89 results about "Greenhouse crops" patented technology

The invention relates to the technical field of intelligent agriculture, in particular to an intelligent greenhouse crop growth state monitoring and management system based on the Internet of Things, which comprises a multi-source sensing module, a space-time calibration module, a physiological parameter calculation module, a growth coupling analysis module, a regulation and control decision module and a closed-loop execution module. Wherein the multi-source sensing module is used for acquiring canopy spectral data, stem pressure fluctuation data and root conductivity data; the space-time calibration module is used for carrying out space-time alignment; the physiological parameter calculation module is used for calculating effective root activity in combination with the conductivity data of the root system; the growth coupling analysis module is used for dynamically calculating a growth coordination index value; the regulation and control decision module is used for generating a regulation and control instruction set; and the closed-loop execution module is used for dynamically correcting parameters of the regulation and control instruction. According to the invention, through multi-source sensing, dynamic calculation and closed-loop feedback mechanisms, precise environment regulation and control based on the real-time growth state of crops are realized, and the resource utilization efficiency is improved.

The invention relates to the technical field of temperature control, in particular to a temperature control method and system for a crop greenhouse cultivation environment, and the method comprises the following steps: calculating the growth rates of crops under various temperature conditions based on the variety information of greenhouse crops, recognizing ideal temperature ranges of a plurality of growth stages, and calculating the temperature of the greenhouse crops according to the ideal temperature ranges; and a crop temperature management model is constructed by integrating ideal temperature range data of a plurality of stages. According to the invention, through calculation of growth rates of crops under various temperature conditions, identification of temperature requirements of the crops in multiple growth stages, calibration of control parameters of a temperature adjusting device, improvement of adaptability and efficiency of a temperature control system, and analysis of an illumination intensity change trend, prediction of internal temperature of a greenhouse is realized. The temperature regulation meets the actual illumination condition, energy is saved, the growth environment is optimized, it is ensured that the environment in the greenhouse is kept in the optimal state, and the market competitiveness of crops and the economic benefits of agricultural producers are improved.

The invention relates to the technical field of crop transpiration calculation, and particularly discloses a greenhouse crop transpiration calculation method based on an improved Penman-Monteith model, which comprises the following steps of: researching convection types in a greenhouse under different ventilation by setting a ventilation mode of the greenhouse; an improved calculation method for an aerodynamic resistance parameter ra and a canopy resistance parameter rc is obtained according to a convection type in combination with meteorological data and plant growth data in a greenhouse, and the improved calculation method is combined with a Penman-Monteith model applied to greenhouse crop ET simulation to obtain an improved greenhouse crop transpiration calculation model. After verification, the model is proved to have relatively high simulation precision; the improved Penman-Monteih greenhouse crop transpiration calculation model is used for predicting the transpiration of the greenhouse tomatoes in different growth periods and comparing the transpiration with the measured value, the influence degree of ventilation on the water consumption of the greenhouse crops in different growth periods can be determined, and the method has practical significance in guiding natural ventilation management of the greenhouse tomatoes in different growth periods.

The utility model discloses an agricultural greenhouse inspection robot which comprises a walking mechanism, a lifting mechanism, a box body and monitors, the walking mechanism is connected with the lifting mechanism through a steel wire rope, the lifting mechanism is installed on the box body, and the monitors are installed on the front portion and the rear portion of the box body. The inspection robot can inspect line by line according to the planting line spacing of crops, the robot can ascend and descend in a large range, the growth condition of the crops can be detected at a short distance, the problems of long inspection distance, low accuracy and the like in the prior art are solved, and the inspection robot is a key technology for short-distance detection of the greenhouse crops.

The invention relates to the field of greenhouse planting, and discloses a method for calculating the irrigation volume of greenhouse tomato series harvest according to the liquid return volume and the weather condition, and the method comprises the following steps: data collection: collecting greenhouse environment data through monitoring equipment, including illumination cumulant, air temperature, relative humidity and rainfall, and matrix humidity data, comprise the maximum weight, the minimum weight and the liquid return amount of the matrix; and theoretical irrigation volume calculation: calculating the theoretical irrigation volume for tomato series harvesting based on the illumination cumulant and the environmental factors. According to the method, the multi-dimensional data is combined with the dynamic adjustment strategy, so that precise irrigation is realized, and the resource utilization efficiency is optimized; through a real-time feedback and optimization mechanism, the environment change is dynamically adapted, and the response capability of the irrigation system is improved; the crop yield and quality are guaranteed; water and fertilizer integrated expansion is supported, and the device adapts to various greenhouse crops; and through abnormity monitoring and long-term optimization, the reliability and adaptability of the system are ensured.

The invention provides a greenhouse production model construction method and device based on mechanism model mapping. The method comprises the following steps: acquiring initial crop state data, initial greenhouse environment state data, a greenhouse control input sequence and an outdoor environment state sequence of a greenhouse; inputting the initial crop state data, the initial greenhouse environment state data, a greenhouse control input sequence and an outdoor environment state sequence into a greenhouse crop production system mechanism model to obtain a crop state sequence and a greenhouse environment state sequence, large-scale acquisition of crop growth data and greenhouse environment data in the greenhouse is realized in a low-cost mode; a data set is constructed based on the crop state sequence, the greenhouse environment state sequence, the greenhouse control input sequence and the outdoor environment state sequence to train a neural network model, and a greenhouse crop production system data model for predicting the crop state and the greenhouse environment state at the next moment is obtained. Accurate construction of the greenhouse crop production system data model is realized.

The invention relates to a greenhouse automatic vine releasing robot and a vine releasing method. The vine releasing robot comprises a movable chassis, a lifting mechanism, a control cabinet, a front mechanical arm, a rear mechanical arm, an electric clamping jaw, a light curtain sensor and a photoelectric switch. The control cabinet and the lifting mechanism are installed on the movable chassis, and the two mechanical arms are fixed to the surface of a top platform of the lifting mechanism. Electric clamping jaws are installed at the tail ends of the two mechanical arms, a photoelectric switch is arranged at the top end of a main shaft of the front mechanical arm, and a light curtain sensor is installed on the tail-end clamping jaw. The greenhouse vine releasing device is suitable for greenhouse crops in a hook vine releasing mode, the height of a vine hanging steel wire is detected through a photoelectric switch above the lifting mechanism before work is started, the vine releasing hook is accurately positioned through a light curtain sensor, the vine releasing hook is taken and placed through a mechanical arm, automation of greenhouse vine releasing work is achieved, and the work efficiency is improved. And the working efficiency of greenhouse vine releasing operation is improved.

The invention provides a greenhouse crop habitat optimization method, system and device and a storage medium, and belongs to the technical field of intelligent agriculture, and the method comprises the steps: continuously obtaining greenhouse internal environment multi-element data, greenhouse external meteorological data, crop root zone current soil moisture data and device state data; predicting greenhouse internal environment multi-element data and soil moisture content of different soil layers in a future time period based on the data; predicting photosynthetic physiological habitat conditions of crops in the greenhouse in the future time period based on the multi-element data of the internal environment of the greenhouse in the future time period and soil moisture contents of different soil layers in the crop root zone; and taking the predicted habitat condition as an optimization target, performing optimization decision on current irrigation and environment multi-element regulation and control, generating a control scheme, further generating a regulation and control signal, and controlling greenhouse terminal equipment to operate according to the regulation and control signal. According to the invention, the accuracy, real-time performance, collaboration and intelligent level of greenhouse environment regulation and irrigation management are obviously improved, and the crop yield, quality and resource utilization efficiency are further improved.

The invention relates to the field of agricultural irrigation, and particularly discloses an agricultural greenhouse water and fertilizer centralized drip irrigation facility which comprises a greenhouse framework, a liquid supply mechanism for providing water and fertilizer liquid is arranged on the greenhouse framework, a supporting mechanism is hung on the greenhouse framework, and a connecting block connected with the liquid supply mechanism is arranged at the output end of the supporting mechanism. An irrigation assembly communicated with the liquid supply mechanism is arranged on the connecting block; a driving mechanism is arranged on the connecting block, and a connecting assembly is arranged at the output end of the driving mechanism. Root drip irrigation and leaf surface spray irrigation of crops are effectively combined, the function of switching a drip irrigation mode and a spray irrigation mode is achieved, the adaptive capacity of the device to the environment is improved, the irrigation efficiency is greatly improved, nutrient supply to the crops is facilitated, healthy growth of the crops is facilitated, the automatic adjusting capacity of the device is improved, and the device is suitable for popularization and application. And a worker can easily manage greenhouse crops, so that the cost of an enterprise is reduced.

The present invention provides an intelligent adjustment method and system for the growth environment of greenhouse crops, which relates to the technical field of intelligent adjustment and includes: obtaining the current temperature parameters, humidity parameters and light parameters of greenhouse crops as the current environmental parameter set, obtaining the adjustment feasible region according to the environmental parameter set within a preset historical time range, constructing a fitness function according to cost and growth conditions, setting constraint conditions for environmental parameter optimization to obtain the optimal environmental parameter set, wherein, based on image processing recognition, obtaining the growth conditions of greenhouse crops, and using the optimal environmental parameter set to adjust the environmental parameters in the growth environment. The present invention solves the technical problems existing in the traditional method for adjusting the growth environment of greenhouse crops, such as relying on manual adjustment, being easily affected by subjective factors, resulting in low efficiency and high cost, and at the same time being unable to meet the development needs of modern agriculture for high efficiency and intelligence.

The invention relates to a facility crop photosynthetic potential prediction method and system based on spectral characteristics, and the method comprises the steps: obtaining sample data through setting different illumination gradient culture environments, carrying out the conversion preprocessing of data through a standard normal variable, extracting a characteristic wavelength through employing a competitive adaptive reweighting algorithm and a continuous projection method, and carrying out the prediction of the photosynthetic potential of a facility crop. And establishing a prediction model by using a regression type support vector machine algorithm to predict the photosynthetic potential of the crops. The prediction system comprises a main control processing unit, a driving light source unit, a reflected light detection unit, a power supply unit, a communication unit, a control software unit, a display unit and the like. The main control processing unit controls operation of all parts and processes data, the light source unit is driven to provide detection light, the reflected light detection unit detects light intensity and converts and processes signals, the communication unit uploads data, and the control software and the display unit calculate and display results. According to the method, the photosynthetic potential of facility crops can be rapidly and accurately evaluated at low cost, a scientific basis is provided for agricultural production, and crop growth state monitoring is facilitated.

The invention discloses a greenhouse closed-loop energy-saving heat supply system based on biological waste fermentation, and relates to the crossing field of agricultural intelligent planting and waste resource utilization, the system comprises the following components: a fermentation unit, a biogas collection and utilization module, a waste heat and fertilizer recovery module and an intelligent management and control system; biogas is generated through fermentation of biological waste, heat generated by combustion of the biogas is used for supplying heat to a greenhouse and a living room, meanwhile, waste heat generated in the fermentation process is recycled for auxiliary heat supply, and the energy utilization efficiency is remarkably improved; according to the invention, residues after fermentation can be converted into a decomposed organic fertilizer for greenhouse crops to use, so that cyclic utilization of resources is realized, the use amount of chemical fertilizer is reduced, the production cost is reduced, and meanwhile, environmental protection is facilitated.

1. The name of the design product: cyclone air cooler. 2. The use of the design product: to release to realize yield increase and quality improvement for greenhouse crops. 3. The design points of the design product: in the perspective view. 4. The picture or photo that best indicates the design points: perspective view. 5. The bottom of the design product is not easy to see or not visible during use, omit the top view.

The invention relates to the field of greenhouse operation management and control, and provides a greenhouse operation management and control method, device and equipment, a medium and a computer program product, and the method comprises the steps: receiving crop visual information and environment information sent by greenhouse monitoring equipment; inputting the crop visual information and the environment information into a greenhouse crop planting knowledge graph to obtain a greenhouse operation management and control macroscopic decision; inputting the greenhouse operation management and control macroscopic decision into a greenhouse model to obtain an operation decision result output by the greenhouse model; the greenhouse model is obtained based on historical greenhouse operation data training. On the basis of the received crop visual information and environment information, the crop visual information and the environment information are analyzed through the established greenhouse crop planting knowledge graph, a greenhouse operation management and control macroscopic decision is obtained, the greenhouse operation management and control macroscopic decision is further combined with a greenhouse model obtained based on historical greenhouse operation data training, and a greenhouse operation management and control result is obtained. Therefore, the accuracy and efficiency of greenhouse operation management and control are improved.

The invention belongs to the technical field of agricultural intellectualization, and provides a multi-angle and multi-strategy-based greenhouse crop production environment monitoring control method, which comprises the following steps: collecting environment and soil crop data to construct a matrix and pre-processing the matrix; extracting time sequence characteristics by adopting Res-BiLSTM, and constructing a yield prediction model in combination with the improved XGBoost; integrating multi-dimensional features to construct a feature matrix, completing standardization, and determining an optimal cluster number and a cluster affiliation label by adopting an improved K-means clustering algorithm; for each cluster, nonlinear features are extracted through improved SVR, and a temperature and humidity prediction model is constructed by combining with a GBDT model fitting residual error; the key parameters of the model are optimized by adopting Chebyshev chaotic mapping for the CEO algorithm; and a layered cooperative regulation and control mechanism of MPC global optimization and feedforward PID real-time correction is adopted. According to the control method, multi-source data acquisition, precise prediction model construction and a layered cooperative regulation mechanism are integrated, precise monitoring and dynamic regulation of the greenhouse environment are realized, and the crop yield and quality stability are improved.

The invention relates to the technical field of data analysis and monitoring, in particular to a greenhouse crop disease and pest comprehensive monitoring method and management system based on big data, and the method specifically comprises the steps: modeling a digital twinborn model of a greenhouse, and measuring to obtain a stomatal conductance coefficient and a fluorescence parameter of crops; calculating to obtain a first leaf health threat value and a second leaf health threat value, and comprehensively judging the pest influence degree of the pest monitoring area according to the second leaf health threat value; and the first leaf health threat value and the second leaf health threat value of each pest monitoring area in the greenhouse are visualized to the greenhouse digital twinborn model, so that the problems that greenhouse crop pest monitoring is not accurate enough, the real-time performance is insufficient and the information integration is not comprehensive in the prior art are solved.

The invention relates to the technical field of crop patrol, and discloses a greenhouse crop patrol robot system comprising a patrol robot configured to cruise along a preset path in a greenhouse and collect crop images and environmental parameters; the cloud server is configured to receive the data uploaded by the patrol robot and execute image recognition and environment data analysis; the remote management terminal is configured to display the data and early warning information processed by the cloud server in real time; and the patrol robot, the cloud server and the remote management terminal are interconnected through the wireless communication module to form a closed-loop management architecture. According to the patrol robot system for the greenhouse crops, automatic, high-precision and datamation closed-loop management and control are achieved, the efficiency and yield of greenhouse planting can be remarkably improved, and powerful technical support is provided for development of intelligent agriculture.

1. The name of the design product: irrigator. 2. The use of the design product: used as a walkable irrigator installed in a greenhouse, for spraying greenhouse crops. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The invention discloses a greenhouse crop disease image segmentation method and system, relates to the technical field of image processing of ground scenes, solves the problems of low segmentation precision and the like in agricultural scenes with complex disease spot forms and the like in the prior art, and improves the segmentation precision, the processing speed and the robustness. Disease images of greenhouse crop disease plants are obtained and preprocessed; performing graying and non-local mean filtering processing on the disease image to obtain a two-dimensional histogram; taking the maximized two-dimensional Kapur entropy function as a target function, and segmenting the two-dimensional histogram by using a Kapur entropy threshold method to obtain an initial threshold set; searching the initial threshold value set by utilizing an improved rime algorithm to obtain an optimal threshold value set; and carrying out multi-threshold segmentation to obtain a disease image in which a lesion area is segmented, and completing greenhouse crop disease image segmentation. The method is suitable for processing strawberry disease images in the field of greenhouse crop disease image processing.

The invention discloses a greenhouse LED dynamic light supplementing method, system, medium and equipment, and relates to the field of greenhouse plant light supplementing. The actual light supplementing intensity of the canopy of the greenhouse crop is obtained according to the distance between the LED light supplementing module and the canopy of the greenhouse crop by using the fitted relationship between the canopy height of the greenhouse crop and the light supplementing intensity, and the difference between the illumination intensity required by the greenhouse crop and the actual light supplementing intensity is obtained according to the carved accumulated illumination quantity required by the crop. Determining the light supplementing duration of the next moment; and after sunrise, monitoring the solar radiation intensity of the canopy of the greenhouse crop, determining the actually accepted accumulated illumination quantity of the canopy of the greenhouse crop in the current time period, comparing the accumulated illumination quantity required by the growth of the greenhouse crop in the previous time period in all the rest previous time periods, and judging whether dynamic light supplement is needed or not. According to the method, greenhouse LED dynamic light supplement regulation and control are carried out according to the cumulative illumination quantity, so that the greenhouse light supplement regulation and control accuracy is improved.

The invention relates to the field of light supplement lamps for greenhouses, aims to solve the problems that light supplement lamps are not high in efficiency due to the fact that control over spectrums and illumination intensity in a light supplement lamp control system is not accurate enough, and particularly relates to a light supplement lamp set applied to greenhouse crops and a cluster control system of the light supplement lamp set. Comprising a lamp bead matrix, a digital terminal, an illumination acquisition unit, a crop analysis unit, a light supplement control unit and a cluster control unit. According to the invention, the corresponding required spectrum is obtained according to the crops in the greenhouse and the growth stage of the crops, the spectrum wavelength required to be supplemented is obtained according to the difference between the required spectrum and the natural spectrum, the light spectrum of the light supplementing lamp is adjusted according to the spectrum wavelength, the more suitable light supplementing spectrum is obtained, and the illumination of the area in the greenhouse is collected. The method comprises the following steps: performing integral calculation on illumination intensity according to a collection result to obtain an accumulated illumination quantity, predicting an illumination accumulation quantity of the whole day on the basis of the accumulated illumination quantity, and judging whether light supplementation is needed or not according to the predicted illumination accumulation quantity to ensure normal growth of plants.

The utility model belongs to the technical field of greenhouse pesticide spraying, and discloses an automatic pesticide spraying device for a greenhouse, which comprises a support, a pesticide box is arranged at one end in the support, a weighing mechanism is directly arranged on the pesticide box and the inner bottom surface of the support, and a battery is fixedly arranged at the other end in the support close to the pesticide box. A spraying device is arranged at the end, close to the water pump, of the outer portion of the support, the spraying device is communicated with the water pump, and a driving device for driving the spraying device to operate is arranged at the position, close to the spraying device, of the support; a swinging head position sensor is arranged on the inner wall of the support corresponding to the spraying device, an electric control system is arranged in the support and located above the water pump, a walking mechanism is arranged on the upper portion of the support, and a position detection sensor is further arranged on the walking mechanism; according to the utility model, full-automatic pesticide spraying can be realized in the pesticide spraying process of greenhouse crops, the pesticide dosage can be monitored, and the use effect is improved.

The present invention relates to the field of intelligent agriculture technology, and specifically relates to an intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things, which includes a multi-source perception module, a spatio-temporal calibration module, a physiological parameter calculation module, a growth coupling analysis module, a regulation decision-making module, and a closed-loop execution module; among which: The multi-source perception module: collects canopy spectral data, stem pressure fluctuation data, and root conductivity data; The spatio-temporal calibration module: is used for spatio-temporal alignment; The physiological parameter calculation module: calculates the effective root activity in combination with the root conductivity data; The growth coupling analysis module: dynamically calculates the growth coordination index value; The regulation decision-making module: is used to generate a regulation instruction set; The closed-loop execution module: is used to dynamically correct the parameters of the regulation instructions. The present invention realizes precise environmental regulation based on the real-time growth status of crops through multi-source perception, dynamic calculation, and a closed-loop feedback mechanism, and improves the resource utilization efficiency.

The invention discloses a greenhouse disease propagation monitoring and optimizing system based on a physical information neural network. The method belongs to the cross technical field of intelligent agriculture and artificial intelligence. The technical problem that a conventional mechanism model-based method and a pure data-driven machine learning method are difficult to deal with a complex and dynamically changing greenhouse environment is solved. The system comprises a multi-mode sensor module which is used for monitoring greenhouse environment data in real time, obtaining greenhouse crop images in real time and inputting the information into an edge calculation module; the edge calculation module is used for defining a multi-stage disease propagation physical model, designing a physical information neural network architecture and defining a loss function of a physical information neural network based on the multi-stage disease propagation physical model; the decision module is used for carrying out severity level division on the predicted disease degree and generating a dynamic regulation and control parameter regulation instruction according to different severity levels; and the optimization module is used for receiving the instruction for dynamically adjusting the regulation and control parameters and regulating and controlling the greenhouse environment.

The invention relates to the technical field of agricultural meteorological early warning, and discloses a greenhouse crop meteorological early warning system based on artificial intelligence. The system comprises a data acquisition module, a data analysis module, a model training module, an early warning generation module, a strategy adjustment module and a feedback verification module. The data acquisition module optimizes an acquisition process and acquires parameters such as air temperature and humidity; the data analysis module obtains an analysis result through sliding window statistics; the model training module predicts model parameters and rules through particle swarm optimization; the early warning generation module combines a genetic algorithm to calculate a meteorological risk and converts the meteorological risk into an early warning control signal; the strategy adjusting module automatically adjusts an early warning strategy through dynamic planning and a branch and bound method; and the feedback verification module verifies the data through Bayesian filtering and feeds back and corrects the data. According to the system, intelligent early warning and strategy optimization of greenhouse meteorological risks are realized, the early warning accuracy and timeliness are improved, the system adapts to dynamic changes of a greenhouse environment, and reliable guarantee is provided for growth of greenhouse crops.

The present invention relates to a method for improving the growth environment of greenhouse crops, in which soil around crops or around places in which the crops are to be planted is finely crushed by a subsoiler in a state in which the crops are planted in the soil or before the crops are planted, a plurality of through-holes deeper than the root depth of the crops are formed at regular intervals around the crops or around the places in which the crops are to be planted by using a through-hole drilling tool or an air supply pipe of the subsoiler, and water pipes connected to a water tank are inserted into the through-holes so as to reach the ground, and then supplied with water so that the water flows out from the bottom of the through-holes to form a wide water retention layer under the roots. The method can provide not only stable growth of the crops but also sufficient water, oxygen, and nutrients, and minimizes the humidity in indoor air, thereby creating the most suitable environment for crop growth.

The invention discloses an ozone atomization sprayer for preventing and treating powdery mildew of greenhouse crops, which comprises a moving mechanism, a water suction pump, a controller, a support frame, swing arms, a clamping mechanism, a spray pipe, an atomization piece and an elastic traction mechanism, the moving mechanism is provided with the water suction pump, the controller and the support frame, and the left and right parts of the support frame are respectively provided with the swing arms; clamping mechanisms are installed at the ends, away from the supporting frame, of the two swing arms, and the two swing arms can controllably drive the two clamping mechanisms to swing left and right correspondingly. When the device is used, due to the fact that the clamping mechanisms have the height difference, the two ends of the spray pipe also have the height difference, when the two swing arms swing away from each other, the clamping mechanisms can pull the spray pipe to tend to be straight, at the moment, the moving mechanism drives the supporting frame to move up and down, and meanwhile the water suction pump is controlled to suck ozone water to supply the ozone water to the spray pipe; and when the swing arms are controlled to swing close to each other, the middle parts of the spray pipes droop and are loosened.

The invention relates to the technical field of facility environment regulation and control and greenhouse energy saving, and provides a greenhouse environment comprehensive regulation and control system based on multi-source heat storage and release and a greenhouse. A greenhouse environment comprehensive regulation and control system based on multi-source heat storage and release is used for regulating the temperature in a greenhouse and comprises a wet curtain ventilation and underground heat exchange device, a solar hot water soil heat collection device, a water body passive heat storage and release device, a heat storage wall and control equipment. The greenhouse environment comprehensive regulation and control system based on multi-source heat storage and release can solve the problems that an existing greenhouse is insufficient in heat storage capacity, low in photo-thermal resource utilization efficiency, poor in temperature regulation and control effect and difficult to guarantee annual efficient and high-quality production of greenhouse crops.

This invention discloses a greenhouse environment control system and method based on crop organ phenotypic information, belonging to the field of greenhouse environment control technology. The method includes: acquiring historical datasets and weather forecast data; constructing a greenhouse environment prediction model; inputting the historical datasets and weather forecast data into the greenhouse environment prediction model to obtain greenhouse environmental data for the next 24 hours; constructing a crop growth prediction model; combining current environmental information and crop organ phenotypic growth information from the previous 7 days to predict crop organ phenotypic growth information to obtain predicted crop growth values; obtaining crop target values; acquiring actual crop growth values ​​using a crop organ phenotypic information acquisition platform and comparing them with the predicted values ​​to obtain the difference; determining whether to adjust the crop target values ​​based on the difference, and thus adjusting greenhouse environmental parameters. This invention incorporates crop organ phenotypic information feedback into greenhouse environment optimization and control technology, improving the yield and quality of greenhouse crops.

The invention discloses a rural household energy supply method and system and a fire wall. The system constructs a comprehensive energy system framework of a rural planting and breeding combined mode based on biological marsh gas energy, distributed photovoltaic power supply (light energy), air energy and the fire wall. One part of the biogas serving as driving energy is used for burning and heating the fire wall to supply heat to the greenhouse, and the other part is used for daily cooking of farmers; solar power generation drives the heat pump to produce hot water, one part of the hot water is used for heating farmer residential areas and supplying domestic hot water, and the other part of the hot water is used for enhancing the heating effect of a fire wall. According to the comprehensive energy system constructed by the invention, electricity utilization and heat supply of farmers can be met in a clean and low-carbon manner, and the yield of crops in the greenhouse can be increased at the same time. The light-gas synergistic ecological agricultural system provided by the invention realizes sustainable agricultural development with low carbon emission, low cost, high yield and resource circulation, and has important significance for constructing a clean and low-carbon energy system in rural areas in the future.