252 results about "Crop mapping" patented technology

The invention discloses a close planting farmland growth vigor assessment method and system based on image processing, and relates to the field of agricultural information, and the method comprises the following steps: S1, multi-source data collection and preprocessing; s2, improving image segmentation, and extracting crop features; and S3, multi-dimensional growth vigor evaluation. According to the method, the field block level, the plant level and the whole growth period are covered through multi-source data collection, a generative adversarial network is used for repairing and shielding the plant image and restoring complete form information, the segmentation problem in a close planting scene is solved, the accuracy of close planting crop image analysis is improved, accurate registration of multi-modal data is achieved by means of feature point matching, and the accuracy of close planting crop image analysis is improved. The graph neural network optimizes image segmentation, effectively distinguishes overlapped leaves and stalks, deeply fuses multi-modal features and dynamically selects a fusion strategy, improves feature distinguishability, constructs a dynamic adaptive evaluation model, improves generalization ability and evaluation precision, identifies and intervenes abnormities in real time, and improves crop anti-risk ability and yield prediction accuracy.

The invention provides a multi-modal intelligent irrigation decision algorithm based on causal inference, and the algorithm comprises the steps: carrying out the knowledge distillation of a pre-trained image feature extractor based on causal inference, and fully extracting the features of crop time sequence information and image information; according to the method, multi-modal data is considered, and heterogeneous data is deeply fused. Through knowledge distillation of an image feature extractor, the reasoning speed of the model is higher, the prediction accuracy is higher, end-to-end water shortage state prediction can be carried out based on greenhouse environment data, root system environment data and crop images, and a prediction result can be used for guiding crop irrigation and correcting the irrigation frequency and the irrigation amount of crops. And a more intelligent and comprehensive irrigation decision is realized.

The invention relates to the technical field of image recognition, in particular to a crop disease monitoring and hazard degree evaluation method. The method comprises the following steps: acquiring a crop image set; performing object growth stage image screening on the crop image set to obtain a crop growth stage image set; performing crop growth morphological feature extraction on the crop growth stage image set to obtain crop growth morphological feature data; performing virtual crop health visualization according to the crop growth morphological feature data to obtain a virtual crop growth health image set; acquiring crop growth environment data; according to the virtual crop growth health image set, crop stage growth vigor abnormity matching is carried out on the crop image set, and crop abnormal growth stage data is generated. According to the invention, through multi-dimensional information acquisition, staged feature extraction, combination of environment and internal disease monitoring, hierarchical risk assessment and dynamic adjustment of prevention and control strategies, the accuracy and effectiveness of disease identification are improved.

The invention relates to the technical field of agricultural environment intelligent regulation and control, in particular to an agricultural environment intelligent regulation and control method and system based on a cloud platform, and the method comprises the steps: collecting farmland environment parameters and crop image data through a multi-source sensor, filtering the farmland environment parameters and crop image data through an edge computing node, and transmitting the filtered farmland environment parameters and crop image data to the cloud platform; preprocessing the farmland environment parameters and the crop image data, and extracting time domain and space domain features; an agricultural environment state index prediction model is constructed based on a multi-head self-attention encoder, training of the agricultural environment state index prediction model is completed through historical data, the agricultural environment state index prediction model is deployed as a cloud service API, farmland environment parameters and crop image data input in real time are processed online, a control instruction is generated according to a prediction result, and the control instruction is sent to the cloud service API. Accurate and intelligent regulation and control of the agricultural environment are completed through Internet of Things protocol issuing equipment. Through dynamic feature modeling and multi-index collaborative optimization, the precision of agricultural environment regulation and control and the resource utilization efficiency are remarkably improved.

The invention provides an intelligent inspection robot and a monitoring system, and the method comprises the steps: generating a three-dimensional map of a to-be-inspected area and an optimal inspection path through an inspection navigation module, so as to drive the intelligent inspection robot to inspect the to-be-inspected area according to the optimal inspection path, and generating inspection data; environment monitoring data is generated through the environment monitoring module; collecting crop image data and the environment monitoring data in real time through a pest and disease detection module, and generating pest and disease detection data and pest and disease prediction data based on a pre-trained crop pest and disease prediction model; therefore, accurate path navigation and autonomous obstacle avoidance are realized, the growth environment of crops and the pest and disease damage conditions of the crops are monitored in real time, the inspection efficiency and the pest and disease damage detection accuracy can be improved, healthy growth of the crops is ensured, and the system can adapt to inspection tasks of various complex agricultural operation environments.

The invention relates to the technical field of agricultural intelligent monitoring, in particular to a crop growth detection system and method based on machine vision, and the system comprises an image collection module, a morphological feature capture module, a growth trend judgment module, an abnormal region marking module and a state information output module. According to the method, crop images are collected through a multi-angle camera, leaf contours, stem bending and plant spacing are extracted, multi-dimensional modeling of morphology is realized, structural change identification is enhanced, time sequence comparison of key morphological characteristics is realized, identification precision and time efficiency are improved, leaf and stem change trends are continuously analyzed, offset is quantified, and growth abnormity is early warned in advance; health degradation identification is combined with fluctuation area marking, dynamic monitoring is achieved, high-risk positioning is carried out on a time overlapping area, time-space locking is enhanced, precise management is assisted, the process is from image analysis to abnormal focusing, an information reasoning chain is established, and the monitoring precision and response efficiency are remarkably improved.

The invention discloses a sparse sample crop drawing method based on weak supervision semantic segmentation. The method comprises the following steps: acquiring monthly synthesis time sequence reflectivity data in a research area; obtaining sparse dotted crop labels in the research area, and processing monthly synthesis time sequence reflectivity data to obtain image edge labels; constructing a time sequence semantic segmentation model; constructing a multi-task weakly supervised learning network, constructing a total loss function, and training the time sequence semantic segmentation model and the multi-task weakly supervised learning network; and obtaining monthly synthesis time sequence reflectivity data, image edge labels and sparse point crop labels of a to-be-predicted region, and obtaining final crop categories of all sample points of the to-be-predicted region by using a time sequence semantic segmentation model and a multi-task weak supervised learning network. Compared with a traditional method, the method has the advantages that the performance is better in different crop category classification, and the F1-score of each crop is remarkably improved.

The embodiment of the invention belongs to the field of artificial intelligence, is applied to the field of smart agriculture, and relates to a crop monitoring method, which comprises the following steps: acquiring images and growth environment data of crops in a target area according to preset time nodes to obtain crop images and growth environment data corresponding to each time node; respectively carrying out standardization processing on the crop images to obtain standardized crop images; inputting the crop standardized image into a preset convolutional neural network model for image processing, and generating crop growth state information; performing feature construction on the crop growth state information and the growth environment data by a sliding window method to obtain a time sequence feature set; inputting the time sequence feature set into a preset crop growth prediction model to generate growth prediction information of crops in the target area; and generating visual information according to the time sequence feature set and the growth prediction information, and feeding back the visual information to the user terminal. The crop monitoring efficiency can be improved, and the growth state can be predicted.

The present disclosure provides a decision-making method and apparatus for water and fertilizer stress of crops, and a mobile phone terminal, and belongs to the technical field of intelligent agriculture. The method includes: determining a current crop coefficient and canopy temperature of a crop to be identified on the basis of a first crop image and a second crop image; determining a water stress state and a nutrient stress state on the basis of the current crop coefficient and canopy temperature of the crop to be identified; and processing the water stress state and the nutrient stress state with a fuzzy control algorithm, and determining a water and fertilizer stress decision result of the crop to be identified.

The invention relates to the technical field of water and fertilizer application evaluation, and particularly discloses an intelligent evaluation method for water and fertilizer utilization efficiency and an operation system, and the method comprises the steps: obtaining a crop image of a crop corresponding to a fertilization parameter; identifying the crop image, outputting a growth evaluation report, and constructing a mapping relation from the fertilization parameters to the growth evaluation report for evaluating the fertilization parameters; fertilization parameters are recorded, meanwhile, crop images are obtained through a visual monitoring point, the crop images are recognized, the growth conditions before and after fertilization are determined, then the growth difference before and after fertilization is determined, and the mapping relation from the fertilization parameters to the growth difference is constructed; the growth difference of the fertilization parameters can be predicted before the fertilization behavior, and the prediction accuracy is high.

The invention relates to a multi-modal fusion corn harvester header height adaptive adjustment method and system, and belongs to the technical field of automation control, the method comprises the following steps: using a binocular camera to collect crop images, and preprocessing the crop images to obtain a depth map; the image is input into a YOLOV8n model, and coordinates of a center point at the bottom of a bounding box of the corncob are obtained through model operation; performing plane fitting on the depth map to obtain a ground height; calculating a corn ear height initial value based on the ground height and the center point coordinate; vibration data of working of the harvester is collected to perform vibration compensation on the initial height value to obtain a height correction value; and dynamically adjusting the header height of the harvester by adopting a dynamic fuzzy PID algorithm based on the correction value. According to the method, the target detection model and the dynamic fuzzy PID algorithm are combined, so that the problems of low visual detection precision and control response lag of existing agricultural machinery in a complex field scene are solved, and crop loss in harvesting operation is effectively reduced.

The present invention belongs to the technical field of crop mapping based on remote-sensing images, and relates to a method and system for crop mapping across large regions with low sample dependence. The method includes: acquiring remote sensing data, ground sample data, meteorological data, soil data, establishing geographically divided crop planting regions; establishing key growth period model libraries corresponding to individual crop regions; constructing machine learning models based on a plurality of machine learning algorithms, to obtain machine learning crop extraction models; selecting an optimal machine learning crop extraction model; acquiring a spatial crop distribution base map; performing product correction based on the disaster information; and acquiring a regional crop map using a target crop extraction model adapted for the disaster response. The present invention, achieve high-accuracy and large-scale crop mapping, and reduce the crop sample dependence of crop mapping.

The invention relates to a crop irrigation demand prediction method based on an intelligent integrated prefabricated pump station, and belongs to the technical field of artificial intelligence. The method comprises the following steps: collecting a crop image in a prefabricated pump station irrigation scene, obtaining auxiliary data, calculating a daily accumulated reference evapotranspiration value, and obtaining a time-synchronized multi-modal data set; labeling and dividing the multi-modal data set; constructing an irrigation demand prediction model, wherein the irrigation demand prediction model comprises a spectral reflectivity enhancement module, a double-flow separation convolution module, a cross gating fusion module, a region sensitive pyramid module and a coupling prediction head module; inputting data in the divided training set into the model, and training the model through weighted total loss to obtain a trained model; inputting a to-be-detected image into the trained model to obtain a soil water content and transpiration prediction result; and performing irrigation decision based on the soil water content and transpiration prediction result. The accuracy of irrigation prediction can be improved.

The invention discloses a DNA (Deoxyribose Nucleic Acid) fingerprint spectrum for identifying a feeding oat variety and application thereof, relates to the technical field of biology, and provides 21 molecular markers capable of analyzing and identifying the feeding oat variety. A molecular probe combination, a gene chip and a kit prepared from the 21 SNP molecular markers can play an important role in the aspects of the blood relationship and genetic basis of oat crops, variety certification and determination, counterfeit and shoddy seed identification, management and detection of genetic breeding and crop maps and the like.

The invention discloses a method for predicting images in a whole growth period based on crop genotypes of artificial intelligence. The method comprises the steps of collecting crop images, processing the crop images, constructing a multi-layer perceptron neural network and a condition control generative adversarial network for training so as to learn the mapping relation of crop genotypes, phenotypes and images in the whole growth period, obtaining a crop gene-phenotype prediction model and a phenotype-image prediction model, and finally obtaining the crop genotype-phenotype prediction model and the phenotype-image prediction model through the two models. Based on the genotype of a to-be-predicted crop, a visual result of the whole growth period of the crop in an ideal environment under the regulation of the gene is predicted. According to the method, based on an artificial intelligence technology, a multi-dimensional space multi-layer perception network and a condition control generative adversarial network are constructed, and vivid and accurate visual prediction of crops is realized by deeply utilizing mapping rules contained in crop genes, phenotypes and image big data.

The invention relates to a field water and fertilizer intelligent control method based on the Internet of Things, and the method comprises the steps: carrying out the crop type recognition and growth stage recognition through collecting crop images, soil water and fertilizer data and meteorological prediction data, and generating an initial water and fertilizer control scheme of a current time node; a soil water and fertilizer prediction model is created to predict the soil water and fertilizer change trend in a specified time period, the initial water and fertilizer control scheme of the current time node is adjusted according to the predicted soil water and fertilizer change trend, growth cycle characteristics corresponding to crop types are obtained based on big data, and a dynamic water and fertilizer control scheme is formulated. A water and fertilizer adjusting control scheme is executed at a current time node, a dynamic water and fertilizer control scheme is executed at a specified time node, the field water and fertilizer intelligent control method integrating Internet of Things sensing, intelligent identification, trend prediction, dynamic planning and closed-loop feedback is realized, the whole process intelligence from data acquisition to scheme execution is realized, and the water and fertilizer utilization efficiency is improved. The agricultural production cost is reduced.

The invention provides a staple food grain crop disease grading method and system based on a prompt-driven large model, and the method comprises the following steps: collecting and preprocessing a main food crop image, and obtaining a main food crop data set; training a ResNet-18 network based on the main grain crop data set to perform initial disease grading, and obtaining disease characterization prompt information; utilizing disease characterization prompt information to drive a prompt drive large model, and segmenting a focus area and a leaf area of the to-be-graded main grain crop image; and calculating the proportion of the lesion area in the leaf area, obtaining a final fine-grained disease grading result of the to-be-graded main grain crop image, and completing staple food crop disease grading based on the prompt-driven large model. According to the method, automatic detection and grading of crop diseases are realized by integrating deep learning and an interactive segmentation technology, so that the accuracy of disease detection and classification is improved, possibility is provided for optimizing the use of pesticides, and environmental pollution and the risk of crop drug resistance are reduced.

The invention relates to the technical field of fertilization and irrigation regulation and control, and discloses a fertilization and irrigation two-way regulation and control system under water demand and fertilizer demand coupling modeling, and the system constructs the fertilization and irrigation two-way regulation and control system based on the water demand and fertilizer demand coupling modeling. The multi-source data acquisition module is used for acquiring soil, crop growth period and meteorological data through a layered soil sensor, crop image acquisition equipment and a meteorological acquisition point; the water and fertilizer scheme generation module calculates irrigation amount, fertilization type and dosage based on data, and adjusts intervals in combination with temperature; the operation execution module controls the operation of the irrigation and fertilization equipment and records the state; and the data correction module compares the sensor with the sample data to generate a correction scheme. The system realizes accurate regulation and control through a closed loop of data acquisition, scheme generation, execution and correction, simultaneously covers equipment state monitoring and early warning, and ensures efficient fertilization and irrigation to adapt to crop requirements.

The invention relates to the technical field of biological breeding, in particular to a biological breeding management method and system based on big data. The method comprises the following steps: acquiring original germplasm data; multi-source heterogeneous data integration is carried out on the original germplasm data, the data integration comprises genotype sequencing integration and historical phenotype integration, and integrated germplasm data is generated and stored in a database in a distributed mode; obtaining original growth data, performing data preprocessing, generating standardized germplasm resource comprehensive data including a crop image feature set and an environment response parameter set, and synchronizing the data to the database; and performing whole genome selection prediction on the integrated germplasm data based on the standardized germplasm resource comprehensive data to generate a genotype-phenotype prediction model. By integrating germplasm resources, field management, breeding management, phenotype management, genotype management and whole genome selection, the accuracy and adjustment flexibility of biological breeding management are improved.

The invention relates to the technical field of computer vision, in particular to a light-weight crop image super-resolution and disease identification method and a light-weight crop image super-resolution and disease identification system. Comprising the steps of extracting crop disease images in a database as a data set; preprocessing the data set, and training a disease recognition model according to a deep convolutional neural network; then training an image super-division model; distilling the image super-division model to obtain a lightweight student model; obtaining a target crop image and carrying out image preprocessing; and carrying out image super-division on the target crop image, and completing disease identification of the target crop based on the disease identification model. According to the invention, the generative adversarial network and the model distillation means are constructed in a combined manner, the generator and the discriminator are used for continuous adversarial learning, and the model distillation is used for transferring knowledge from a large-scale and complex teacher model to a small-scale and efficient student model; therefore, the number of parameters and calculation requirements of the model are significantly reduced while the image enhancement performance is maintained.

The invention relates to the technical field of agricultural irrigation, in particular to an intelligent irrigation control system and method based on an intelligent crop growth model. The method comprises the steps of collecting temperature, humidity, illumination and soil moisture data of a crop growth environment, and taking the data as input of a machine learning model; an LSTM machine learning algorithm is utilized to construct a crop growth model used for predicting the crop growth trend and calculating the irrigation water demand; in the crop growth process, crop image data are acquired in stages and compared with a standard growth image, similarity and a correction coefficient are calculated, and the predicted growth trend is corrected in real time; according to the corrected growth trend, the irrigation water demand is dynamically adjusted, and closed-loop feedback control is formed; according to the corrected irrigation water demand, the irrigation equipment is accurately controlled for irrigation, and intelligent monitoring of crop growth and efficient utilization of water resources are achieved.

The invention provides a multi-modal agricultural technology question and answer method and system, and belongs to the technical field of artificial intelligence, and the method comprises the steps: extracting a text feature vector, an image feature vector, a time sequence feature vector and a spatio-temporal context feature vector according to a query text and a crop image; calculating an initial fusion feature according to the spatio-temporal context feature vector, the image feature vector and the time sequence feature vector; when the query text contains a semantic entity of a preset type, calculating to obtain a final fusion feature according to the text feature vector and the initial fusion feature; splicing the final fusion feature and the text feature vector, and mapping the spliced vector to a space-time knowledge graph for reasoning to obtain a causal reasoning path; and generating a question and answer result according to the causal reasoning path. According to the method, deep alignment of time and space and semantics is carried out on the multi-modal agricultural data, and causal reasoning is carried out in combination with the knowledge graph with time and space constraints, so that the accuracy of a question and answer result is remarkably improved.

The invention provides an agricultural greenhouse digital cockpit monitoring method and system based on Unity 3D and AIoT, and relates to the technical field of agricultural information, and the system realizes accurate monitoring and regulation of a greenhouse environment through multi-modal data acquisition, intelligent decision and virtual-real interaction. The method comprises the following steps: constructing a three-dimensional virtual model simulation environment; the Internet of Things nodes collect data such as temperature, humidity and illumination, and the inspection vehicle obtains crop images and preprocesses the crop images; a YOLOv8 model library and an OfficientDet-D7 model library are adopted to recognize diseases and insect pests, and the accuracy is improved by combining multi-model voting; a regulation and control strategy is generated based on the crop growth model, and remote control and optimization are performed through a VR cockpit. The system comprises an environment sensing unit, a mobile inspection device, a cloud platform, a VR interaction terminal and a regulation and control execution system. The Internet of Things node communicates with the cloud, the inspection vehicle performs full-ridge inspection, the cloud supports disease and insect pest recognition and strategy generation, and the VR cockpit provides immersive interaction. According to the method, multi-source data, edge intelligence and reinforcement learning are fused, and the data credibility and the recognition accuracy are improved.

The invention relates to the field of intelligent greenhouse management, and particularly discloses an intelligent greenhouse management system based on computer vision and deep learning, and the system comprises a data collection module which obtains environment data and soil data in a greenhouse in real time; the image acquisition module is used for acquiring crop growth images; the core controller preprocesses the data acquired by the data acquisition module and the image acquisition module and uploads the data to the cloud database; the computer vision and deep learning analysis module is used for identifying crop types and pest and disease damage conditions based on the crop images; the water-fertilizer control unit comprises an intelligent control module, a water-fertilizer all-in-one machine and a drip irrigation system; the user interaction module provides remote monitoring and control functions for a user; according to the system and the method, the condition of economic loss caused by the fact that crop diseases and insect pests cannot be correctly treated due to insufficient experience in traditional agriculture is overcome, environmental visualization is realized, a farmer can conveniently monitor the greenhouse in real time, precise irrigation and fertilization are realized, the agricultural production efficiency is improved, and resource waste is reduced.

The invention relates to an insect pest detection processing method, system and device, and belongs to the field of insect pest detection.The method comprises the steps that a crop image in a target area is collected, black spot recognition and malformation recognition are conducted on the crop image, and black spot parameters and malformation parameters are obtained; according to the black spot parameter and the malformation parameter, calculating to obtain a first insect pest parameter, and configuring an insect body detection range and an insect body detection scale; collecting a detection image set, and carrying out insect body recognition to obtain an insect body recognition result; and processing according to the insect body identification result to obtain a second insect pest parameter, and calculating to obtain an insect pest parameter as an insect pest detection processing result in combination with the first insect pest parameter. According to the method, the technical problems of inaccurate detection and low treatment efficiency caused by the fact that pest distribution characteristics are not considered in mangosteen pest detection in the prior art are solved, and the technical effects of analyzing the pest distribution characteristics through crop pest symptoms, optimizing a detection strategy and improving pest detection accuracy and treatment efficiency are achieved.

The invention provides an unmanned aerial vehicle pesticide spraying area recognition method and system combined with image recognition, and relates to the technical field of agricultural unmanned aerial vehicle operation, and the method comprises the steps: firstly obtaining an image set which is collected by an unmanned aerial vehicle and comprises farmland scene images at different time periods, and carrying out the region semantic association analysis to generate a farmland region semantic association graph; next, an image recognition model is called to perform growth state recognition on the crop image part, and a spraying area adaptation model is constructed in combination with the semantic association graph; then, based on the spraying area adaptation model, evaluating a spraying demand, dividing farmland sub-areas and generating a spraying area division scheme; determining pesticide spraying parameters of each sub-region according to the spraying region division scheme, and generating an unmanned aerial vehicle pesticide spraying instruction; finally, the instruction is sent to an unmanned aerial vehicle control system, the unmanned aerial vehicle is controlled to execute pesticide spraying operation, and therefore the pesticide spraying accuracy and uniformity can be improved.

The invention provides a navigation line generation method and device, a storage medium and a program product, and relates to the technical field of agricultural production. According to the method, the target area is segmented from the crop image, the target area refers to the crop area and / or the land area, then the reference line of the target area is extracted, the weight coefficient of the reference line is acquired, and the navigation line of the agricultural equipment can be generated according to the weight coefficient and the reference line, so that the navigation line of the agricultural equipment can be generated in the operation process of the agricultural equipment. The navigation line is dynamically generated by integrating the reference line of the target area, so that the accuracy and the real-time performance of agricultural machinery navigation are improved, the artificial interference is reduced, the adaptive capacity of the system to a complex operation environment is enhanced, the operation cost can be effectively saved, and the operation efficiency can be effectively improved.

The invention discloses an AI-based agricultural disease and insect pest identification method. The disease and insect pest identification method comprises the following specific steps: S100, transmitting image data of various diseases and insect pests to a disease and insect pest standard database of identification equipment; s200, carrying out image acquisition on the crops by using identification equipment, carrying out feature extraction on the shot image by using the identification equipment, and processing the extracted image data to enable the image data type to be the same as the image type in the pest and disease damage standard database; s300, comparing the acquired image features with data in a pest and disease damage standard database to determine the type of pest and disease damage; and S400, transmitting the determined pest and disease damage type to terminal equipment in a wireless transmission mode. According to the method, crops are monitored in real time through recognition equipment, the recognition equipment can collect images of the crops around the recognition equipment, and the collected data are compared with data in a database.

The invention discloses an agricultural greenhouse water and fertilizer management system and method based on video recognition, and relates to the technical field of computer image recognition, and the system comprises a camera which is used for collecting crop images; the environment sensor is used for collecting water and fertilizer environment data of the agricultural greenhouse; the local host is provided with a crop identification model, and the crop identification model is used for identifying the real-time growth state of crops in the crop image; the cloud server is deployed with a growth decision model and a growth regulation model, the growth decision model formulates a growth control strategy according to the types and growth stages of crops in the agricultural greenhouse and the water and fertilizer environment data, the growth regulation model calculates an income value, and a regulation scheme for the growth control strategy is solved by taking the maximum income value as a target; and forming an adjusted growth control strategy. According to the method, the growth state of the crops is considered on the whole instead of achieving the optimal state of some crops, and the benefit of the agricultural greenhouse is improved.

The invention discloses an intelligent agricultural irrigation method which comprises the following steps: S1, carrying out environment monitoring on a target area to obtain environment information of the target area; s2, performing soil detection on the target area to obtain soil information of each sub-planting area in the target area; s3, analyzing crops planted in each sub-planting area in the target area, and determining growth stages of various crops; s4, according to the environment information of the target area, the soil information of each sub-planting area in the target area and the growth stage of each type of crops, calculating irrigation coefficients of each type of crops; and S5, determining irrigation methods of various crops. According to the method, the growth stages of various crops are determined, the growth stages of various crops are obtained by comparing the crop images with the growth crop standard models of various crops, and the growth stages of various crops are obtained according to the crop images in combination with the CNNs, so that the growth stages of various crops can be accurately judged; and determining irrigation methods of various crops.