33 results about "Plant morphology" patented technology

The invention relates to the technical field of peanut plant growth state intelligent monitoring, in particular to a peanut plant growth state intelligent monitoring method and system and electronic equipment. The method comprises the following steps: respectively acquiring visible light and near-infrared images, laser radar point cloud data, near-infrared spectra and microscopic images of peanut plants by using a multispectral camera, a laser radar, a near-infrared camera and a microscopic imager carried by a field robot; carrying out de-atomization and color correction on the image by adopting a self-adaptive illumination compensation algorithm; a laser radar point cloud is fused to construct a plant three-dimensional point cloud model; segmenting an overlapped leaf area based on a graph neural network, and extracting morphological characteristics of a single plant; the nitrogen content and the moisture content of the leaves are analyzed through near infrared spectroscopy, and early scabs are recognized in combination with microscopic imaging; and inputting the morphological characteristics and the physiological parameters into a growth state evaluation model, and outputting growth indexes and disease and pest early warning. According to the invention, automatic and refined monitoring and growth state comprehensive evaluation of peanut plant morphology and physiological parameters are realized.

The invention relates to the technical field of planting environment detection, in particular to an agricultural product planting environment detection method and system. The method comprises the following steps: acquiring a soil layer state of a strawberry planting area through a soil monitoring sensor, and performing pH fluctuation analysis to obtain strawberry soil layer pH fluctuation data; obtaining strawberry plant morphology data, evaluating a root system extension state, simulating organic matter decomposition and decay based on soil layer acidity and alkalinity data, and analyzing the peracid development retardation of strawberry root systems; analyzing the soil layer over-alkaline fluctuation index, simulating the influence of the over-alkaline soil layer on the absorption of trace elements in the strawberry root system, and carrying out regression analysis on the nutrient uptake efficiency; and constructing an automatic planting environment detection model in combination with the root development retardation evolution data and the nutrient uptake efficiency regression data, and uploading the model to a cloud platform to execute agricultural product planting environment detection. According to the invention, the planting environment detection technology is optimized, so that the planting environment detection technology is more perfect.

The invention provides a plant three-dimensional reconstruction method and system fusing physical prior and density pixel alignment. The method comprises the steps of obtaining target multi-view observation data; and inputting the target multi-view observation data into a tomato plant neural radiation field model to obtain a tomato plant neural radiation field, the tomato plant neural radiation field model being obtained based on improved neural radiation field training. The improved neural radiation field comprises a multi-resolution hash coding sub-model, a plant morphology priori constraint sub-model and a density pixel alignment loss function sub-model; and based on a hierarchical rendering strategy and a neural slice rapid rendering technology, rendering the tomato plant neural radiation field to obtain a tomato plant three-dimensional reconstruction result. The tomato plant three-dimensional reconstruction precision and efficiency are improved.

The invention discloses a plant leaf globulin BrLH1 coding gene, and belongs to the field of plant genes. According to the invention, a specific primer is designed, a PCR technology is utilized, and a gene named as BrLH1 is cloned from comfortable heading Chinese cabbage; the qPCR analysis finds that the gene is expressed in a Chinese cabbage flat-collapse non-heading mutant, but the expression of the gene is lower than that of a wild type. Through MutMap result analysis, the BrLH1 (leafy head 1) is a function deletion type gene, and after the gene BrmLH1 is mutated, the leaf of the Chinese cabbage does not form a ball. The BrLH1 can be used as a gene resource for improving plant morphology, can realize precise design and breeding of plant gene editing, and has important theoretical significance and application value in the aspects of creating new germplasm and improving yield and quality.

The invention discloses a thick-stem high-yield caulis spatholobi germplasm screening and breeding method suitable for an under-forest habitat. According to the method, a standardized under-forest cultivation system is constructed, spiral winding induction synergistically mediated by plant morphology and plant hormones is implemented, lignification development of stems is enhanced by applying mild mechanical stress with specific parameters, rhizosphere microorganism regulation and systematic nutrition management are synchronously combined, and the yield of the stems is increased. And finally, a multi-dimensional comprehensive evaluation system covering the stem shape, the wood density, the active ingredient content and the under-forest weak light adaptability is established, so that an integrated technical scheme for efficiently and accurately breeding the excellent caulis spatholobi germplasm with thick stem phenotype, high flavone accumulation and strong under-forest weak light tolerance from a large number of candidate germplasm is realized.

The present invention relates to the technical field of planting environment detection, and particularly to a method and system for detecting the planting environment of agricultural products. The method includes the following steps: obtaining the soil layer state of the strawberry planting area through soil monitoring sensors, and performing an analysis of the pH fluctuation to obtain the strawberry soil layer pH fluctuation data; obtaining the strawberry plant morphology data, evaluating the root extension state, and simulating the decomposition and decay of organic matter based on the soil layer pH data to analyze the acid-induced growth retardation of strawberry roots; analyzing the over-alkaline fluctuation index of the soil layer, simulating the influence of the over-alkaline soil layer on the absorption of trace elements by strawberry roots, and performing a regression analysis of the nutrient uptake efficiency; combining the root development retardation evolution data and the nutrient uptake efficiency regression data to construct an automated planting environment detection model, and uploading the model to the cloud platform to perform the detection of the planting environment of agricultural products. The present invention makes the planting environment detection technology more perfect through the optimization of the planting environment detection technology.

The present application belongs to the technical field of plant morphology, and particularly relates to a seed plant propagule three-dimensional shape measurement method based on a video stereomicroscope, comprising: collecting seed plant propagules at the mature stage and air-drying to a constant weight; classifying the propagules according to the seed, fruit shape and surface accessory structure, and determining the measurement criteria of the shape of different categories of propagules; scanning the three-dimensional structure of each category of propagule by using a video stereomicroscope, and accurately measuring the shape parameters length, width and thickness of the propagule; normalizing each category of propagule, calculating the shape index of the propagule, and representing the closeness to a spherical shape. The method quantifies the complex three-dimensional shape characteristics of the seed plant propagule into a comprehensive shape index, realizes accurate quantitative measurement and digitization of the morphological characteristics of the propagule, solves the pain point of determining the shape of the propagule by using a traditional observation tool, and facilitates the comparative identification of the three-dimensional shapes of different categories of seed plant propagules.

A canopy morphology 3D feature analysis method based on lightweight drones and point cloud singular value decomposition belongs to the field of plant morphology analysis. The method is as follows: a drone-mounted RGB camera is combined with the SfM‑MVS algorithm to perform high-precision three-dimensional reconstruction of the soybean canopy structure. At the same time, a label-based method introduces the singular value decomposition (SVD) method for the point cloud coordinate matrix, and 9 canopy morphology 3D features at multiple scales are established as a new interpretable feature at the crop plot level. The results show that the global canopy 3D features are significantly correlated with soybean yield, lodging, canopy height, and vegetation index (p <= 0.05). 3D feature extraction based on local range can extract more detailed structural information of the canopy surface, which helps to characterize the differences in more growth stages of soybeans.

The invention discloses a basil leaf sampling device, and relates to the technical field of sample collection. The device comprises a walking base, and further comprises a mounting frame mounted on the walking base; the two side plates are connected through the two parallel connecting plates and are in a rectangular frame shape, strip-shaped grooves are formed in the end portions of the side plates, the strip-shaped grooves extend in the length direction of the side plates and penetrate through the two sides of the side plates, and the tail ends of the side plates are connected with the mounting frame. Through the design that the two first conveying belts moving in the opposite directions are matched with the flexible abutting piece, the leaves can be gently clamped and guided to be conveyed, the complex plant forms such as multi-layer stacking can be effectively handled, and the separation mechanism can be matched with the separation mechanism to achieve separation of the leaves by means of the included angle difference between the leaf orientation and the conveying direction. Automatic deviation of inclined leaves is realized by matching with frictional resistance difference, single-leaf sampling of basil leaves is realized, and unnecessary wounds of basil plants can be effectively reduced by matching with a cutting mechanism to cut off leaf stalks.

The invention discloses equipment for monitoring environmental parameters and interaction of an ecological bottle based on artificial intelligence video analysis, and relates to the crossing field of artificial intelligence and ecological monitoring technologies. The equipment comprises an ecological bottle body module, a data acquisition module, an AI core analysis module, an intelligent interaction module and a data storage and display module. The data acquisition module acquires plant morphology data and environmental parameter data in real time through a high-definition camera and a sensor; the AI core analysis module establishes an association relationship between plant morphology and environmental parameters by means of a pre-trained plant morphology-environmental parameter association model, matches optimal growth environmental parameters in combination with a plant environmental parameter database, and evaluates a growth state; the intelligent interaction module realizes bidirectional interaction through voice and an APP, and provides regulation and control suggestions, planting consultation and emotion accompanying; and the data storage and display module is used for storing full-cycle data and generating a delayed video according to user requirements. According to the invention, monitoring of environmental parameters of the ecological bottle and intelligent interaction of the user are realized, scientific planting guidance is provided for the user, user experience is enriched, and the system is suitable for ecological bottle maintenance in scenes such as families, schools and scientific research institutions.

The application provides a high-cold and steep-slope stress-resistant plant classification method and system based on plant image processing, and belongs to the fields of image processing and artificial intelligence. Time-series growth morphology images of a target plant group in a preset growth cycle in a high-cold and steep-slope region are collected, the time-series growth morphology images are subjected to morphology response trajectory analysis processing, a plant morphology response trajectory set is obtained through time-series image frame difference and morphology change vector extraction, stress resistance interactive modeling is performed on the plant morphology response trajectory set, a plant stress resistance interactive atlas is generated through multi-feature synergistic effect strength calculation and stress resistance contribution degree adjustment, and the plant morphology response trajectory set and the plant stress resistance interactive atlas are subjected to progressive prototype adaptive matching with a preset stress resistance prototype trajectory mode library to determine a high-cold and steep-slope stress-resistant plant classification result. Through the application, the influence of feature correlation misjudgment on the classification result can be effectively reduced, and the reliability and robustness of stress-resistant plant classification can be improved.

The invention provides a dynamic plant spacing adjustment method and system for cotton close-planting high-yield cultivation. The method comprises the steps that plant morphology, field microenvironment and target cotton plant close-planting photosynthetic rate initial data of cotton in different growth stages are collected; based on the data, calculating the optimal plant spacing range at the current stage by using a pre-constructed plant spacing adaptation model which takes the cotton close planting yield maximization as an objective function and takes the plant canopy overlapping rate, the inter-row illumination transmittance and the soil humidity as constraint conditions; obtaining the actual plant spacing of the cotton field, comparing the actual plant spacing with the optimal range, judging whether to adjust, generating an adjusting instruction if the plant spacing exceeds the range, controlling the automatic transplanting device to operate, and collecting data again after adjustment to verify whether the plant spacing accords with the optimal range. According to the method, the plant spacing adaptation model is constructed, and the problem of multi-constraint synchronous coordination is solved by combining dynamic weight coefficient and growth stage characteristic matching; data are regularly collected to update model parameters, the cotton close planting yield is improved, and cultivation management intelligence is achieved.

This invention discloses an intelligent flowerpot and its control system. The intelligent flowerpot includes a flowerpot body and integrated within the flowerpot body a main control module, an image acquisition module, an environmental sensing module, an execution control module, a communication module, and a power supply module. The interior of the flowerpot body is divided into a plant planting chamber, a liquid storage chamber, and a sealed equipment chamber by physical partitions. This invention acquires images of plant morphology and soil surface through the image acquisition module, identifies plant species and soil physical states using the main control module, and then matches the optimal growth parameter range and executes closed-loop adaptive adjustment. Furthermore, the system also has a soil ecological anomaly monitoring function based on multi-source data fusion. By comprehensively analyzing image information, physicochemical parameters, and plant growth status, it achieves automatic early warning and physical intervention for soil ecological anomalies. This invention enables precise monitoring and intelligent management of the plant growth environment, significantly improving plant maintenance efficiency and survival rate.

The invention belongs to the technical field of muskmelon growth determination methods, and discloses a muskmelon growth determination method in combination with a light-water-fertilizer technology, and the method comprises the following specific steps: S1, selecting a sunlight greenhouse in an experiment field and a material experiment field, and selecting a muskmelon variety 'Mei Net 9'; plants are divided into a shading group and a non-shading group, the two illumination treatment groups are subjected to different field moisture capacity treatment, the field moisture capacity is continuously refined according to different fertilizers, and a dynamic interaction mechanism of illumination, moisture and fertilizer three factors is integrated, so that the limitation of traditional single environment factor research is broken through; systematic and accurate monitoring of the growth process of the facility muskmelons is achieved, the interaction mechanism of illumination and water and fertilizer treatment is disclosed, the yield and quality of the muskmelons are improved, development of the muskmelon industry is promoted, and core index measurement covering plant morphology, photosynthetic efficiency and fruit quality is established.

The application discloses a kind of plant morphology phenotype information extraction method, belong to computer vision technical field.The method includes: propose local neighborhood structure plant node search positioning algorithm, the geometric distance and direction vector angle between adjacent nodes are calculated, the local neighborhood topological structure between nodes is analyzed, the node class and its belonging branch are judged;Plant region growth connection algorithm is proposed, the mask contour of the plant part of the same adjacent node is reduced dimension, the point that the maximum distance from direction axis is found on the target contour along the line parallel to direction axis, the boundary point of target plant part is determined, adjacent boundary point is sequentially connected, a continuous curve segment is formed, that is, the path close to adjacent node in real plant image;All the same adjacent nodes are sequentially repeated this operation, connect each part of plant, and construct complete plant morphology, realize fast and effective extraction plant morphology phenotype information, with the characteristics such as robust output result, convergence fast and stable.

The invention belongs to the field of plant morphology, and discloses a method for generating a representative leaf based on leaf contour analysis, which comprises the following steps: S1, selecting a target tree species and collecting leaves; s3, preprocessing the grid image data of the blade contour; s4, superposing the blade contours to generate a density map; and S5, reconstructing a representative blade contour. According to the invention, through high-precision digital image processing, form registration and density threshold optimization algorithms, precise reconstruction of the blade contour and generation of the representative blade are systematically realized. According to the method, optimization is carried out in the aspects of standardized leaf collection, automatic morphological analysis and optimal threshold calculation, so that quantification of leaf morphological characteristics is more accurate and higher in stability, and the method has remarkable universality and is suitable for various tree species and different ecological environments.

The application discloses a Gossypium hirsutum l. leaf roll regulation gene GHCLG and application thereof. The nucleotide sequence of the gene in a normal broadleaf Gossypium hirsutum variety TM-1 is shown in SEQ ID NO. 1, and the nucleotide sequence of the gene in a Gossypium hirsutum leaf roll mutant T582 is shown in SEQ ID NO. 3. The gene cloned by the application has a direct connection with cotton leaf shape. Transgenic research is carried out on normal broadleaf Gossypium hirsutum 668 by using a constructed plant gene editing vector, and the result shows that knocking out the GHCLG gene leads to cotton leaf shape upwarping and inrolling, so that the gene has great application potential in improving plant leaf shape and plant morphology, improving plant photosynthetic efficiency and resisting pests and the like.

The invention provides an alpine meadow stellera chamaejasme directional clearing device. According to the alpine meadow stellera chamaejasme directional clearing device, high-confidence-coefficient identification of stellera chamaejasme in a complex vegetation community is ensured through a multi-spectrum and three-dimensional vision fused identification unit; the adaptive operation decision-making module based on a root growth model can intelligently match optimal clearing parameters according to plant morphology; the multi-degree-of-freedom mechanical arm and the drilling and spraying integrated execution head achieve the synergistic effect of physical damage and chemical killing under millimeter-level positioning precision. According to the method, the extensive mode of traditional broad-spectrum pesticide application or comprehensive ploughing is changed fundamentally, prevention and control activities are strictly limited to target plant individuals, and damage of herbicides to non-target plants and large-area damage of mechanical operation to meadow soil and native vegetation are avoided; and an efficient, environment-friendly and generalizable innovative technical solution is provided for treatment of invasive plants in the high and cold ecologically fragile area.

The present application discloses a method and device for speech recognition of dysarthria caused by stroke. The technical solution of the present application classifies the acquired speech sample data according to the syllable category represented by the audio and the normal and patient categories, and obtains the audio spectrogram through transformation. Then, in the stage of building the network model, the front-end network uses the core processing module designed by plant morphology and physiology, and forms the rhizome with continuous Downsample after the STEM module to quickly transport the computing nodes to a higher receptive field area. The back-end network is based on the alternating configuration of the deep separable convolution and attention mechanism based on the Xception module to form a vine cross structure. The attention mechanism is selectively placed in the alternating convolution modules to improve the recognition ability and accuracy of key speech features, thereby being able to capture the global receptive field at multiple scales, accurately learn and discriminate the significant characteristic information of stroke pathology, and solve the technical problem of low accuracy in the existing articulation analysis of stroke patients.

The invention provides an application of a GhMAKR1 gene of upland cotton in regulation and control of cotton fiber elongation and plant morphology, 419 parts and 1081 parts of upland cotton varieties are re-sequenced and subjected to whole genome association analysis with fiber length traits, and it is found that the GhMAKR1 gene located in a D11 chromosome has significantly associated SNP and SV with the cotton fiber length. Gene overexpression and gene silencing means are used for verification and show that high expression of GhMAKR1 in a fiber development stage can significantly inhibit fiber elongation, and silencing causes fiber length increase. Meanwhile, research finds that the GhMAKR1 is used as an unknown functional protein, and overexpression of the GhMAKR1 shows a series of unique phenotypic characteristics, including dwarfing which is mainly shown in stem and internode shortening. In addition, overexpression plants also produce short and small flowers, the vigor of the flowers is reduced, and cotton bolls and seeds become small. The GhMAKR1 and the GhBIN2 protein can form an interaction compound to jointly inhibit the fiber cell elongation process.

The invention discloses a precise sowing system for a hillside orchard based on remote sensing of an unmanned aerial vehicle. The system belongs to the technical field of unmanned aerial vehicle intelligent agriculture and comprises a hyperspectral data acquisition module, a hyperspectral data processing module, an oblique photography data acquisition module, a plant morphology distribution data extraction module and a precise sowing module. The hyperspectral data acquisition module and the oblique photography data acquisition module output spectral images, oblique images and POS data; the hyperspectral data processing module generates a plant growth vigor and stress degree feature map according to the spectral image; the plant morphology distribution data extraction module generates a morphology distribution diagram of single plants in the orchard based on the inclined image; the precise sowing module completes precise sowing under the scheduling of the MCU controller; according to the method, through fusion of hyperspectral remote sensing monitoring, live-action three-dimensional modeling, optimal path planning and variable sowing control, accurate positioning and accurate dosage sowing with a single plant as a unit are realized, the utilization efficiency of water, fertilizer and pesticide is improved, the fruit quality is improved, and intelligent and accurate management of the hillside orchard is promoted.

The invention discloses a plant form phenotype information extraction method, and belongs to the technical field of computer vision. The method comprises the following steps: providing a local neighborhood structure plant node searching and positioning algorithm, calculating a geometric distance and a direction vector angle between adjacent nodes, analyzing a local neighborhood topological structure between the nodes, and judging a node category and a branch to which the node category belongs; proposing a plant region growth communication algorithm, performing dimension reduction on a plant part mask contour of similar adjacent nodes, searching a point with the largest distance from a direction axis on a target contour along a line parallel to the direction axis, determining boundary points of a target plant part, and connecting adjacent boundary points in sequence to form a continuous curve segment, the path is close to the path of adjacent nodes in the real plant image; the operation is repeated on all adjacent nodes of the same kind in sequence, all parts of the plant are connected, the complete plant morphology is constructed, plant morphology phenotypic information is rapidly and effectively extracted, and the method has the advantages of being robust in output result, rapid and stable in convergence and the like.

The invention discloses a garden landscape design method and system based on a virtual reality technology, and relates to the technical field of garden landscape design, and the method comprises the steps: constructing a three-dimensional garden scene frame through a virtual reality device, and initializing terrain base parameters and ecological simulation parameters; a dynamic terrain is generated in real time based on whole body motion capture data of a designer, and a human body motion track is converted into a continuous terrain curved surface; receiving biomechanical input through tactile feedback equipment, driving dynamic adjustment of the plant growth model, and generating a plant form with manual intervention memory; establishing a real-time bidirectional feedback model of microclimate parameters and plant physiological parameters, and deducing a plant community succession process based on a time axis; scene details are dynamically optimized according to the visual focus area, and cross-culture semantic features are fused to generate composite landscape elements.

The application relates to the field of agricultural technology, and particularly discloses a corn yield rapid estimation method, device and equipment based on corn plant morphology, which comprises the following steps: obtaining corn plant morphology data 10 days before flowering to obtain a first above-ground biomass estimation result; obtaining corn plant morphology data 14 days after flowering to obtain a second above-ground biomass estimation result; based on the above estimation results, a plant growth rate estimation result in a yield formation critical period is obtained; and finally, based on a correlation model of the plant growth rate in the yield formation critical period and the corn yield and the growth rate estimation result, the corn yield is estimated. In this way, the corn yield can be directly estimated by using the corn plant morphology data surveyed in the field, and the method is simple and efficient. Moreover, since the plant growth rate in the yield formation critical period, i.e. 24 days before and after corn flowering, is determined, the most critical influence of drought stress on yield can be considered to the greatest extent, and the estimation accuracy is improved.

The present invention provides a rice gene OsFd4 and its application in rice resistance to bacterial blight. The OsFd4 open reading frame nucleotide sequence of the gene is shown in SEQ ID NO.1, and the amino acid sequence encoded by it is shown in SEQ ID NO.2. The present invention uses the CRISPR / Cas9 method to knockout the rice gene OsFd4 . The inoculation experiments of wild-type and knockout mutant plants with Xanthomonas oryzae pv. oryzae are carried out respectively. The results show that knockout of OsFd4 significantly improves the levels of reactive oxygen species burst and callose deposition in rice plants after treatment with the flagellin short peptide of bacterial pathogens, thereby enhancing the resistance of rice to Xanthomonas oryzae pv. oryzae. At the same time, through the observation of the plant morphology in the field, it is found that OsFd4 the growth status of the knockout mutants is normal and there is no significant difference compared with the wild type. The above results indicate that OsFd4 is a gene that negatively regulates the immunity of rice to bacterial blight. The content of the present invention provides an endogenous gene target for improving the resistance of rice through gene editing technology when bacterial blight occurs in the field.

The invention provides a differential medium for screening corn transformants and a method for screening corn positive seedlings, and belongs to the technical field of plant genetic engineering. The invention provides a differential medium for screening corn transformants. The differential medium contains glufosinate-ammonium or glufosinate-ammonium pesticide. The differential medium provided by the invention is suitable for the corn transformant carrying a target exogenous gene and a glufosinate-ammonium resistance gene. The method comprises the following steps: inoculating corn buds obtained by recombinant agrobacterium tumefaciens-mediated transformation to a differential medium containing glufosinate-ammonium or glufosinate-ammonium pesticide with specific concentration, and culturing; in the culture process, corn buds with glufosinate-ammonium resistance genes (co-transformed with target exogenous genes) successfully introduced can normally grow on the culture medium to form positive seedlings with complete plant morphology. Through the differential culture medium, positive seedlings can be screened out from a large number of transformed seedlings in a short time, and it is ensured that high-proportion positive plants are obtained.

The present application belongs to the field of mechanical devices, and relates to a tool for picking Sichuan pepper. After replacing the cutting edge, the function can be extended to picking fruits of rose, dragon head and other thorny plants. The special requirements of Sichuan pepper trees, such as special multi-spine plant morphology, disorderly branches and the fact that the skin of Sichuan pepper particles cannot be damaged during picking to prevent the permeation of pepper oil and affect the quality, are met. The design of the upper and lower three blades of the mechanical Sichuan pepper picker can ensure that 99.4% of the Sichuan pepper particles are picked at one time. The integrated transmission device of 8 movable shafts, 5 fixed shafts and 5 transmission force arms can effectively shorten the force arm and improve the flexibility of operation. The self-locking safety function of the force arm device realized by the movable shafts and the fixed shafts can prevent the cutting edge from hurting the hand when carrying. The spring can automatically rebound by 30° to ensure that the diameter of the picking opening between the upper and lower blades is 7.5 cm. The picking and collecting are synchronized, the pain of being pricked by the pepper thorns is avoided, the efficiency is 2-3 times that of manual picking, and the labor cost is reduced.

The invention relates to the technical field of agricultural information, in particular to an irrigation area soil moisture content monitoring system based on unmanned aerial vehicle remote sensing, and the system comprises a data collection module which synchronously obtains multi-source environment and vegetation data and constructs a terrain model through a model generation module; the crop identification module is used for accurately dividing crop areas based on plant morphology and historical records and generating a plant height data set; the lodging correction module is used for judging and correcting spatial heterogeneous distribution caused by turbulent flow of the unmanned aerial vehicle based on the plant height data; and the irrigation decision-making module outputs a precise irrigation decision-making instruction in combination with the land state, the plant health condition and the weather forecast data. According to the system, when remote sensing control of the unmanned aerial vehicle is executed, collection and fusion of multi-dimensional information of land, plants and environment are accurately obtained, refined and high-robustness moisture state classification is achieved, and a solid technical support is provided for accurate water resource management of modern agriculture.

The present invention discloses cotton GhSNL1 protein and its coding gene and application. The cotton GhSNL1 protein coding gene (Gh_A01G1757; LOC107925613) has a coding sequence of 1290 nucleotides and a protein sequence of 429 amino acids. GhSNL1 Phenotypic analysis of CRISPR‑Cas9 gene editing materials revealed GhSNL1 The knockout material of the gene showed the phenotype of slow fiber initiation, shortened fiber density and regeneration of bolls in mature plants, indicating that GhSNL1 The gene is involved in the development of cotton fibers, promotes the initiation of cotton fibers and affects plant morphology, providing a theoretical basis for cotton variety improvement and can be used to breed new cotton varieties with excellent fiber quality and high yield.