366 results about "Canopy" patented technology

The disclosure provides an improved canopy height correction method. The method includes: obtaining GEDI LiDAR data, airborne canopy height data, GDEM with high resolution and land cover product within the selected target area and timeframe; performing quality filtering and spatial-scale filtering on GEDI footprints; extracting laser pointing parameters and waveform parameters; extracting reference canopy height from airborne data for each footprint; extracting laser pointing parameters and waveform parameters; preprocessing the GDEM and calculating topographic parameters, including topographic variability index (TVI); constructing the Laser Pointing and Topographic Index (LPTI) according to the 3D forest-ground geometry model; inputting the waveform parameters, even topographic parameters, TVI and LPTI as independent variables, and the reference canopy height as the dependent variable to modeling an improved forest canopy height extraction, and utilizing the improved canopy height extraction model to correct the twofold influence of topographic on GEDI canopy height extraction.

The invention relates to the technical field of precision agriculture and grape cultivation, and discloses a vineyard fertilization method based on unmanned aerial vehicle multi-source remote sensing data, and the method comprises the steps: obtaining a canopy multi-spectral image and three-dimensional structure data through an unmanned aerial vehicle; carrying out image preprocessing and finely extracting a grape canopy region; fusing the extracted spectral vegetation index, texture features and three-dimensional structure features, and constructing a multi-source feature vector; constructing and optimizing a nitrogen nutrition inversion model through a machine learning algorithm by utilizing actually measured nitrogen nutrition parameters; generating a nitrogen content distribution diagram based on a model inversion result, calculating the nitrogen deficiency amount and the recommended dressing pure nitrogen amount of each space unit in combination with critical nitrogen concentration diagnosis and target yield, and converting the nitrogen deficiency amount and the recommended dressing pure nitrogen amount into the use amount of a foliage spraying working solution; and generating a variable fertilization prescription map, and converting the variable fertilization prescription map into a nozzle flow control instruction executable by the unmanned aerial vehicle to realize on-demand accurate variable fertilization. According to the invention, closed-loop management from nitrogen nutrition monitoring to variable rate fertilization is realized, and the nitrogen fertilizer utilization efficiency and the fertilization accuracy are improved.

The invention discloses an unmanned aerial vehicle LiDAR-based high-canopy-density crop plant height double-end adaptive inversion method and system, and relates to the technical field of agricultural remote sensing. The method comprises the following steps: acquiring a crop canopy point cloud and extracting density, skewness and original ground elevation characteristics; constructing a near-surface pseudo ground layer thickness correction model, introducing a pseudo ground layer thickness parameter to carry out pressing correction of a physical layer on an original ground elevation, and reconstructing a real bare soil layer ground reference; constructing a canopy end density-form adaptive model, and dynamically calculating the optimal upper boundary percentile by using the point cloud density and the elevation distribution skewness; and calculating the final plant height based on a double-end correction result. According to the method, the problems of systematic overestimation caused by near-surface pseudo ground blocking and poor adaptability of a fixed threshold value to the canopy form in LiDAR ground detection under the high-canopy-density canopy are effectively solved, and high-precision and robust plant height automatic extraction under multiple varieties and different flight heights is realized under the condition that external terrain auxiliary data is not needed.

The invention relates to the technical field of forestry management, in particular to a forest and grass resource dynamic monitoring system which comprises the steps that a graph attention network is adopted to conduct high-low weight recognition processing on the structure difference value between node pairs, feature vectors are established through the canopy height difference and canopy density difference between adjacent nodes, and the canopy height difference and the canopy density difference between adjacent nodes are obtained; an edge weight scoring system is constructed in the form of segmented statistics and proportion weighting, node edge pairs with high influence relation strength are dynamically screened, ordered aggregation of spatial communication strength is realized, a graph neural network is introduced in a parameter fusion stage to construct a node parameter representation mechanism, and the spatial communication strength is improved. The tree species proportion, the grade of diameter at breast height and the community vertical structure in a regional sample plot are used as input features, unified mapping of node features of each region is completed in multiple rounds of iteration, cross-regional difference analysis is executed on model output in combination with biomass change frequency, parameter items with the difference proportion lower than a set threshold value are replaced with unified expression, and the model output is obtained. And parameter synchronization is realized to realize space nesting and feature integration.

The invention relates to the field of forest ecological monitoring and operation management, and provides a method for quantifying the complexity of a three-dimensional structure of a forest canopy based on LiDAR and fractal dimension. Aiming at the key bottleneck that traditional canopy structure quantitative indexes are difficult to represent cross-scale three-dimensional space heterogeneity, high-precision three-dimensional point cloud data are acquired by using foundation and tower footing LiDAR, and precise individual tree segmentation is realized in combination with a machine learning clustering algorithm and tree structure features. According to the method, the three-dimensional void rate of the forest stand canopy is calculated, and the fractal dimension of a single tree is calculated by adopting a box counting method. The weight of a single tree is determined by comprehensively considering a single tree structure and a spatial distribution pattern of the single tree structure, the fractal dimension of a stand scale is calculated, a canopy structure complexity index is constructed in combination with a stand three-dimensional void rate, and scale-free quantification of the forest canopy three-dimensional structure complexity is achieved. According to the method, typical temperate zone secondary forest monitoring data is used for simulation and verification, and technical support is provided for forest accurate operation (cutting and complementary planting), carbon sink evaluation, biodiversity protection and the like.

The invention provides an automatic illumination adjusting method and system based on seed and seedling cultivation, and relates to the technical field of illumination control and intelligent agriculture, the method comprises the following steps: firstly, collecting seedling chlorophyll fluorescence parameters to construct a light stress index, and accurately identifying a light inhibition risk; then calculating a physiologically permissible light intensity threshold adaptive to the dark response capability of the plant by combining the environment temperature and the CO2 concentration; positioning a shielded weak light area by using three-dimensional point cloud voxelization and a ray tracing technology, and calculating a shadow compensation gain coefficient; and finally, generating a control matrix in combination with a dynamic left-rotation light strategy, and driving an independent addressing LED array and a variable-angle lens to execute local precise light supplement and light vector adjustment. Through physiological feedback and spatial light field reconstruction, the problem that traditional illumination control neglects individual tolerance difference and canopy shielding is solved, photooxidation damage is effectively prevented, and the growth uniformity and the light energy utilization rate of seedling groups are remarkably improved.

The invention relates to the technical field of intelligent agriculture and crop cultivation, in particular to a soybean planting chemical control self-adaptive regulation and control method and system based on canopy risk identification, and the method comprises the steps: constructing a digital twinborn model for dynamically simulating the growth process of a soybean population; monitoring a canopy structure, a leaf area index LAI and a light interception rate of the soybean in the key growth period by using an unmanned aerial vehicle or a sensor, and calibrating the digital twinborn model; future weather forecast data are input into the calibrated digital twinborn model for growth simulation, and light distribution is simulated by means of a pre-established AI model to identify soybean population closing and illumination competition risks; and according to the risk identification result, a chemical control decision is generated autonomously, and the variable pesticide spraying machine is controlled to perform precise variable operation of soybean planting based on the chemical control decision. According to the method, group development can be predicted in a prospective manner, and risks can be accurately quantified, so that an intelligent regulation and control mechanism with prospective and active properties is actively triggered, and active intervention of planting is realized.

The invention relates to an intelligent soil regulation and control method based on soil data and image processing. The method comprises the following steps: acquiring and standardizing soil data; orchard global multispectral image acquisition: acquiring and preprocessing an orchard image, and acquiring an orchard global multispectral image; vegetation region identification and parameter calculation: calculating NDVI based on red light and near infrared bands, distinguishing green manure and fruit tree regions through a threshold segmentation method, and calculating green manure coverage, a green manure normalized vegetation index and a fruit tree canopy area; carrying out soil-vegetation data fusion and parameter extraction; distributing weights based on the three types of core parameters, calculating the application amount and type ratio of nitrogen, phosphorus and potassium fertilizers, and generating an initial regulation and control scheme; and repeating the steps every specified time period to realize dynamic iterative optimization of the scheme. Through deep fusion of soil data and an image processing technology, intelligent soil regulation and control are realized, regulation and control accuracy and timeliness are remarkably improved, and fertilizer waste and environmental risks are reduced.

The invention discloses a rice chlorophyll content prediction method, device, medium and equipment, and relates to the technical field of crop growth, and the method comprises the following steps: obtaining canopy spectral data and physicochemical parameters of rice in a to-be-detected area; dividing the rice canopy into three concentric circle areas based on the leaf inclination angles of different leaves of the rice, and obtaining the layered rice canopy; constructing a hierarchical radiation transfer model HRTM for simulating a rice hierarchical spectrum, and inputting the physical and chemical parameters into the hierarchical radiation transfer model HRTM to generate hierarchical spectrum data; constructing a lookup table LUT based on the physicochemical parameters and the layered spectral data, taking the lookup table LUT as an analog data set, improving the analog data set by adopting a quartile method, and determining analog spectral data; based on the simulated spectrum data, constructing an inversion model for acquiring the chlorophyll content of the rice layers layer by layer; and inputting hyperspectral data to be measured into the inversion model, and determining a corresponding chlorophyll content prediction result.

The invention provides a soybean growth monitoring method based on satellite remote sensing, and relates to the technical field of crop growth monitoring, and the method specifically comprises the steps: setting two monitoring nodes according to a soybean growth period, and collecting multispectral and thermal infrared remote sensing images and normal growth leaf area density and stem diameter sample data; a soybean area and a non-soybean area are distinguished by using a vegetation index threshold, cloud / shadow pixels are eliminated by using a multiband combination-threshold method, effective pixels are screened, and two types of remote sensing data are matched. Extracting leaf area density, canopy temperature and collaborative inversion stem diameter from the effective data; calculating the relative growth rate of the two types of indexes based on samples, and constructing a collaborative growth reference equation and a residual standard deviation; and substituting the growth rate of the planting area into an equation to obtain a theoretical value, calculating a deviation value, and judging the soybean growth state by combining the comparison between the deviation and a normal fluctuation interval and the positive and negative characteristics of the growth rate.

The invention provides an unmanned aerial vehicle remote sensing monitoring and evaluating method and system for field orchard diseases and insect pests, and relates to the technical field of orchard disease and insect pest monitoring and evaluating. The interference of complex background noise is effectively eliminated by constructing a ternary separation index integrating feature enhancement, background suppression and brightness normalization; a threshold value is determined based on the exponential distribution, pixels are accurately divided into a non-canopy background, a shadow canopy and an illumination canopy, and fine segmentation of an unstructured scene is achieved; constructing a gain compensation item by using a local illumination reference mean value, correcting a shadow canopy reflectivity curve, and eliminating illumination non-uniform interference; selecting a local high quantile to establish a health reference basis, coupling a discriminant item and an attenuation item, and resolving to obtain a physiological stress index, so as to realize acute capture of early weak disease characteristics; and performing power weighted accumulation on the abnormal pixel difference value, outputting a regional hazard level index, and objectively quantifying the overall disaster risk of the region through nonlinear aggregation.

The invention discloses an under-forest cultivation method of a clitocybe maxima Shenshi No.2 strain, and belongs to the technical field of edible mushroom cultivation. The method comprises the steps that a forest land with the canopy density of 60%-75% is selected, and when the air temperature is stabilized at 23 DEG C or above in summer, bag-removed Shenmushroom No.2 fungus bags are placed in a furrow bed at the interval of 8-15 cm and covered with soil; a simple arched shed covered with a plastic film and a sunshade net is built above the furrow bed, and ventilation and humidity are regulated and controlled by opening and closing the two ends of the simple arched shed. According to the method, through two core measures of interval placement and arched shed regulation and control, the problems that fungus bags are prone to fungus burning and rainstorm scouring in the high-temperature period under the forest in summer are systematically solved, efficient and stable production of the high-temperature-resistant variety Shenmushroom No.2 under the forest in summer is achieved, and the blank of under-forest cultivation in the high-temperature season is effectively filled up.

The invention relates to the technical field of 3D point cloud instance segmentation, and particularly discloses a diversified forest point cloud individual tree instance segmentation method and system based on MAMBA structure enhancement, the overall performance is obviously optimized by constructing an intelligent adaptive segmentation framework, the method innovatively establishes a dynamic decision mechanism based on point cloud quality evaluation, and the method has the advantages that the overall performance is improved; whether a high-performance MAMBA enhancement module is started or not can be adaptively selected according to key indexes such as density characteristics, noise level and structural complexity of local point clouds; the flexible processing strategy enables the system to maintain the optimal working state when facing forest scenes with different complexity degrees: a lightweight processing flow is adopted in a local area with a sparse structure, and efficient operation is ensured; and the enhancement module is activated in a complex area with densely staggered crowns and serious shielding, so that the segmentation precision is ensured, and a solution with both efficiency and precision is provided for point cloud processing in large-scale forestry investigation.

The invention discloses an intelligent plant protection machine for small-plot crops, and belongs to the technical field of agricultural machinery. The core of the device comprises an intelligent control system, a self-propelled integrated chassis and a pesticide application system, the intelligent control system has intelligent recognition and self-adaption capabilities, can automatically adjust the wheel track, the pesticide application height and the pesticide application amount according to the crop row spacing and the canopy height, and integrates an intelligent obstacle avoidance module to effectively recognize and actively avoid; the self-propelled integrated chassis is compact in structure, electrically driven, flexible in operation and beneficial to small-plot operation, and is provided with the unique mechanical seedling protecting-pulling unit, so that seedlings are pulled aside and guided in the advancing process, and damage is avoided; the pesticide application system has the function of automatically calculating and preparing pesticide liquid according to the plot information. According to the invention, whole-course intelligentization and unmanned operation of plant protection operation are realized, the labor intensity is effectively reduced, the poisoning risk is completely eradicated, and the operation safety, precision and efficiency are effectively improved.

The invention discloses a carbon sink intelligent evaluation platform based on solid waste resource utilization, and the platform constructs a digital elevation model and a canopy height model through aerial remote sensing and ground environment data collection, and evaluates the vegetation net primary productivity in combination with a light energy utilization rate model. Training a soil organic carbon inversion model by adopting machine learning, and analyzing multi-stage hyperspectral image data; and establishing a soil carbon dynamic model to simulate a carbon solid storage and turnover process. Remote sensing data, LiDAR structure parameters and ground actually measured biomass are fused through random forest regression, and the biomass estimation precision is optimized. And finally integrating the plant and soil carbon sequestration amounts, calculating the total carbon sequestration amount of the ecological system and evaluating the temporal and spatial variation characteristics of the total carbon sequestration amount. According to the platform, intelligent and precise carbon sink evaluation is realized, and a scientific basis is provided for ecological restoration and carbon sink management.

The invention discloses a tree positioning calculation method based on unmanned aerial vehicle remote sensing, and relates to the technical field of agriculture and forestry vegetation positioning. According to the method, firstly, a digital orthoimage and a digital earth surface model (DSM) are generated through aerial photography of an unmanned aerial vehicle, a crown mask is obtained by using an instance segmentation technology, and a trunk position is accurately positioned by calculating a fusion score in the mask by using a joint resolving mechanism of a DSM local peak domain and an iterative corrosion morphological core. The method improves the positioning precision and efficiency through a DSM highest point * iterative corrosion combined optimization strategy, and improves the positioning stability of dense crowns through the corrosion depth constraint space position. According to the method, the positioning precision and efficiency of a complex forest environment are remarkably improved, and the problems of insufficient positioning precision, difficulty in positioning trunks under dense crowns and the like in the existing unmanned aerial vehicle remote sensing technology are solved.

The invention provides a crop pest risk assessment and early warning method fusing spectrum and structural features, and relates to the technical field of agricultural remote sensing monitoring and intelligent early warning, the method comprises the following steps: obtaining environmental factor data, multispectral image data and laser radar point cloud data; performing geometric correction, radiation correction and orthographic splicing processing on the multispectral image data to obtain a multispectral orthoimage; performing filtering and three-dimensional reconstruction on the laser radar point cloud data to generate a canopy height model; performing spatial registration and resolution unification on the multispectral orthoimage and the canopy height model to obtain a multi-source fusion data set; and based on the multi-source fusion data set, extracting a disease spectral index DSI under a micro scale and a canopy structure anomaly index CSAI under a macro scale. According to the invention, multi-scale dynamic monitoring, quantitative risk assessment and regionalized graded early warning of diseases and insect pests can be realized, and the monitoring precision and timeliness are improved.

The invention discloses a crop spectrum interpretation and stress diagnosis method considering a water-nitrogen coupling effect, and the method comprises the steps: obtaining crop canopy hyperspectrum and growth period data, recognizing a water-nitrogen cross sensitive wave band, executing the signal decoupling processing, and separating independent water and nitrogen feature vectors. Spectral covariant features are extracted, and in combination with growth period data, an adaptive coupling coefficient reflecting real-time interaction intensity is calculated; and constructing a dynamic joint evolution model containing moisture and nitrogen stress states, defining an interaction relationship between the moisture and nitrogen stress states by using an adaptive coupling coefficient, and solving a steady-state solution through numerical iteration to obtain a moisture and nitrogen stress index after coupling correction. And mapping the corrected index to a multi-dimensional diagnosis space, calculating the membership degree of the sample to different stress types, and outputting a quantitative proportioning result of water nitrogen stress. According to the invention, the problems of difficult decoupling of water nitrogen physiological signals and lack of dynamic evolution characterization are solved, and accurate quantitative diagnosis of composite stress is realized.

The invention discloses a method for controlling the growth condition of strawberry seedlings, and belongs to the technical field of strawberry seedling raising. The method for controlling the growth condition of the strawberry seedlings comprises the steps that illumination uniformity is adjusted, nutrient soil is prepared for strawberry seedling raising, and illumination uniformity adjustment comprises the steps that different numbers of LED lamp tubes are used for irradiating the growing strawberry seedlings, the illumination uniformity of strawberry seedling canopies is improved, and the energy efficiency ratio is increased; the preparation of the nutrient soil comprises the following steps: mixing and stirring the components of the nutrient soil to ensure the uniform distribution of the raw materials, then putting the nutrient soil into a planting pot, watering and wetting for strawberry seedling planting. The nutrient soil comprises the following components in parts by weight: 35-35 parts of leaf mold; 45 parts of the raw materials and 17 parts of vermiculite powder; 23 parts of perlite, and 23 parts of perlite; 30 parts of carbon nitride composite humic acid; 8 parts of the fertilizer and 5 parts of a mixed fertilizer; 7 parts, 1 part of mycorrhizal inoculant; and 3 parts. According to the method, the growth condition of the strawberry seedlings is cooperatively controlled by adjusting the illumination intensity and preparing the nutrient soil, so that the strawberry seedlings are developed in root system, thick and strong in leaf stems and free of plant diseases and insect pests, and the plant height and the leaf area of the strawberry seedlings are obviously increased after the seedling age is 30 days.

This invention belongs to the field of remote sensing technology and relates to a method for predicting canopy combustible moisture content (CFMC) by combining remote sensing CFMC products and meteorological data. Specifically, through global two-stage sensitivity analysis, key input parameters for different tree species in the MEDFATE model are identified. Using these parameters, a cost function is constructed to measure the error between the CFMC simulated by the MEDFATE ecophysical process model and the CFMC measured in the field, achieving species-based localized correction of MEDFATE. Then, through four-dimensional variational data assimilation technology, the remote sensing CFMC product is combined with the localized MEDFATE. This achieves enhanced CFMC simulation, improving the accuracy of MEDFATE CFMC simulation while also increasing the temporal resolution of remote sensing data and the spatial resolution of the MEDFATE model. With accurate meteorological forecast data, effective daily CFMC prediction of vegetation can be achieved.

The utility model belongs to the technical field of agricultural machinery, and particularly relates to an anti-brown-spot layered pruning support suitable for canopies of tangelo trees. Comprising first connecting rods, second connecting rods, first connectors, second connectors and telescopic stand columns, the multiple first connecting rods and the multiple first connectors are connected to form a first closed loop, the multiple second connecting rods and the multiple second connectors are connected to form a second closed loop, and the adjacent first connectors are connected with the second connectors. Layered pruning management is achieved, the light transmittance is improved, humidity accumulation is reduced, the support can be rapidly assembled, the device adapts to different tree ages and tree forms, the manual climbing risk is reduced, operation is easy and convenient, and practicability is high.

The invention discloses a one-film six-row wide-narrow-row close-planting staggered planting method for cotton, which comprises the following steps: measuring the soil nutrient content of a region to be planted, calculating the comprehensive score of soil fertility, selecting a basic plant spacing according to the tillering coefficient of a cotton variety, fitting an accumulated temperature correction function, calculating a predicted value of the tillering rate, and calculating a dynamic offset in combination with the latitude of a planting field. Determining a narrow row spacing and a sowing depth according to the dynamic offset, extracting a growth picture of the same variety of cotton in the same region after the flowering and boll-setting period, extracting a planting row spacing mean value of the canopy coverage in a preset range as a wide row spacing, planting cotton according to the basic plant spacing, the narrow row spacing, the wide row spacing and the sowing depth, and monitoring the canopy photosynthetically active radiation in real time. And dynamically regulating and controlling a drip irrigation fertilization strategy according to the photosynthetically active radiation of the canopy. By means of the method, the photosynthetic efficiency, the water and fertilizer utilization efficiency and the stress resistance of cotton groups can be improved, a brand new technical path is provided for high-yield and high-quality cultivation of cotton, and the method has important significance in promoting intelligent transformation of the cotton planting industry.

The invention relates to the technical field of vegetable facility cultivation, and discloses a sunlight greenhouse high-lighting-effect cultivation method for overwintering tomatoes, which comprises the following steps: S1, greenhouse preparation: selecting a single-slope sunlight greenhouse with a front slope covering and walls on the east, middle and west sides, covering the front slope with a light-transmitting heat-insulating material, and covering the front slope with a heat-insulating roller shutter quilt containing double-layer heat-insulating cotton at night. According to the sunlight greenhouse high-photosynthetic-efficiency cultivation method for the overwintering tomatoes, by optimizing model selection and configuration of a greenhouse (such as covering a front slope with a high-light-transmittance PO film), adopting an accurate one-ridge double-row staggered-row planting mode and cooperating with scientific pruning and pruning, illumination distribution and a plant population structure in the greenhouse are systematically improved, and the yield of the greenhouse is increased; according to the method, the effective light reception of the middle and lower parts of the plants under the weak light condition in winter is remarkably enhanced, the photosynthetic efficiency of the whole canopy is improved, the problems of excessive growth of the plants, canopy closing, uneven coloring and the like caused by a traditional close planting mode are effectively solved, and a solid foundation is laid for realizing high quality and high yield of the overwintering tomatoes.

This invention relates to a method for detecting peanut leaf spot disease images in the canopy, overcoming the shortcomings of existing technologies in detecting peanut leaf spot disease in complex backgrounds. The invention includes the following steps: constructing a dataset; constructing a leaf spot disease detection model; training the leaf spot disease detection model; collecting and preprocessing high-resolution images of the peanut canopy to be detected; and obtaining the leaf spot disease detection results. This invention can handle the detection of small-target peanut leaf spot in complex backgrounds, balancing detection accuracy and real-time performance in the small-target detection problem, achieving real-time field-scale detection of peanut leaf spot disease in the canopy.

The invention discloses a leaf area index inversion calculation method, and aims to solve the technical problem that the existing leaf area index estimation accuracy is limited. According to the method, collected crop canopy data is analyzed by adopting internal feature importance, a feature importance ranking is output by adopting a random forest algorithm, the most valuable feature is effectively screened out to participate in inversion of the leaf area index, and the inversion complexity can be greatly reduced. Bayesian optimization parameters are adopted, and historical parameter adjustment information is fully utilized through Bayesian optimization iteration, so that deep search of the parameters is fully realized, unnecessary objective function evaluation is reduced, and parameter adjustment efficiency is improved. Wherein the Bayesian optimization adopts a Gaussian process, previous parameter information is further considered, prior information is continuously updated, and the prediction precision is improved.

This invention discloses a forest fire risk early warning system based on Internet of Things (IoT) communication, belonging to the field of data processing technology. The system comprises a data acquisition module that collects forest area data from edge devices; an environmental feature module that uses an adaptive forgetting factor recursive method to obtain environmental anomaly response vectors; a feature fusion module that extracts image feature tensors, maps these vectors to scaling matrices, combines them with a forced masking weighted feature tensor, and outputs predicted bounding box parameters; a feature inversion module that obtains a penalty matrix based on the parameters, constructs a partial differential equation for canopy drag force, and solves for the fire plume feature matrix and flow field uncertainty tensor matrix by minimizing the inverse source-finding objective function; and a communication optimization module that extracts elements as fire risk mapping weights, arranges them in descending order, and allocates time resources so that the terminal only sends fire plume elements to the decision center. This invention effectively isolates meteorological interference, improves the accuracy of locating small fire points under dense smoke, and solves the communication congestion problem during extreme fire events.

The invention relates to the related technical field of crop irrigation, in particular to a high-temperature-resistant and drought-resistant intelligent precise spraying device which comprises a bearing seat, a water supply unit, a stand column, a bearing frame, a spraying unit, an L-shaped bearing column, a redirection unit and a leaf turning execution unit. Through the synergistic effect of active plant turning and intelligent redirection spraying, the problem that an anti-evaporation agent is difficult to act on the back faces of leaves is solved. The leaf turning execution unit gently turns up the top canopy layer of the cotton through the unique movement mode that a leaf pushing plate ascends firstly and then turns over, air hole dense areas on the back faces of leaves are accurately exposed, an intelligent avoiding mechanism is arranged in the leaf turning execution unit, and it is guaranteed that crops are not damaged in the turning process. And meanwhile, the redirection unit captures the falling medicament, the direction of the medicament is changed into lateral upward spraying by utilizing airflow, and the medicament is deeply atomized through a dual physical crushing mechanism, so that the medicament can be uniformly attached to a target area in the form of tiny liquid drops.

The invention belongs to the technical field of ecological hydrology, and particularly discloses a forest underlying surface earth surface evapotranspiration estimation and component segmentation method based on kNDVI. According to the method, based on a maximum entropy increase model, under the constraint of a surface energy balance equation, the surface state parameters of a forest vegetation-free area are used for resolving to obtain the soil evaporation capacity of the bare soil; on the basis of a maximum entropy increase model, under the constraint of a canopy energy balance equation, the vegetation transpiration amount is obtained through calculation according to the canopy state parameters of the forest vegetation coverage area; calculating the canopy interception evaporation capacity according to the leaf area index and the rainfall capacity of the forest vegetation coverage area; and finally, based on the kNDVI index, carrying out weighted summation on the bare soil evaporation amount, the vegetation evaporation amount and the canopy interception evaporation amount in the forest to obtain the total evapotranspiration of the underlying surface of the forest. Compared with an existing evapotranspiration estimation model, the forest underlying surface earth surface evapotranspiration estimation method is lower in complexity and higher in estimation precision.

The invention relates to the technical field of agricultural plant protection machinery, and discloses a grape rail type leaf back pesticide sprayer which comprises a rail and a frame, the rail is a route laid by strip-shaped steel and used for driving of the pesticide sprayer, and the frame plays a role in supporting and connecting all parts of the pesticide sprayer and bears various loads from the inside and the outside of a vehicle. The two sides of one end of the vehicle frame penetrate through and are rotationally connected with connecting rods, the two sides of the outer rings of the connecting rods are fixedly connected with rail wheels, the rail wheels make contact with the tops of rails, and the two sides of the vehicle frame are fixedly connected with connecting pieces. The equipment integrates the fertilization tank and the star-shaped discharger structure, and the running vibration of the frame is matched with the shaking of the elastic band in the fertilization process, so that the fertilizer blockage is effectively avoided, the grape root fertilization and canopy pesticide application operations can be synchronously completed, the fertilization equipment does not need to be independently configured, the field equipment investment and operation links are reduced, and the overall operation efficiency is improved.

This invention relates to the field of forestry resource monitoring technology, and discloses a dynamic monitoring system and method for forestry resources based on remote sensing imagery. The system includes: generating temporal remote sensing reflectance data within the same grid; generating a continuous canopy occupancy map; generating an infill set; determining the intrinsic neck scale; calculating the topological spectrum intensity of the continuous canopy infill; calculating the interlayer decoupling topological index; generating monitoring judgment results; and outputting change patches. This invention constructs an adaptive multi-scale erosion sequence based on the intrinsic neck scale, combines Eulerian features to quantify the infill topological fragmentation process, and integrates the degree of infill fragmentation with the stability state of the outer envelope through the interlayer decoupling topological index. This achieves accurate determination of change units and precise spatial patch location, avoiding the omissions or misjudgments of deep forest understory structure changes by traditional techniques, ensuring that the monitoring results accurately reflect the actual mechanisms of dynamic changes in forest resources.