养、水分的光谱、图像和冠层温度特征进行优化，得到氮磷钾营养和水分特征空间；对作物的光谱和图像形态特征进行提取，得到作物的叶面积指数、茎粗、植株和果实生长速率；将获取的作物营养、水分、长势和温光水气肥温室环境信息进行连续监测记录并格式化，作为温室作物的生长和环境综合检测信息。利用该方法获取的信息，能够根据温室作物生长的实际需求进行水肥管理和环境调控。
The invention discloses a greenhouse crop growth and environmental information detection method based on multi-sensing information, and belongs to the technical field of greenhouse crop growth information and environmental information detection. Use spectrometers, multispectral imagers and thermal imagers to obtain the spectrum, multispectral images and canopy temperature information of greenhouse crops; use temperature, humidity, irradiance, CO2 concentration, EC and pH sensors to obtain temperature, light, water, gas and fertilizer in greenhouses environmental information. Optimize the spectrum, image and canopy temperature characteristics of crop nutrition and water to obtain the characteristic space of nitrogen, phosphorus and potassium nutrition and water; extract the spectral and image morphological characteristics of crops to obtain the leaf area index, stem diameter, plant and Fruit growth rate; the acquired crop nutrition, water, growth status and temperature, light, water, gas and fertilizer greenhouse environment information are continuously monitored and formatted as comprehensive detection information for greenhouse crop growth and environment. Using the information obtained by this method, water and fertilizer management and environmental regulation can be carried out according to the actual needs of greenhouse crop growth.
Description
技术领域 technical field
本发明属于温室作物生长信息和环境信息检测技术领域，涉及一种基于多传感信息的温室作物生长和环境信息检测方法，特指利用光谱、视觉图像、红外温度探测等多种无损探测技术，结合温室环境的温度、湿度、光照、CO2浓度和营养液EC(电导率)、pH值的检测，获取设施作物的氮、磷、钾、水分和叶面积指数、茎粗、植株和果实生长速率，以及温光水气肥等作物生长和环境综合信息。利用该方法获取的综合信息，可实现根据作物生长的实际需求进行科学的水肥管理和温室环境调控。 The invention belongs to the technical field of detection of greenhouse crop growth information and environmental information, and relates to a method for detecting greenhouse crop growth and environmental information based on multi-sensing information, specifically referring to multiple non-destructive detection technologies such as spectrum, visual image, and infrared temperature detection. Combined with the detection of temperature, humidity, light, CO2 concentration and nutrient solution EC (conductivity), pH value of the greenhouse environment, obtain nitrogen, phosphorus, potassium, moisture and leaf area index, stem diameter, plant and fruit growth of facility crops rate, as well as comprehensive information on crop growth and environment such as temperature, light, water, gas and fertilizer. Using the comprehensive information obtained by this method, scientific water and fertilizer management and greenhouse environment regulation can be realized according to the actual needs of crop growth.
背景技术 Background technique
温室作物生长信息无损检测主要包括作物氮磷钾营养、水分等养分检测和叶面积指数、茎粗、株高、果实颜色质量、植株和果实生长速率等长势信息检测两个方面。环境信息主要指温室的温度、湿度、光照、CO2浓度和营养液EC和pH值信息。 The non-destructive detection of crop growth information in the greenhouse mainly includes the detection of crop nitrogen, phosphorus and potassium nutrients, water and other nutrients and the detection of leaf area index, stem diameter, plant height, fruit color quality, plant and fruit growth rate and other growth information detection. Environmental information mainly refers to the temperature, humidity, light, CO2 concentration, and nutrient solution EC and pH value information of the greenhouse.
温室生长过程究其本质是作物受环境、营养、水分等外部因子作用，并对其进行转化的复杂的动力学过程。温室内作物生长环境参数的空间分布性强、时空变异性大、多参数间相互影响，加上不同种类作物及不同植株之间的个体差异，造成传统的栽培和环境调控方式很难适应不同种类、不同植株及其不同生育期的生长需要。因此，在对温室作物生长的环境和营养、水分等外部作用因子进行准确检测的基础上，研究环境、营养、水分等外部因子与作物长势、生产过程之间的作用关系，建立基于温室作物生长和环境信息综合评价体系，并根据评价结果，制定最优的控制策略，这对提高我国温室技术的研究水平，实现设施农业的高产、优质、高效、低碳和可持续发展有十分重要的理论意义和实用价值。 The essence of the greenhouse growth process is a complex dynamic process in which crops are affected by external factors such as the environment, nutrition, and water, and transformed. The spatial distribution of crop growth environment parameters in the greenhouse is strong, the spatio-temporal variability is large, and the interaction between multiple parameters, coupled with the individual differences between different types of crops and different plants, makes it difficult for traditional cultivation and environmental regulation methods to adapt to different species. , Different plants and their growth needs in different growth stages. Therefore, on the basis of accurate detection of the environment for greenhouse crop growth and external factors such as nutrition and water, the relationship between external factors such as environment, nutrition and water, crop growth and production process is studied, and the establishment of greenhouse crop growth based on the and environmental information comprehensive evaluation system, and formulate the optimal control strategy based on the evaluation results, which is very important for improving the research level of greenhouse technology in my country and realizing high-yield, high-quality, high-efficiency, low-carbon and sustainable development of facility agriculture significance and practical value.
目前作物的生长信息无损检测主要以光谱技术和图像技术为主。目前，作物营养、水分检测方面已有一些相关研究。在光谱检测方面，申请号为200510088935.0的发明专利申请，公开了一种便携式植物氮素和水分含量的无损检测方法及测量仪器，通过检测植株叶片在四个特征波长处的光谱反射强度信息来进行植物的营养诊断，利用对四个波长植被指数的反演来获取植物的氮素和含水率信息。申请号为200410048127.7的发明专利申请，公开了一种基于自然光照反射光谱的黄瓜叶片含氮量预测方法，可以通过黄瓜叶片在指定波长处的光谱反射强度得出叶片的反射植被指数，进而判断其氮素水平。在视觉图像检测方面，申请号为200710069116.0的发明专利，公开了一种多光谱成像技术快速无损测量茶树含氮量的方法。申请号为200510062298.X的发明专利申请和申请号为200520134360.7的实用新型专利申请，公布了一种油菜氮素营养多光谱图像诊断方法及诊断系统。上述系统均采用3CCD多光谱摄像系统作为视觉采集装置，在计算机控制下，通过3CCD多光谱摄像系统采集植株冠层多光谱图像信息，能够非破坏性的诊断植株的氮素营养状况。在作物的长势检测方面，申请号为200610097576.X的发明专利申请，公开了一种嵌入式农业植物生长状态监测仪及其工作方法，可以对作物生长的环境温湿度、茎粗、株高、土壤粘度和酸碱度进行探测，该系统仅通过茎粗、株高判断作物生长状态，且缺少动态的作物生长评价模型，因此难以对作物生长状态做出全面科学的评价。申请号为200410014648.0的发明专利申请公开了一种用于农作物生长监测及营养施肥处方生成装置和方法，该发明采用摄像机来获取作物的茎、叶、花、果、皮图像信息，利用营养成分检测仪获取农作物和土壤营养信息，由于摄像机仅能获取可见光合成图像，难以对作物的氮磷钾营养和水分特征进行精确分析，营养成分检测仪虽然可以获取作物的营养信息，但其取样和检测方式会对作物造成损害。 At present, the non-destructive detection of crop growth information is mainly based on spectral technology and image technology. At present, there have been some related researches on crop nutrition and moisture detection. In terms of spectral detection, the invention patent application with application number 200510088935.0 discloses a portable non-destructive detection method and measuring instrument for plant nitrogen and water content, which is carried out by detecting the spectral reflection intensity information of plant leaves at four characteristic wavelengths. Nutritional diagnosis of plants, using the inversion of the vegetation index of four wavelengths to obtain plant nitrogen and water content information. The invention patent application with the application number of 200410048127.7 discloses a method for predicting the nitrogen content of cucumber leaves based on natural light reflection spectrum. The reflective vegetation index of the leaves can be obtained from the spectral reflection intensity of the cucumber leaves at a specified wavelength, and then its Nitrogen levels. In terms of visual image detection, the invention patent application number is 200710069116.0, which discloses a method for quickly and non-destructively measuring the nitrogen content of tea trees with multi-spectral imaging technology. The invention patent application with the application number 200510062298.X and the utility model patent application with the application number 200520134360.7 disclose a multi-spectral image diagnosis method and diagnosis system for rapeseed nitrogen nutrition. The above systems all use 3CCD multi-spectral camera system as the visual acquisition device. Under computer control, the 3CCD multi-spectral camera system collects multi-spectral image information of plant canopy, which can non-destructively diagnose the nitrogen nutrition status of plants. In terms of crop growth detection, the invention patent application with the application number 200610097576.X discloses an embedded agricultural plant growth state monitor and its working method, which can monitor the temperature and humidity of the crop growth environment, stem thickness, plant height, Soil viscosity and pH are detected. The system only judges the growth status of crops by stem thickness and plant height, and lacks a dynamic crop growth evaluation model, so it is difficult to make a comprehensive and scientific evaluation of crop growth status. The invention patent application with the application number 200410014648.0 discloses a device and method for crop growth monitoring and nutrient fertilization prescription generation. It is difficult to accurately analyze the nitrogen, phosphorus and potassium nutrition and water characteristics of crops because the camera can only obtain synthetic images of visible light. Although the nutrient composition detector can obtain the nutritional information of crops, its sampling and detection methods Can cause damage to crops.
综上所述，目前作物的生长信息的无损检测主要基于光谱和图像技术。光谱技术可以较便捷获得含氮量、含水率与光谱反射率或其演生量的关系；可见光或近红外视觉图像的颜色（灰度）、纹理、形态特征在一定程度上也能表征作物营养水平、叶面积、茎果叶等信息，作物的冠-气温差与水分胁迫也显著相关。但仅靠光谱、图像和冠层温度单一检测方法，获取营养或水分或叶面积指数、茎粗、株高、果实颜色形态等孤立信息，很难对作物生长状态做出全面、系统、科学的判断。且营养之间、营养与水分之间具有交互作用，检测过程受作物冠层结构、土壤背景光谱及大气窗口、温湿度等环境因子的影响较大，因此，仅仅用光谱技术，或可见光视觉图像、或近红外视觉图像、或植株的冠气温差等单一探测技术不足以准确、全面地反映作物营养、水分和长势等生长信息。而快速准确地获取作物的生长和环境信息，是对作物的生长状态进行科学评价的前提。 To sum up, the current non-destructive detection of crop growth information is mainly based on spectral and image technology. Spectral technology can easily obtain the relationship between nitrogen content, water content and spectral reflectance or its evolution; the color (gray scale), texture, and morphological characteristics of visible light or near-infrared visual images can also characterize crop nutrition to a certain extent. The crown-air temperature difference of crops was also significantly correlated with water stress. However, it is difficult to make a comprehensive, systematic and scientific assessment of crop growth status only by relying on a single detection method of spectrum, image and canopy temperature to obtain isolated information such as nutrition or water or leaf area index, stem diameter, plant height, and fruit color shape. judge. Moreover, there is an interaction between nutrients and between nutrients and water. The detection process is greatly affected by environmental factors such as crop canopy structure, soil background spectrum, atmospheric window, temperature and humidity. Therefore, only spectral technology or visible light visual image , or near-infrared visual images, or plant canopy temperature difference and other single detection technologies are not enough to accurately and comprehensively reflect the growth information of crop nutrition, water and growth. The rapid and accurate acquisition of crop growth and environmental information is a prerequisite for scientific evaluation of crop growth status.
综上所述，目前我国温室园艺信息检测尚无法对设施作物的生长和环境信息进行全面、精确的检测和解析，无法感知和反映作物生长真实的调控要求，造成作物产量潜力没有充分挖掘，运行能耗偏大的问题。鉴于以上原因，目前需要一种全方位获取温室作物的营养、水分和叶面积指数、茎粗、果实和植株生长速率，以及温光水气肥等作物生长和环境综合信息的方法，以指导现代温室生产，提高产量和品质，减少过量施肥和传统调控方式造成的浪费和污染，提高经济效益。 To sum up, at present, my country's greenhouse horticulture information detection is still unable to conduct comprehensive and accurate detection and analysis of the growth and environmental information of facility crops, and cannot perceive and reflect the real control requirements of crop growth, resulting in insufficient exploitation of crop yield potential. The problem of high energy consumption. In view of the above reasons, there is a need for a comprehensive method of obtaining greenhouse crop nutrition, water and leaf area index, stem diameter, fruit and plant growth rate, as well as crop growth and environmental comprehensive information such as temperature, light, water, gas and fertilizer, so as to guide modern greenhouse production. , improve yield and quality, reduce waste and pollution caused by excessive fertilization and traditional control methods, and improve economic benefits.
发明内容 Contents of the invention
本发明的目的是提供一种融合光谱、视觉图像、红外成像多种无损检测技术，结合温室环境温度、湿度、光照、CO2浓度和营养液EC、pH值等作物生长和环境综合信息的准确探测，进而对作物的生长和环境信息进行科学评价，指导温室环境按需调控的信息获取方法，为现代温室环境调控和水肥管理提供科学依据。 The purpose of the present invention is to provide a combination of multiple non-destructive detection technologies of spectrum, visual image, and infrared imaging, combined with the accurate detection of crop growth and environmental comprehensive information such as greenhouse environment temperature, humidity, light, CO2 concentration, nutrient solution EC , pH value, etc. Detection, and then conduct scientific evaluation of crop growth and environmental information, guide the information acquisition method for on-demand regulation of the greenhouse environment, and provide scientific basis for modern greenhouse environment regulation and water and fertilizer management.
为实现上述目的，本发明基于多传感信息的温室作物生长和环境信息检测方法，按照下述步骤进行： In order to achieve the above object, the multi-sensing information-based greenhouse crop growth and environmental information detection method of the present invention is carried out according to the following steps:
（1）利用光谱仪、多光谱成像仪和热成像仪直接获取温室作物的可见光-近红外反射光谱信息、多光谱图像信息和冠层温度信息； (1) Use spectrometers, multispectral imagers and thermal imagers to directly obtain visible light-near-infrared reflection spectrum information, multispectral image information and canopy temperature information of greenhouse crops;
（2）利用温度传感器、湿度传感器、辐照度传感器、CO2浓度传感器、EC和pH值传感器获取温室环境的温度、湿度、光照、CO2浓度、营养液电导率(EC)和pH值信息； (2) Use temperature sensor, humidity sensor, irradiance sensor, CO2 concentration sensor, EC and pH value sensor to obtain temperature, humidity, light, CO2 concentration, nutrient solution conductivity ( EC ) and pH value information of the greenhouse environment ;
（3）对采集的作物的可见光-近红外反射光谱和多光谱图像进行分析处理，提取作物氮磷钾的可见光-近红外反射光谱特征波长和多光谱图像的颜色、纹理、灰度均值及融合特征，进而将获取的氮、磷、钾的可见光-近红外反射光谱和多光谱图像特征进行优化，构建作物氮磷钾营养的反射光谱和图像组合特征空间； (3) Analyze and process the collected visible-near-infrared reflectance spectra and multispectral images of crops, extract the characteristic wavelengths of visible-near-infrared reflectance spectra of crop nitrogen, phosphorus and potassium, and the color, texture, gray average and fusion of multispectral images features, and then optimize the acquired visible-near-infrared reflectance spectra and multispectral image features of nitrogen, phosphorus, and potassium to construct the reflectance spectra and image combination feature space of crop nitrogen, phosphorus, and potassium nutrition;
（4）对采集的作物的可见光-近红外反射光谱和作物的冠层温度信息进行分析和处理，提取作物水分的可见光-近红外反射光谱的特征波长和冠层温度，结合环境温度、湿度信息，获取冠-气温差和饱和水汽压，建立冠-气温差和水分胁迫指数模型；通过特征优化构建作物水分的反射光谱和冠层温度组合特征空间； (4) Analyze and process the collected visible light-near-infrared reflection spectrum and crop canopy temperature information, extract the characteristic wavelength and canopy temperature of the visible light-near-infrared reflection spectrum of crop moisture, and combine the environmental temperature and humidity information , obtain the canopy-air temperature difference and saturated water vapor pressure, and establish the canopy-air temperature difference and water stress index model; through feature optimization, construct the feature space of crop water reflectance spectrum and canopy temperature combination;
（5）对采集的作物的可见光-近红外反射光谱光谱信息和多光谱图像信息进行分析和处理，提取作物的叶面积指数和茎粗、株高、果实形态特征；并根据连续观测数据，求得植株生长速率和果实生长速率； (5) Analyze and process the collected visible light-near-infrared reflection spectrum spectral information and multi-spectral image information of the crops, extract the leaf area index, stem diameter, plant height, and fruit shape characteristics of the crops; and based on continuous observation data, calculate The plant growth rate and fruit growth rate were obtained;
（6）利用获取的作物的氮磷钾营养、水分养分信息和叶面积指数、茎粗、株高、植株生长速率、果实生长速率长势信息，以及温室环境的温度、湿度、光照、CO2浓度、营养液EC和pH值信息，计算机进行连续监测记录和格式化，作为作物的生长和环境信息的检测数据。 (6) Use the obtained crop nitrogen, phosphorus and potassium nutrition, water nutrient information and leaf area index, stem diameter, plant height, plant growth rate, fruit growth rate and growth information, as well as the temperature, humidity, light, and CO 2 concentration of the greenhouse environment , Nutrient solution EC and pH value information, the computer continuously monitors and records and formats them as the detection data of crop growth and environmental information.
其中所述的步骤（3）、（4）、（5）中所采用的可见光-近红外反射光谱的分析处理方法，按照下述步骤进行：首先进行滤波，之后进行逐步回归和主成分分析提取特征。 The analysis and processing method of the visible light-near-infrared reflectance spectrum used in the steps (3), (4) and (5) mentioned therein is carried out according to the following steps: firstly filter, then stepwise regression and principal component analysis extraction feature.
其中所述的步骤（3）、（4）、（5）中所采用的多光谱图像的分析处理方法，按照下述步骤进行：首先增强多光谱图像并进行像素级图像融合，之后通过超绿特征和二维直方图分割背景，最后进行颜色（灰度）均值计算、纹理分析和融合特征分析。 The analysis and processing method of the multispectral image adopted in the steps (3), (4) and (5) described therein is carried out according to the following steps: firstly, the multispectral image is enhanced and pixel-level image fusion is performed, and then the supergreen Features and two-dimensional histograms are used to segment the background, and finally color (grayscale) mean value calculation, texture analysis and fusion feature analysis are performed.
本发明的效果是（1）本发明通过光谱、图像、红外温度等多种无损探测技术的有机融合，结合温度、湿度、光照、CO2浓度、营养液EC和pH值等温室环境信息检测，全方位获取作物生长和环境的综合信息，不仅信息获取量更大，更丰富，而且能够更全面、精确地把握作物的生长状态，这在以往的文件中都没有涉及；（2）本发明通过作物的可见光-近红外反射光谱和多光谱图像信息的融合，来综合判断作物的氮磷钾营养水平；通过近红外光谱和冠层红外温度的信息融合来判断作物的水分胁迫状态，通过温室作物的视觉图像，提取其形态特征进而判断作物的长势，这在以往的文件中都没有涉及。 The effect of the present invention is (1) The present invention combines the detection of greenhouse environment information such as temperature, humidity, light, CO2 concentration, nutrient solution EC and pH value through the organic fusion of various non-destructive detection technologies such as spectrum, image and infrared temperature, Obtain comprehensive information on crop growth and environment in an all-round way, not only the amount of information obtained is larger and richer, but also the growth status of crops can be grasped more comprehensively and accurately, which has not been involved in previous documents; (2) the present invention adopts The fusion of visible light-near-infrared reflection spectrum and multi-spectral image information of crops can comprehensively judge the nitrogen, phosphorus and potassium nutrition level of crops; the water stress state of crops can be judged by the information fusion of near-infrared spectrum and canopy infrared temperature, and the greenhouse crop The visual image of the crop, extracting its morphological features and then judging the growth of the crop, which has not been covered in previous documents.
附图说明 Description of drawings
图1是本发明所述方法所需硬件组成和信息流程示意图； Fig. 1 is a schematic diagram of hardware composition and information flow required by the method of the present invention;
图2是本发明基于多传感信息的温室作物生长和环境信息检测方法流程图。 Fig. 2 is a flow chart of the method for detecting greenhouse crop growth and environmental information based on multi-sensing information in the present invention.
具体实施方式 Detailed ways
本发明基于多传感信息的温室作物生长和环境信息检测方法所需硬件组成和信息流程如附图1所示。其所需硬件组成包括光谱仪、多光谱成像仪、热成像仪、温湿度传感器、辐照度传感器、CO2浓度传感器、EC和pH值传感器、数据采集卡和计算机。其中光谱仪、多光谱成像仪、热成像仪用来采集作物营养、水分、长势等生长信息；辐照度传感器、温湿度传感器、CO2浓度传感器、EC和pH值传感器用来采集温室环境信息。光谱仪、多光谱成像仪、热成像仪获取的信息读取并传输给计算机；温湿度传感器、辐照度传感器、CO2浓度传感器、EC和pH值传感器的输出信号经过数据采集卡进行A/D转换后上传计算机。 The hardware composition and information flow required by the greenhouse crop growth and environmental information detection method based on multi-sensing information in the present invention are shown in Figure 1 . The required hardware components include spectrometer, multispectral imager, thermal imager, temperature and humidity sensor, irradiance sensor, CO2 concentration sensor, EC and pH value sensor, data acquisition card and computer. Among them, spectrometers, multispectral imagers, and thermal imagers are used to collect growth information such as crop nutrition, water, and growth; irradiance sensors, temperature and humidity sensors, CO2 concentration sensors, EC and pH sensors are used to collect greenhouse environmental information. The information obtained by the spectrometer, multispectral imager, and thermal imager is read and transmitted to the computer; the output signals of the temperature and humidity sensor, irradiance sensor, CO2 concentration sensor, EC and pH value sensor are A/D through the data acquisition card Upload to computer after conversion.
下面结合附图2来说明对本发明所述该方法的具体实施方式。本发明基于多传感信息的温室作物生长和环境信息检测方法包括以下步骤： The specific implementation of the method described in the present invention will be described below in conjunction with accompanying drawing 2 . The greenhouse crop growth and environmental information detection method based on multi-sensing information of the present invention comprises the following steps:
（1）首先利用光谱仪、多光谱成像仪和热成像仪直接获取温室作物的可见光-近红外反射光谱信息、多光谱图像信息和冠层温度信息； (1) First, use spectrometers, multispectral imagers and thermal imagers to directly obtain the visible light-near-infrared reflection spectrum information, multispectral image information and canopy temperature information of greenhouse crops;
在温室环境下，选择无云的晴天，实施本方法，信息采集时间选择在9：00～15：00；选用的光谱仪为美国ASD公司的FieldSpec® 3型便携式光谱分析仪，其光谱测量范围350-2500nm；选用25°视场的探头，采用漫反射的方式采样，探头距离样本表面2～3cm，光谱测量以10次扫描平均值作为1个采样点光谱，每个样本选取3个采样点，再以其平均值作为作物的光谱反射率值。多光谱成像仪选用美国产MS-3100型多光谱累进扫描数字式相机，MS-3100成像光谱范围为350-1100nm，在俯视视场和R、G、B、NIR和RGB、CIR模式下，距离作物样本冠层70cm处采集中心波长分别为660nm、560nm、460nm的R、G、B图像和中心波长为810nm的近红外图像，及RGB、CIR合成图像；在侧视视场同样模式下，距离植株50cm处，采集中心波长分别为660nm、560nm、460nm、810nm的可见光-近红外多光谱图像，及RGB、CIR合成图像。作物冠层温度的测量选用美国FLUKE公司的TI50红外热成像仪，测量范围为-20～305℃，精度为0.07℃，为了消除太阳方位角及作物种植方向对观测值的影响，仪器与地面成45°，从6个不同方向进行样本测量，每次取6个测定值的平均值作为该样本的冠层温度值。光谱仪、多光谱成像仪和热像仪获取的数据由其自带的专业分析软件进行数据分析和处理。其中光谱分析软件采用自带的ViewSpec Pro 4.05进行光谱预处理和导出，采用化学计量学光谱分析软件NIRSA进行光谱数据处理；多光谱图像数据采用自带的Duncan软件进行数据采集，利用ENVI和IDL软件对多光谱图像进行处理、分析和特征提取；热成像仪采用其自带软件SmartView 1.0进行分析和处理。 In the greenhouse environment, choose a cloudless sunny day to implement this method, and the information collection time is selected from 9:00 to 15:00; the selected spectrometer is the FieldSpec® 3 portable spectrum analyzer from ASD Company in the United States, and its spectral measurement range is 350 -2500nm; use a probe with a 25° field of view, use diffuse reflection sampling, the probe is 2 to 3 cm away from the sample surface, and the spectrum measurement takes the average value of 10 scans as a sampling point spectrum, and selects 3 sampling points for each sample. Then take the average value as the spectral reflectance value of the crop. The multi-spectral imager uses the MS-3100 multi-spectral progressive scanning digital camera made in the United States. The MS-3100 imaging spectral range is 350-1100nm. The R, G, and B images with center wavelengths of 660nm, 560nm, and 460nm, the near-infrared images with a center wavelength of 810nm, and RGB and CIR composite images were collected at 70cm from the canopy of crop samples; At a distance of 50 cm from the plant, the visible-near-infrared multispectral images with center wavelengths of 660nm, 560nm, 460nm, and 810nm were collected, as well as RGB and CIR composite images. The temperature of the crop canopy is measured by using the TI50 infrared thermal imager from the American FLUKE company. The measurement range is -20-305°C and the accuracy is 0.07°C. 45°, the sample was measured from 6 different directions, and the average value of 6 measured values was taken each time as the canopy temperature value of the sample. The data acquired by the spectrometer, multispectral imager and thermal imager are analyzed and processed by their own professional analysis software. Among them, the spectral analysis software uses the built-in ViewSpec Pro 4.05 for spectral preprocessing and export, and the chemometric spectral analysis software NIRSA is used for spectral data processing; the multi-spectral image data is collected using the built-in Duncan software, using ENVI and IDL software Process, analyze and feature extract the multi-spectral images; the thermal imager uses its own software SmartView 1.0 for analysis and processing.
（2）利用温湿度传感器、辐照度传感器、CO2浓度传感器、EC和pH值传感器获取温室环境的温度、湿度、光照、CO2浓度、营养液EC和pH值信息；并将上述传感器采集的信息通过数据采集卡进行数字化转换后上传计算机分析； (2) Use the temperature and humidity sensor, irradiance sensor, CO 2 concentration sensor, EC and pH value sensor to obtain the temperature, humidity, light, CO 2 concentration, nutrient solution EC and pH value information of the greenhouse environment; and collect the above sensors The information is digitized through the data acquisition card and uploaded to the computer for analysis;
环境温湿度度采集选用奥地利的EE08型环境温湿度一体传感器，温度测量范围-40～80°C，湿度测量范围为0～100％RH；辐照度传感器采集选用意大利Dealto公司的HD2021T 型辐照度传感器，测量范围为0～100KLux ；温室内的CO2浓度测量选用国产CY8100型CO2浓度传感器，营养液电导率EC测量采用德国WTW公司的Cond3310型EC传感器，营养液pH值测量采用BPH-200A型pH值传感器。数据采集卡为美国NI公司的NI USB-6251型数据采集卡，其AD精度为16位，具有8路差分BNC模拟输入，单通道采样率为1.25 MS/s。将环境温度、湿度、辐照度、电导率和pH值传感器的输出信号采用差分方式输入数据采集卡前端5路差分输入通道， A/D转换后通过USB总线上传计算机，计算机采用DELL580型台式计算机。利用数据采集卡自带的数据采集软件对温室环境信息进行处理，提取环境的温度、湿度、光照、CO2浓度、营养液EC和pH值信息； The environmental temperature and humidity collection adopts the Austrian EE08 environmental temperature and humidity integrated sensor, the temperature measurement range is -40～80°C, and the humidity measurement range is 0～100%RH; The measurement range is 0-100KLux; the CO2 concentration measurement in the greenhouse uses the domestic CY8100 CO2 concentration sensor, the EC measurement of the nutrient solution conductivity uses the Cond3310 EC sensor of the German WTW company, and the nutrient solution pH value measurement uses BPH- Model 200A pH sensor. The data acquisition card is NI USB-6251 data acquisition card of American NI Company, its AD precision is 16 bits, it has 8 differential BNC analog inputs, and the single channel sampling rate is 1.25 MS/s. The output signals of ambient temperature, humidity, irradiance, conductivity and pH value sensors are differentially input to the front end of the data acquisition card with 5 differential input channels. After A/D conversion, they are uploaded to the computer through the USB bus. The computer adopts a DELL580 desktop computer. . Use the data acquisition software that comes with the data acquisition card to process the greenhouse environmental information, and extract the environmental temperature, humidity, light, CO2 concentration, nutrient solution EC and pH value information;
（3）对采集的作物的可见光-近红外反射光谱和多光谱图像进行分析处理，所采用的可见光-近红外反射光谱的分析处理方法为首先进行滤波，之后进行逐步回归和主成分分析提取特征；多光谱图像的分析处理方法为首先增强多光谱图像并进行像素级图像融合，之后通过超绿特征和二维直方图分割背景，最后进行颜色（灰度）均值计算、纹理分析和融合特征分析。计算机提取作物氮磷钾的可见光-近红外反射光谱特征波长和多光谱图像的颜色、纹理、灰度均值及融合特征，进而将获取的氮、磷、钾的可见光-近红外反射光谱和多光谱图像特征进行优化，构建作物氮磷钾营养的反射光谱和图像组合特征空间； (3) Analyze and process the visible-near-infrared reflection spectrum and multi-spectral images of the collected crops. The analysis and processing method of the visible-near-infrared reflection spectrum used is to filter first, and then perform stepwise regression and principal component analysis to extract features ; The analysis and processing method of the multispectral image is to first enhance the multispectral image and perform pixel-level image fusion, then segment the background through the super green feature and the two-dimensional histogram, and finally perform color (grayscale) mean value calculation, texture analysis and fusion feature analysis . The computer extracts the visible light-near-infrared reflectance spectrum characteristic wavelength of crop nitrogen, phosphorus and potassium and the color, texture, gray-scale average and fusion features of the multispectral image, and then the obtained visible light-near-infrared reflectance spectrum and multispectral image of nitrogen, phosphorus and potassium Image features are optimized to construct the reflectance spectrum and image combination feature space of crop nitrogen, phosphorus and potassium nutrition;
（4）对采集的作物的可见光-近红外反射光谱和作物的冠层温度信息进行分析和处理，提取作物水分的可见光-近红外反射光谱的特征波长和冠层温度，结合环境温度、湿度信息，获取冠-气温差和饱和水汽压，建立冠-气温差和水分胁迫指数模型；通过特征优化构建作物水分的反射光谱和冠层温度组合特征空间； (4) Analyze and process the collected visible light-near-infrared reflection spectrum and crop canopy temperature information, extract the characteristic wavelength and canopy temperature of the visible light-near-infrared reflection spectrum of crop moisture, and combine the environmental temperature and humidity information , obtain the canopy-air temperature difference and saturated water vapor pressure, and establish the canopy-air temperature difference and water stress index model; through feature optimization, construct the feature space of crop water reflectance spectrum and canopy temperature combination;
（5）对采集的作物的可见光-近红外反射光谱光谱信息和多光谱图像信息进行分析和处理，提取作物的叶面积指数和茎粗、株高、果实形态特征；并根据连续观测数据，求得植株生长速率和果实生长速率； (5) Analyze and process the collected visible light-near-infrared reflection spectrum spectral information and multi-spectral image information of the crops, extract the leaf area index, stem diameter, plant height, and fruit shape characteristics of the crops; and based on continuous observation data, calculate The plant growth rate and fruit growth rate were obtained;
（6）利用获取的作物的氮磷钾营养、水分养分信息和叶面积指数、茎粗、株高、植株生长速率、果实生长速率长势信息，以及温室环境的温度、湿度、光照、CO2浓度、营养液EC和pH值信息，计算机进行连续监测记录和格式化，作为作物的生长和环境信息的检测数据。 (6) Use the obtained crop nitrogen, phosphorus and potassium nutrition, water nutrient information and leaf area index, stem diameter, plant height, plant growth rate, fruit growth rate and growth information, as well as the temperature, humidity, light, and CO 2 concentration of the greenhouse environment , Nutrient solution EC and pH value information, the computer continuously monitors and records and formats them as the detection data of crop growth and environmental information.
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Families Citing this family (30)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US12028588B2 (en) | 2018-12-03 | 2024-07-02 | Micasense, Inc. | Image sensor and thermal camera device, system and method |
| WO2020172546A1 (en) * | 2019-02-21 | 2020-08-27 | Cannagri Blockchain, Inc. | Machine vision sensor system for optimal growing conditions |
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| CN110514240A (en) * | 2019-08-09 | 2019-11-29 | 中国农业大学 | Method and device for visualization of greenhouse environment |
| 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 |
| CN111624165B (en) * | 2020-05-20 | 2025-02-21 | 北京市农林科学院信息技术研究中心 | A meteorological environment parameter and spectrum synchronous measurement system and method |
| 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 | A method and system for monitoring the growth dynamics of Huashan pine based on image recognition |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101936882A (en) * | 2010-08-10 | 2011-01-05 | 江苏大学 | A method and device for non-destructive detection of crop nitrogen and moisture |
| 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 not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101936882A (en) * | 2010-08-10 | 2011-01-05 | 江苏大学 | A method and device for non-destructive detection of crop nitrogen and moisture |
| 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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