300 results about "Growth data" patented technology

Provided is an intelligent irrigation control method for rice fields based on a cloud service platform and a system thereof. Constructing a planting condition data set and a condition coefficient, monitoring the current water supply environment of crops, and constructing a water supply coefficient. If the water supply coefficient is lower than the water supply threshold, based on the current growth data of crops, a plurality of corresponding irrigation strategies are matched from the pre-constructed crop irrigation knowledge graph, and the target strategy is selected from a plurality of irrigation strategies; constructing an irrigation observation coefficient set, and constructing irrigation coefficient. If the irrigation coefficient is lower than the efficiency threshold, use the trained strategy optimization model to optimize the target strategy, and control the irrigation coefficient based on the optimized target strategy to complete the irrigation of crops in the irrigation area.

The invention discloses a sheep flock accurate nutrition supply method based on an intelligent feeding algorithm, and the method comprises the following steps: collecting the body type, physiology, movement and environment data of sheep through a multi-modal sensing device, and constructing individual three-dimensional representation based on a neural radiation field model, historical growth data and a real-time health state are analyzed by utilizing an individual nutritional requirement calculation model, an optimal feed distribution strategy is determined by combining an intelligent sheep flock optimization algorithm, a feeding scheme is dynamically adjusted by adopting deep reinforcement learning, and through a cloud-side collaborative calculation architecture, the calculation efficiency is improved and a feeding decision is optimized. A non-intrusive health monitoring technology is combined, a group health evaluation model is constructed, the feed ratio is adjusted in real time, accurate matching of individual and group nutritional requirements is achieved, and through prediction of the individual and group nutritional requirements, self-adaptive adjustment is conducted on feeding parameters, and a dynamic nutrition supply strategy is optimized. The system effectively improves the feeding precision, reduces the feed waste, and optimizes the health management of the sheep flock.

The invention discloses a storage battery capacity attenuation trend prediction method, and belongs to the technical field of storage battery prediction. By collecting voltage, current and temperature data of each monomer in real time and combining historical capacity attenuation and internal resistance growth data, the method identifies a voltage and capacity difference value, evaluates cyclic stress non-uniform distribution, and determines a current sharing proportion and a load unbalance degree. Identifying an abnormal mode of new battery overload and aged battery deep discharge, constructing a mixing abnormal working condition identification mode, if the unbalance degree exceeds the standard, adaptively adjusting the charging and discharging time and the current switching frequency, establishing a load balance control framework, predicting the capacity attenuation rate and the residual cycle index of each monomer, and determining the capacity matching degree and the life matching degree; finally, a comprehensive residual life estimation value and a credibility interval are generated through fusion; the performance balance of the mixed battery pack is remarkably improved, the overall service life is prolonged, and the method is suitable for real-time monitoring of a battery management system.

The invention discloses a dark tea extract intelligent production control system, and relates to the technical field of plant extraction, the system comprises a data acquisition and monitoring module, an intelligent control module, an analysis optimization module and a visualization module; the data acquisition and monitoring module acquires environmental data and eurotium cristatum growth data in real time; the intelligent control module automatically adjusts the temperature, the humidity and the oxygen concentration in the fermentation process based on the environmental data and the eurotium cristatum growth data; the analysis and optimization module predicts and adjusts production conditions in real time by using a neural network and an optimization model; the visualization module provides real-time monitoring and remote operation functions through a graphical user interface, so that an operator can monitor the state of the system in real time. According to the method, the growth environment of eurotium cristatum can be accurately controlled, the fermentation and extraction processes are optimized, the quality consistency and production efficiency of dark green tea are improved, and the problems of quality fluctuation and inaccurate control in a traditional production method are solved.

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 ecological environment protection, in particular to a mangrove forest carbon sink monitoring and metering method based on an unmanned aerial vehicle, a radar and an AI technology, and the method comprises the steps: 1, obtaining the laser radar data of a mangrove forest through a laser radar carried by the unmanned aerial vehicle; step 2, acquiring elevation data in a mangrove forest vegetation layer area, and obtaining point cloud data after topographic error correction; 3, obtaining a multispectral image of the mangrove forest, and obtaining a multispectral data matrix; 4, identifying forest growth data features, constructing a mangrove forest carbon sink prediction model, and predicting the mangrove forest carbon sink amount; step 5, marking the image region with the NDVI value higher than a preset NDVI threshold value as a blade over-dense region; and according to the area of the overdense leaf region and the multispectral data matrix, calculating a light depression factor by using a photosynthetic depression factor formula, determining the carbon sink deviation of the overdense leaf region by using a regional carbon sink deviation formula, and obtaining a real carbon sink value of the mangrove forest according to a carbon sink calculated value obtained by prediction.

The invention discloses a nutrition recipe intelligent generation system and method, and belongs to the technical field of nutrition information processing and intelligent recommendation. The system is suitable for individual nutrition management of individuals of all ages, and particularly, optimization design is carried out for infant stages of 0-72 months. The system intelligently generates a personalized nutrition recipe based on individual basic information, growth indexes, nutrition standards, available food materials, user preferences and feedback and other multi-dimensional data, and supports nutrition intake evaluation and dynamic optimization. The system comprises core modules of data input, nutritional requirement calculation, recipe generation, nutrition analysis and interactive display and the like, and expands and supports functions of sensitization screening, regional and seasonal adaptation, work and rest linkage, family sharing and the like. Through fusion of individual growth data and environmental conditions, the system realizes scientific and high-adaptability nutrition recommendation, improves feeding convenience and intervention accuracy, and has a wide application prospect.

The invention discloses an intelligent growth monitoring and analysis method and system for mariculture, and the method comprises the following steps: firstly, collecting breeding environment data in real time through a first sensor group; and acquiring growth data of the breeding object through the second sensor group. Then, the cultivation environment data and the growth data are transmitted to a cloud platform by using a data acquisition system, and the data are preprocessed by the cloud platform to generate a standardized data model; based on the standardized data, a deep learning algorithm is used for analyzing the growth trend of the breeding object and predicting the growth change in a period of time in the future. Through combining the breeding environment and the growth data, the cloud platform performs risk assessment, generates early warning information in real time, and identifies potential risk factors. According to the early warning information, the cloud platform automatically adjusts breeding environment parameters and management measures, and healthy growth of breeding objects is ensured. According to the method, intelligent decision support is provided for breeding management, and the breeding efficiency and the growth quality of bred objects are improved.

The invention relates to the technical field of automatic feed distribution, in particular to an automatic feed production regulation and control method and system.The method comprises the following steps that healthy and growth rate data are collected and input into a data stream, the number of dynamic Bayesian network nodes and a connection mode are adjusted, real-time data are analyzed, and the learning rate and the transition probability are adjusted; and a prediction error matching feedback mechanism is established, key indexes are extracted, a distribution instruction is generated through a fuzzy logic controller, the feed ratio is adjusted to balance cost nutrition and environmental influence, and an optimal configuration result is obtained. According to the method, health and growth data are comprehensively utilized, feed feeding is more personalized and accurate, a feedback mechanism is analyzed and optimized in real time, it is ensured that the feed ratio responds to specific nutritional requirements of animals in real time, accordingly, resource waste is reduced, healthy growth of the animals is enhanced, feeding instructions are finely regulated and controlled through fuzzy logic, and the feeding efficiency is improved. The method adapts to different stages of animal growth, and a particle swarm optimization algorithm is adopted to balance the cost, nutrition and environmental influence.

The invention discloses an infrastructure digital twinborn dynamic registration method, system and device based on a deep neural network, a medium and a product, and relates to the field of digital twinborn registration, and the method comprises the steps: building a digital twinborn model based on the project design of an infrastructure project; predicting growth data of the digital twin model; based on the predicted growth data, using the trained deep neural network to predict a compensation error; and compensating the digital twin model based on the compensation error to realize dynamic registration of the digital twin model. According to the method, the error between the digital twinborn model and the physical entity can be reduced, and the fitting precision of the digital twinborn model and the actual construction state is improved.

The invention belongs to the technical field of monitoring of a hot spring algae culture process, and discloses an intelligent monitoring and analyzing method for growth data of hot spring algae culture. According to the invention, by identifying the abnormal growth state and dynamically adjusting the spectrum composition and intensity of the multiband illumination sensor array when the abnormal growth state is identified, spectrum-growth state closed-loop control is realized, the accuracy and effectiveness of illumination control are improved, proper illumination is provided for the hot spring algae in time, and the quality of the hot spring algae is improved. The photosynthesis and normal growth are guaranteed, the culture efficiency and quality are effectively improved, and the loss caused by abnormal growth is reduced. According to the method, the growth cycle is identified based on the relative growth rate and the cell division rate, so that the growth change can be more sensitively captured, the exact growth stage of the hot spring algae can be accurately grasped, a key support is provided for subsequent accurate regulation and control of culture conditions, and the scientificity and effectiveness of culture are greatly improved.

The invention relates to the technical field of concrete curing, in particular to an intelligent control method and system for a precast concrete component curing kiln, and the method comprises the steps: obtaining historical and real-time environment parameters of the curing kiln, generating monitoring data with an identifier in combination with a component type and a current process stage, and matching a corresponding preset temperature and humidity regulation and control model. And optimizing the model by using a historical temperature and humidity response sample and component strength increase data to generate a personalized target temperature and humidity regulation and control model. And analyzing real-time parameters based on the model, outputting a control instruction containing a target temperature rise rate, a humidity threshold value and heat preservation time, and converting the control instruction into a kiln body adjustment control signal according to a dynamically adjusted execution rule. And finally, sending to an execution terminal to realize cooperative regulation and control of heating, humidification, ventilation and heat preservation systems. According to the method, multi-source data and process characteristics are fused, the adaptive learning ability is achieved, the maintenance environment can be accurately regulated and controlled, and intelligent, refined and efficient operation of the maintenance process is achieved.

The invention relates to the technical field of automobile batteries, in particular to a battery cell matching system with improved cycle life, comprising: a battery cell parameter acquisition module for acquiring initial capacity, internal resistance value and voltage curve of a to-be-matched battery cell for a new energy automobile; the dynamic attenuation modeling module is used for establishing a dynamic attenuation model according to capacity attenuation and internal resistance growth data in the battery cell recycling process; the matching optimization module is used for combining the output of the dynamic attenuation model with the current matching strategy and adjusting the battery cell group in the matching scheme to obtain a battery pack; the high-temperature correction module is used for dynamically adjusting a matching correction value of a high-temperature cycle attenuation coefficient according to the attenuation performance of the battery cells in the battery pack under high-temperature cycle; and the control module is used for determining a screening threshold value of the static parameters of the battery cells according to the maximum difference quantity of the initial capacities of the plurality of battery cells in the same battery pack, or determining a dynamic characteristic weight coefficient according to the internal resistance difference of the plurality of battery cells in the battery pack. According to the invention, the stability of battery cell matching is improved.

The invention discloses a corn drought resistance identification device and method, and belongs to the technical field of agricultural scientific research. According to the method, the problems of inaccurate environment control, long identification period, poor repeatability and the like of an existing method are solved, the drought resistance index of the corn is calculated through the random forest model, the drought resistance grade of the corn is divided, and the drought resistance index threshold value is set; the drought resistance indexes of the corn in different growth periods are analyzed, the drought resistance index threshold value is dynamically adjusted according to the corn growth data monitored in real time, and the drought resistance of the corn in different growth periods can be reflected more accurately; feature importance scores are calculated through a random forest model, feature variables are sorted, and it can be determined that certain features make the greatest contribution to the drought resistance index; based on feature importance analysis, suggestions on corn drought resistance research and breeding are provided, researchers can be helped to optimize breeding strategies, errors caused by subjective judgment are reduced, and the breeding efficiency of drought-resistant varieties is improved.

The invention discloses a collaborative management method for fishing and light complementation of a photovoltaic station, and relates to the technical field of photovoltaic station operation and maintenance. The method comprises the following steps: collecting data; screening important features by adopting a recursive feature selection method based on a random forest to obtain growth data, and calculating a feature weight of each feature by utilizing a TreeSHAP method; calculating the growth deviation degree corresponding to each feature in the growth data, calculating the aquatic organism efficacy index by combining with the feature weight, and drawing an efficacy curve; calculating a growth trend factor of the efficiency curve based on a least square method of a sliding window, and determining growth trend stability; the inertia weight of the particle swarm optimization algorithm is adjusted by using the growth trend stability, and the optimal state of the photovoltaic panel, namely the optimal photovoltaic panel inclination angle and the optimal reflective film angle, is obtained; photovoltaic panel power generation is predicted through the optimal photovoltaic panel inclination angle and the optimal reflective film angle, the optimal power generation amount of the photovoltaic station is obtained, the photovoltaic station is cooperatively managed according to the optimal photovoltaic panel inclination angle and the optimal reflective film angle, and the operation and maintenance state of the photovoltaic station is optimal.

The invention relates to the technical field of forestry intelligent monitoring, and particularly discloses an intelligent monitoring system for the growth condition of afforestation and greening nursery stocks based on deep learning, which is characterized in that multi-modal growth data of the nursery stocks are acquired through a multi-spectral imaging sensor, a three-dimensional laser scanning sensor and an environment monitoring sensor, and a standardized data set is formed through space-time alignment processing; extracting morphological structure and spectral response features through spatial domain and frequency domain parallel analysis, and generating multi-dimensional feature representation through cross-modal fusion; converting the parameters into growth state parameters by utilizing a feature recombination and space mapping technology; identifying an abnormal growth mode through time sequence dynamic analysis; and finally, adaptively adjusting the working parameters of the sensor according to the abnormal type to form closed-loop monitoring. The problem that an existing monitoring system cannot autonomously optimize a monitoring strategy according to the abnormal state is solved, and accurate monitoring and intelligent regulation and control of the nursery stock growth condition are achieved.

The invention discloses an intelligent henhouse management system and method based on the Internet of Things, and the method comprises the steps: S1, obtaining the individual parameters of each broiler chicken and the environmental parameters of each column, dividing the columns with similar feeding time into one group, and constructing a digital twinborn model; the method comprises the following steps: constructing a database containing abnormal broiler chicken caused by environment and non-environment correlation through abnormal cases in broiler chicken breeding in combination with a machine learning interpolation method; according to the collected data, constructing a normal growth curve prediction model, and outputting an expected normal growth curve of each broiler chicken in the current environment; monitoring broiler growth conditions in real time, comparing actual growth data with an expected normal growth curve, marking an abnormal column, and positioning abnormal individual broilers; according to the image data of the abnormal individual broiler chicken, the digital twinborn model and the abnormal database are combined, abnormal reasons are analyzed, abnormal types are judged, and a targeted solution is pushed. According to the method, the accuracy of anomaly detection is improved, and full-closed-loop management of dynamic sensing, accurate diagnosis and self-adaptive regulation and control is also realized.

The invention provides a multi-crop interplanting planting scheme determination method and system based on big data analysis, and relates to the technical field of digital agriculture, and the method comprises the steps: obtaining first information which comprises soil data, meteorological data, crop growth data and field management data of pepper-potato planting; screening the crop growth key influence factors based on the first information, performing dimension reduction processing on the screened data, and establishing a pepper-potato crop interaction relation model based on the pepper-potato growth data feature set after dimension reduction; optimizing a preset spatial distribution mode of a plurality of planting schemes based on the crop interaction relationship model, and deducing to obtain a preliminary planting scheme of pepper-potato interplanting; a growth dynamic change model is established based on the preliminary planting scheme of pepper-potato interplanting, and a crop growth curve is optimized and adjusted. The crop yield and quality can be improved, the water and fertilizer utilization efficiency is optimized, and the expandability of the planting mode is enhanced.

The invention relates to a road bougainvillea speetabilis intelligent fertilization supervision method and system, and the method comprises the steps: enabling collected multispectral image data and thermal imaging image data to be precisely matched with GPS coordinates, enabling soil component parameters to be aligned with the corresponding multispectral image data and thermal imaging image data, and forming a spatiotemporal data set, performing feature extraction on the multispectral image by adopting a convolutional neural network, constructing a growth attitude comprehensive analysis model, comparing a result output by the growth attitude comprehensive analysis model, namely a current growth state quantized value, with a preset standard based on historical growth data and a bougainvillea speetabilis growth rule, and calculating the missing amount of related fertilizers, a precise fertilization formula and a fertilization time node are determined by combining the current fertility condition of the soil, a control instruction is generated according to the fertilization formula and the time node, and the fertilization robot executes fertilization operation based on the control instruction and a preset path. The method has the effects of improving the fertilizer utilization rate, reducing the labor cost and improving the maintenance efficiency.

The invention discloses a corn plant multi-parameter measurement system, which comprises a multi-mode sensing unit used for synchronously acquiring corn plant height, corn cob size and corn cob internal structure data; the ground intelligent mobile platform carries the multi-mode sensing unit and has autonomous navigation and obstacle avoidance capabilities; the edge calculation unit integrates a machine learning model to process multi-source data in real time; and the digital twinborn visualization module is used for generating a farmland three-dimensional growth model and an AR auxiliary diagnosis interface. The system is based on a ground intelligent mobile platform, adopts a multi-mode sensing fusion technology, can synchronously measure the plant height, the size of a corn cob, the internal structure of a stalk and grain components in a non-contact and high-precision manner, and can effectively solve the technical problems existing in manual measurement. The invention further discloses a measurement method, according to the method, the multi-modal sensing data fusion technology is adopted in the corn multi-parameter measurement process for the first time, and the accuracy and efficiency of corn growth data measurement can be remarkably improved.

The invention belongs to the technical field of irrigation disaster monitoring, and provides an irrigation disaster monitoring method and system based on remote sensing monitoring, and the method comprises the steps: carrying out the quantitative analysis of the growth data difference between an irrigation region and a surrounding region through a neighborhood matrix method, determining a suspected drought disaster region, and constructing a drought evaluation model of the suspected drought disaster region; determining whether the suspected drought disaster area is a drought disaster area; the irrigation area disaster monitoring efficiency is improved, the irrigation area disaster monitoring resource cost is saved, the drought time sequence is processed and analyzed by combining the self-correlation function and the partial self-correlation function, whether the drought occurrence of the drought disaster area has periodic changes or not is judged, the monitoring system of the drought disaster area is updated according to the periodic changes, and the drought disaster monitoring efficiency is improved. The irrigation area drought disaster monitoring efficiency is improved, the prediction model is constructed according to the analysis results of the autocorrelation function and the partial autocorrelation function, irrigation area drought prediction is achieved, irrigation area disaster comprehensive monitoring is achieved, and monitoring comprehensiveness is improved.

The invention is suitable for the technical field of three-dimensional planting management, and particularly relates to a three-dimensional planting field management method and system, and the method comprises the steps: dividing a three-dimensional agricultural planting region, cutting the planting region into a plurality of blocks, collecting the growth data of all agricultural plants through video monitoring equipment disposed in the planting region in advance, and storing the collected growth data in a database; identifying a risk point location, selecting an infrared anchor point, starting a laser generator integrated in the video monitoring equipment in advance, and projecting infrared laser to the infrared anchor point; sending a working instruction to the inspection equipment, shooting a snapshot of the planting area, reading the projection positions of the infrared anchor points, integrating all the infrared anchor points, generating an anchor point group, and positioning the real-time position of the inspection equipment when the real-time position coincides with the projection positions. By determining the management report, defective plants in three-dimensional planting can be found in time, health management of agricultural plants is enhanced, and intelligence and refinement of agricultural production are effectively improved.

The invention discloses an AI-based rice planting environment data informatization management system, and relates to the technical field of agricultural informatization. The method comprises the steps that an environment data acquisition module obtains multi-dimensional space-time marked environment parameters; the disturbance modeling module is used for analyzing environment change, identifying a disturbance structure and calculating a sudden change value; the growth stage parameter module is used for constructing a stage characteristic parameter set; the growth state modeling module is used for generating growth state data in combination with disturbance and a growth stage; the strategy generation module formulates a resource regulation and control strategy based on the growth state and the feedback information; the resource coupling analysis module is used for identifying coupling and interference relations among resources; the strategy constraint module is used for adjusting a resource strategy to generate a control strategy; and the control execution feedback module is used for executing the regulation and control instruction and feeding back growth data. Through high-precision space-time environment data fusion, deep coupling analysis of environment disturbance and growth states and resource regulation and control closed-loop optimization management, intelligent and precise monitoring and regulation and control of a rice planting environment are realized.

The invention discloses an intelligent prediction method for corn yield, and the method comprises the steps: obtaining a multi-layer feedforward neural network model through training, training the multi-layer feedforward neural network model through a genetic method, and enabling an obtained prediction model to have higher prediction precision when predicting the corn yield; according to the method, by assimilating vegetation net primary productivity data having a great influence on the corn yield, more accurate prediction data of the corn growth state can be obtained, and the network model is updated based on the prediction data, so that the model better adapts to the current corn growth data, and the accuracy of corn yield prediction is improved; according to the method, the prediction operation speed is increased through the multi-layer feedforward neural network model, and the corn yield can be predicted more scientifically and reasonably, so that corn production and management measures can be accurately adjusted in time more conveniently.

The invention discloses a tree growth prediction method and system based on a TCN time sequence convolution model, and the method comprises the steps: collecting the historical growth data of trees in a power transmission line corridor, and carrying out the data preprocessing; constructing a neural network model based on the TCN; dividing the preprocessed data into a training set and a test set, and training the TCN model by using the data of the training set; performing performance evaluation on the trained TCN model by using the test set; and deploying the trained TCN model in an actual application scene, and performing real-time growth prediction on the target tree species in the power transmission line corridor area. According to the method, through the model based on the time convolutional network (TCN), the growth condition of the tree species in the power transmission line corridor is accurately predicted. The TCN model captures the sequential relationship in the time sequence by using the convolution operation, thereby avoiding the problem of gradient disappearance or explosion easily occurring when the traditional recurrent neural network processes the long sequence, and improving the prediction accuracy.

The invention discloses a water-saving irrigation method and system for traditional Chinese medicinal materials, and relates to the technical field of water-saving irrigation, by deploying sensors, collecting soil data and traditional Chinese medicinal plant growth data, and utilizing a source sensor to realize accurate positioning of the positions of other sensors; by constructing a sensor ad hoc network, collected soil data are intelligently analyzed to judge the accuracy and effectiveness of the soil data; based on the obtained effective sensor data, designing a water-saving irrigation control scheme by using a neural network model, and optimizing a sensor ad hoc network and a corresponding irrigation control scheme according to the growth condition of the traditional Chinese medicine plants; by analyzing the effectiveness of sensor data, accurate environment data of the traditional Chinese medicine plants can be obtained, and intelligent and accurate water-saving irrigation for the traditional Chinese medicine plants is achieved.

The invention discloses a single plant afforestation carbon sink metering and monitoring method based on machine vision recognition, and belongs to the technical field of geographic spatial data processing, and the method comprises the steps: S10, obtaining spatial distribution data of a preset monitoring sample plot, the spatial distribution data comprising forest types and forest distribution; monitoring deployment is carried out in a preset monitoring sample plot, and the monitoring deployment mode comprises the step of arranging a plurality of observation cameras in the preset monitoring sample plot; and step S20, video picture data of the forest is acquired based on the arranged observation camera so as to acquire growth data information of the forest, and the growth data information comprises the height, the diameter and the coverage of the forest. According to the invention, through a machine vision identification technology, effective monitoring of forest tree growth data is realized, accuracy and efficiency of carbon sink metering are improved, and through periodic recording and analysis of growth data information, a change rule of forest tree growth can be found in time, and a scientific basis is provided for dynamic monitoring of carbon sink.

The invention discloses an intelligent agricultural management method and system based on computer analysis, and particularly relates to the technical field of agricultural information, and the method comprises the following steps: obtaining farmland resource information, obtaining and processing a farmland resource information set, obtaining an environment data set and a growth data set according to a processing result, and then carrying out the environment analysis and growth analysis, a growth deviation combination and an environment deviation combination are obtained, a crop classification combination set of the target farmland is further obtained, category code numbers are generated, corresponding abnormal type early warning signals and comprehensive abnormal levels are generated according to the category code numbers and a preset early warning strategy and summarized, and an early warning combination set is obtained; by monitoring and analyzing the environment condition and the crop growth state of the farmland in real time, problems in agricultural production can be found and solved in time, various potential problems and risk factors in the farmland can be identified, and basis data of a precise management strategy can be provided for agricultural production.

The invention discloses a corn and wheat water and fertilizer adaptive control method and system, and relates to the technical field of water and fertilizer control. S2, disease monitoring and diagnosis; s3, nutrient demand analysis; s4, adjusting the irrigation amount; and S5, growth data feedback. According to the invention, through disease monitoring and diagnosis, whether the crops in the planting area have diseases and the types and severity of the diseases can be found in time, through nutrient demand analysis, the crop nutrient demands of the disease area and the non-disease area can be obtained respectively, and according to the disease occurrence condition and the nutrient demands of the crops, the crop quality is improved. Real-time partition accurate regulation and control are achieved, more accurate self-adaptive water and fertilizer irrigation amount regulation is achieved, a suitable growth environment is provided for crops, the influence of diseases on crop growth is relieved, recovery and growth of the crops are guaranteed, and therefore the yield and quality of the crops are improved.

The invention relates to the technical field of plant monitoring, in particular to a tree growth intelligent monitoring and lesion early warning system which comprises a data acquisition module, a growth state analysis module, a lesion risk assessment module, an environmental adaptability assessment module and a comprehensive early warning analysis module. Wherein the data acquisition module is used for acquiring tree growth data and environment data in real time; the growth state analysis module is used for evaluating the current growth state of the tree; the lesion risk assessment module is used for detecting whether the trees have lesion symptoms or not; the environment adaptability evaluation module is used for judging whether the tree faces an environment adaptability problem or not; and the comprehensive early warning analysis module is used for comprehensively judging whether the trees have health threats or not. According to the invention, through comprehensive real-time collected tree growth data, environment data and lesion image analysis, early warning and intelligent management of tree health are realized, the monitoring efficiency is significantly improved, and potential growth problems and lesion risks are effectively prevented.