116 results about "Plant canopy" patented technology

The invention relates to the technical field of image recognition, and further relates to a phenotypic character image recognition and extraction method for efficient corn breeding. The method comprises the following steps: step 1, acquiring an image of the same plant canopy in a test field by using an unmanned aerial vehicle carrying a multispectral sensor in a plurality of different growth periods from a corn jointing period to a mature period, so as to obtain a normalized grayscale image; 2, performing gradient operation on a target wave band based on the normalized grayscale image to obtain significant edge intensity, and generating a character sensitive probability field of a corresponding time phase; and 3, presetting an optimal observation period for each target character, selecting a pixel set which has a probability value not less than 0.5 and is judged to be the character type from an image of a time phase corresponding to the optimal observation period, and sequentially measuring the total projection area, the average connected domain area and the average leaf angle to obtain a comprehensive breeding sequence value of the character. According to the method, high-precision probability identification of the effective phenotype area of the grain-leaf is realized.

The invention relates to the field of power system supply and demand interaction, and discloses an intelligent greening irrigation system and a water-saving control method, the system comprises a multi-source data acquisition module, a space-time tensor modeling module, a dynamic decision generation module, an edge calculation acceleration module, a distributed execution control module and a drip irrigation module; the method comprises the following steps: collecting multispectral, meteorological and soil moisture data of a plant canopy in real time to generate an original data set; a fusion data set is obtained through space-time alignment and normalization preprocessing; the water demand is predicted based on an LSTM-Transform model, and an irrigation safety interval is screened; the irrigation uniformity and water pressure distribution are simulated and evaluated through multi-physics field coupling; a double-delay DDPG algorithm is used for optimizing valve opening parameters; the model and the execution error are corrected through Bayesian modeling, and an NDVI feedback optimization strategy is combined. According to the invention, a double-delay depth deterministic strategy gradient algorithm is introduced for dynamic optimization of an irrigation strategy, so that the capability of quickly adapting to environmental changes is achieved.

The invention relates to the technical field of intelligent agricultural equipment, in particular to a tomato disease diagnosis method based on multi-modal data analysis, which comprises the following steps of: 1, synchronously acquiring and preprocessing multi-modal data, synchronously triggering a hyperspectral imaging device and a microscopic camera, respectively acquiring a plant canopy hyperspectral image and a stem microscopic image, and acquiring a plant canopy hyperspectral image and a stem microscopic image; meanwhile, temperature, conductivity and dissolved oxygen environment parameters are continuously collected in the root zone; 2, self-adaptive feature extraction and fusion in the growth stage are carried out, reflectivity correction and leaf segmentation are carried out on the hyperspectral image, and leaf surface spectrum curve features are extracted; step 3, hybrid model construction and space-time analysis: constructing a hybrid model comprising spectrum, microscopy and environment analysis networks, and dynamically adjusting each network weight through a gating network; and 4, generating a disease decision. The method can realize accurate, efficient and real-time tomato disease diagnosis, has high practical value, and can effectively improve the disease prevention and control capability in agricultural production.

The invention discloses a wheat growth period identification method based on multi-modal features, and belongs to the technical field of crop growth period identification. Image data features of a wheat growth period are acquired by using a multi-scale convolution sum attention mechanism technology, and the wheat growth period is identified by adding asymmetric multi-channel convolution. Multi-scale extraction of canopy image features is carried out and a nonlinear expression capability is increased at the same time; the attention mechanism module is introduced to enable the network to pay more attention to main effective features, interference features are filtered, the capability of extracting key information from redundant information is enhanced, the recognition accuracy is improved, the problem of canopy image area weight dispersion is solved, the obtained wheat growth period image data features are more accurate and comprehensive, and the wheat growth period image data feature extraction efficiency is improved. According to the image data features of the wheat growth period, on one hand, the image features can be directly classified to identify the growth period, and on the other hand, the image data features of the wheat growth period and serialized environmental data features can be fused; the method realizes small difference and high similarity of morphological characteristics of plant canopies in the transition period of adjacent growth periods, and also can accurately identify the growth period in which the plant canopies are located.

A remote inspection vehicle for inspecting a plant for early drought stress and a neural network model for calculating a score corresponding to a level of early drought stress in the plant. The inspection vehicle may be equipped with an inspection camera having a radiometric infrared sensor and a red, green, and blue light sensor to capture plant images. Image data from the inspection vehicle, as well as soil moisture and calculated CWSI scores, can be used to train the neural network model, deriving weights and biases therefrom using a plurality of neural network layers. Using a plant canopy temperature value, a red raster value, a green raster value, a blue raster value, and a soil moisture value, the trained neural network model can generate a score which can be used to assess the level of early drought stress in a plant.

The invention provides a spraying parameter acquisition method based on plant canopy density and a spraying machine, and relates to the technical field of agricultural plant protection, and the method comprises the steps: determining the point cloud number of a to-be-sprayed plant in a plurality of canopy regions according to the canopy point cloud data of the to-be-sprayed plant; determining a leaf area index of each canopy region of the to-be-sprayed plant according to the number of point clouds of the to-be-sprayed plant in the plurality of canopy regions; determining the canopy density of each canopy region of the to-be-sprayed plant according to the leaf area index of each canopy region of the to-be-sprayed plant; inputting the canopy density of each canopy area of the to-be-sprayed plant into the constructed correlation model to obtain a spraying parameter corresponding to each canopy area of the to-be-sprayed plant; the association model is used for representing an association relationship between the canopy density and the spraying parameters. According to the method and the device, the spraying parameters corresponding to each canopy region are accurately obtained, so that the spraying accuracy and efficiency are improved.

The invention provides a fruit quality detection method, a robot and terminal equipment, and the method is applied to the fruit quality detection robot, and specifically, a plant canopy is scanned to obtain a canopy image so as to recognize a target detection fruit and adjust the collection posture of a mechanical arm, so that target recognition and positioning of the fruit are realized; fruit appearance images of target detection fruits are collected through the visual system, fruit external appearance characteristic detection is achieved, fruit spectrum data of the target detection fruits are collected through the spectrum collection system, fruit internal quality detection is achieved, and detection surrounds the whole growth stage of the whole cherry tomato plant and is continuous; in addition, fruit quality evaluation is carried out based on a deep learning model, and it is ensured that a quality detection task can be rapidly responded and completed in an orchard environment. Therefore, the fruit growth stage can be efficiently, accurately and automatically monitored in the orchard, the overall quality and market competitiveness of the fruits are improved, and agricultural mechanization and automation development is promoted.

The invention provides a plant canopy light energy utilization efficiency estimation method and device. The method comprises the following steps: constructing a group canopy geometric model according to a multi-view RGB image sequence of a target plant; for each single plant three-dimensional model, the specific leaf nitrogen content of each leaf surface element is calculated according to the spectral reflectivity of the corresponding wave band of the leaf surface element, and the net photosynthetic rate of each leaf surface element under the saturated light intensity is calculated according to the specific leaf nitrogen content; calculating the average instantaneous net photosynthetic rate of each blade surface element according to the instantaneous photosynthetically active radiation intensity intercepted by the blade surface element; according to the average instantaneous net photosynthetic rate and the day-by-day cumulant of the PAR interception intensity on the population, the canopy day-by-day light energy utilization efficiency of the target plant is obtained for screening of high-photosynthetic-efficiency germplasm resources or varieties. According to the method, the calculation precision of the photosynthetic productivity of the canopy is improved through surface element scale light-nitrogen fusion, and the estimation accuracy and estimation efficiency of the light energy utilization efficiency of the field plot scale canopy are improved.

The invention provides a numerical simulation and quantitative analysis method for a plant cooling effect, and the method comprises the following steps: extracting morphological parameters of a plant canopy based on tree three-dimensional laser point cloud data, so as to build a tree crown geometric model; building a plant-air multi-physics field coupling model which comprises a fluid control equation, a turbulence model, a plant action source item and a canopy energy balance model; solving the coupling model through a computational fluid dynamics method, and outputting data of a temperature field, a wind speed field and a humidity field around the plant so as to quantitatively analyze the cooling effect.

The invention provides a plant nitrogen content monitoring method and device, electronic equipment, a medium and a program product, and relates to the technical field of plant nitrogen content monitoring. The method comprises the following steps: acquiring a vegetation index and a vegetation coverage of a target plant; based on the vegetation coverage and the vegetation index, constructing a canopy structure correction index of the target plant; inputting the canopy structure correction index into a mapping relation between the canopy structure correction index and the plant nitrogen content to obtain the plant nitrogen content of the target plant; according to the method, the canopy structure correction index of the target plant can be obtained by obtaining the vegetation index and the vegetation coverage of the target plant, so that the influence of the plant canopy structures in different growth stages on the vegetation index is eliminated; further, the plant nitrogen content of the target plant is obtained according to the canopy structure correction index, and accurate monitoring of the plant nitrogen content of the target plant in different growth stages is achieved.

The invention relates to a method for measuring plant height based on a vehicle-mounted laser radar, which comprises the following steps: laser point cloud subscription and coordinate system correction: the laser radar directly acquires reflection point information at the top of a plant canopy in a vertically downward scanning mode; performing terrain adaptive local area point cloud enhancement, detecting the local point cloud density in real time, and adaptively generating new interpolation points, so that the overall distribution of the point cloud is uniform; carrying out multi-plane segmented ground fitting; non-plant points are automatically identified and removed based on filtering of geometric features; point cloud clustering and plant height extraction are carried out, the highest point position of each plant is determined, and the plant height of each crop is accurately calculated; and time sequence Kalman filtering smooth tracking is carried out, and a stable smooth plant height value is estimated and output in real time. According to the method, point density self-adaptive enhancement is carried out on the sparse point cloud area of the plants in the landforms such as slopes and hills, the precision of the plant height calculation reference plane and the subsequent clustering precision are improved, the instantaneous measurement error caused by wind-blown crop shaking and platform movement is effectively reduced, and the measurement stability is greatly improved.

The invention relates to a cotton leaflet character regulation gene and an encoded protein and application thereof, and relates to the technical field of plant genetic engineering, the nucleotide sequence of the gene is shown as SEQ ID NO.1, and the amino acid sequence of the encoded protein is shown as SEQ ID NO.2. According to the invention, a gene capable of regulating and controlling the leaf rolling character of cotton is obtained by cloning from the goblet oilless cotton and is named as a Ghcup gene, and silencing research is carried out on the Ghcup gene in upland cotton goblet oilless cotton by constructing a VIGS vector of the Ghcup gene of the cotton, so that the Ghcup gene can be used for regulating and controlling the leaf rolling character of the cotton. A cotton Ghcup gene overexpression vector is constructed, transgenic overexpression is carried out in normal broad-leaf upland cotton Xinluzhong 75, and results show that the curly phenotype of an oilless goblet-leaf cotton leaf of a silent Ghcup gene disappears, the overexpression of the gene causes the upward curly shape of the Xinluzhong 75 cotton leaf to be similar to a cup shape, and the cotton Ghcup gene overexpression vector is used for transgenosis overexpression in the normal broad-leaf upland cotton Xinluzhong 75. Therefore, the Ghcup gene has great application potential in the aspects of improving the leaf shape and the plant canopy structure of the cotton, improving the photosynthetic efficiency of the plant and the like.

The invention discloses a garden intelligent spraying control method and system based on computer vision, and belongs to the technical field of computer vision, and the method specifically comprises the steps: obtaining plant canopy image data and multi-dimensional environment parameters through a sensing device disposed in a garden area; analyzing the image by using a pre-trained disease and insect pest recognition model, and generating an initial spraying demand vector containing disease and insect pest categories, infection grades and spatial position information; constructing a three-dimensional temperature field reflecting vertical temperature distribution from the canopy to the earth surface based on the temperature sensor array, and extracting temperature gradient data; through a pre-established temperature-pesticide effect coupling model, optimizing the initial spraying demand vector according to the real-time temperature data, and determining the optimal spraying amount and the operation opportunity; a spraying instruction is generated, and an executing mechanism is driven to achieve precise spraying. According to the invention, deep fusion of disease and pest identification and environment adaptive spraying is realized, the pesticide utilization rate and the control effect are improved, and pesticide liquid waste and environmental pollution are reduced.

The present invention discloses a multi-scenario self-propelled crop phenotype high-throughput non-destructive acquisition equipment, which belongs to the field of crop phenotype detection. The equipment includes a mobile robot platform, a satellite positioning module, a three-dimensional visual sensor, an edge computing device and a multi-sensor phenotypic data acquisition unit. The mobile robot platform adopts a four-wheel independent drive and independent steering mode, and the height is adjustable; the satellite positioning module controls the mobile robot platform to move to the target plot, and the edge computing device dynamically adjusts the steering angle of the mobile robot platform and the upper platform height according to the plant canopy depth image and three-dimensional point cloud information collected by the three-dimensional visual sensor; the edge computing device analyzes each modal phenotypic sensor data collected by the multi-sensor phenotypic data acquisition unit to obtain a variety of phenotypic data; it can also realize the automatic mapping of multi-modal phenotypic sensor data and target plot number, as well as line break operations. The present invention realizes multi-scenario self-propelled crop full growth period phenotypic high-throughput non-destructive measurement.

The invention relates to the technical field of image processing, in particular to a landscaping maintenance intelligent monitoring method and device and a storage medium, and the method comprises the steps: carrying out the type division of each pixel block; generating a garden protection strategy based on the near-infrared band reflectivity acquired in the monitoring period, the gray level image contrast of the plant canopy image and the type of each pixel block; acquiring soil parameters, meteorological parameters and plant physiological parameters in the monitoring period; standardizing the acquired soil parameters, meteorological parameters and plant physiological parameters, and updating the generation process of the garden protection strategy by environmental pressure; counting the number of sound anomaly events based on the sound pulse signals acquired in the monitoring period, calculating a sound anomaly proportion, and determining an adjustment weight; and constructing a health index based on the insect pest index, the environmental factor and the sound anomaly proportion, and performing graded early warning to the user. According to the invention, the monitoring efficiency of landscaping maintenance is effectively improved.

The invention discloses a moisture prediction method and system based on airborne multispectral sensitive wave band combination, and relates to the technical field of agricultural remote sensing and image processing, and the method comprises the steps: on the basis of the response characteristics of soil and plant canopy reflection spectrums to moisture, combining with the physical mechanism of O-H bond vibration in water molecules, and predicting the moisture content of the soil and plant canopy reflection spectrums; determining the bandwidths of all central wavelengths by considering the influence of atmospheric water vapor on the near infrared spectrum; generating reflection spectrums of the soil and the vegetation canopies, generating a spectral response function by using the relationship between the central wavelength and the bandwidth, converting the simulated spectrums into a moisture sensitive wave band, and calculating the reflectivity and the spectral index; and collecting reflection spectrum images of the soil and the vegetation canopies at the selected positions, and inputting the reflection spectrum images into the moisture prediction model to predict moisture. By optimizing wave band selection and bandwidth allocation, the problems that when an existing airborne multispectral sensor executes a moisture monitoring task, the pertinence of wave band selection is insufficient, and the monitoring precision is not high can be effectively solved.

A system for providing horticulture lighting for a crop in a horticulture production facility is provided, including light sources, sensors, and a controller. The light sources include red and far-red light sources, and generate local light illuminating a plant canopy of the crop. The controller (1) periodically captures, via the sensors, imaging and climate data between a first and second date; (2) generates, via a plant growth model, an estimated plant canopy at a future date based on the imaging and climate data; (3) receives a light reception map of the plant canopy; (4) generates, based on the light reception map and the estimated plant canopy, a growth inducing target map; (5) generates, based on the growth inducing target map, a growth inducing lighting recipe including an optimized red to far-red light ratio; (6) adjusts, via the light sources, the local light based on the growth inducing lighting recipe.

1. The name of the design product: canopy analyzer. 2. The use of the design product: the design product is used in agricultural production and agricultural scientific research, for investigating canopy light energy resources, measuring the interception of light in plant canopy, and studying the relationship between crop growth, yield, quality and light energy utilization. 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 present application relates to the technical field of agricultural equipment, and discloses an alfalfa planting and topdressing device and a topdressing method, which comprise: a lightweight mobile frame, a connecting head connected with a power end, a front wheel and an infrared laser radar arranged at the front end, and an adjustable rear wheel group arranged at the rear end; a multi-modal detection unit integrated with the front wheel, a detection surface of the multi-modal detection unit and a scanning direction of the infrared laser radar form a spatial orthogonal layout, and the multi-modal detection unit comprises: a rolling trigger type gamma ray sensor which periodically emits rays while rotating with the front wheel; through the cooperative work of the multi-modal detection unit, the present device realizes comprehensive perception of soil deep nutrients, ion concentration, surface moisture and plant canopy, combines the terrain scanning function of the infrared laser radar, and dynamically generates a fertilizer formula and adjusts fertilization parameters, so that the precision and adaptability of fertilization are significantly improved, and the limitations of traditional single detection means are avoided.

The invention relates to the technical field of garden potting detection, in particular to an automatic pest and disease detection device and method for garden potting, and the device comprises a vehicle frame, a lifting assembly, a rotating disc and a rotating arm, and the rotating arm comprises an adjusting member, a supporting arm, a shell, a multispectral camera and a visible light camera. The device is moved to a to-be-detected potted plant, at the moment, the supporting arm is located on the side of the periphery of a canopy of the potted plant, the multispectral camera and the visible light camera face the potted plant, the rotating disc is driven to rotate through the lifting assembly, and then the supporting arm, the shell, the multispectral camera and the visible light camera are driven to do circular motion; the multispectral camera and the visible light camera are used for acquiring images, respectively acquiring spectral reflection data and morphological images of the potted plant in different directions, and sending the data to the processing unit; and the processing unit outputs a visual result to the terminal, so that accurate recognition and positioning of the diseases and insect pests are realized.

Systems and methods for illuminating a plurality of plants (11) are disclosed. The system (1) comprises an illumination system (3) configured to illuminate a plurality of plants (11) with radiation having one or more controllable radiation properties, and a data processing system (100) communicatively connected to the illumination system (3). A data processing system (100) comprises an input interface for receiving a signal indicative of a status of a plant canopy, an output interface for sending a control signal to an illumination system, and a processor. The processor is configured to: receive a signal indicative of a status of a plant canopy formed by a plurality of plants (11); determining whether the plant canopy is closed based on the received signal indicative of the status of the plant canopy; and sending a control signal to the illumination system (3), the control signal being based on the determined canopy closure. The illumination system (3) is configured to switch from illuminating the plurality of plants (11) according to a first radiation recipe to illuminating the plurality of plants (11) according to a second radiation recipe different from the first radiation recipe based on the received control signal.

Flexible diffuse-reflective smart camber (FDRSC) devices are disclosed herein. The devices include UV-C lights sources and an irradiance chamber. The irradiance chamber is open on at least one end through which the plants to be treated are exposed to the UV-C irradiance. Further, the inner surfaces of the irradiance chamber are covered by or consist of a diffuse reflective material that passively intensifies and uniformly distributes the UV-C irradiance to penetrate plant canopies. Also described herein are autonomous vehicles for moving the FDRSC devices across fields of crops and other plants, which is controlled in real time by a dosing control system. Also described herein are methods of in-situ UV-C treatment of plants using the FDRSC devices.

The present invention relates to the technical field of greenhouse plant protection machinery, and in particular to a facility self-propelled powder spraying equipment and a powder spraying method. The facility self-propelled powder spraying equipment provided by the present invention comprises a self-propelled platform, a lifting mechanism, a height detection component and a powder spraying device. The lifting mechanism is arranged on the self-propelled platform, and the lifting mechanism has a movable part that can be lifted and lowered; the height detection component is arranged on the movable part, and the height detection component is used to detect the height of the plant canopy, and the height detection component is electrically connected to the lifting mechanism through a controller; the powder spraying device is arranged on the movable part, and the powder spraying device has a powder spraying output end for spraying powder on the plant canopy; wherein the lifting mechanism is lifted and lowered according to the height information of the plant canopy detected by the height detection component, so that the powder spraying output end of the powder spraying device sprays powder on the plant canopy from above, and the facility self-propelled powder spraying equipment provided by the present invention can adjust the powder spraying height according to the height of the plant, thereby improving the uniformity of the powder spraying on the plant.

One variation of a method includes accessing an image of a plant canopy in an environment inhabited by: a population of microbe sensors of a microbe type including a microbe promoter-reporter pair configured to generate microbe-reporter signals representing presence of a stressor in the environment; and a set of sensor plants of a sensor plant type including a plant promoter-reporter pair configured to signal presence of microbe-reporter signals at the set of sensor plants. The method further includes: accessing a reporter model linking features extracted from images of sensor plants of the sensor plant type to pressures of the set of stressors based on plant-reporter signals generated by the plant promoter-reporter pair and microbe types of microbe sensors inhabiting the environment; and interpreting a pressure of the stressor in the environment based on the reporter model, the microbe type, and features extracted from the image.

The application provides a method for identifying potato drought-resistant genes using hyperspectral, and relates to the technical field of crop drought-resistant gene identification, and comprises the following steps: setting a drought group and a control group, synchronously acquiring plant canopy hyperspectral images and transcriptome data and establishing a correlation data set; extracting spectral features, screening drought response candidate genes; calculating the correlation coefficient of each candidate gene and the spectral features based on Spearman correlation analysis, and determining the primary gene set according to whether the average value of a plurality of positions after the absolute value is sorted exceeds a threshold value; constructing a random forest regression model with the spectral features as input and the expression amount of the primary gene as output, predicting the expression amount through a verification sample, and screening target drought-resistant genes according to the coefficient of determination and the prediction error; constructing a target gene overexpression plant, measuring the relative change rate of physiological indexes and the change multiple of gene expression amount under drought, calculating a comprehensive function verification index, and determining the gene as a drought-resistant gene if the index exceeds a threshold value.

1. Name of the design product: Upper Wireless Canopy Analyzer. 2. Use of the design product: This design product is used for the analysis of plant canopies. 3. Design key points of the design product: Lies in the shape. 4. Picture or photo that best shows the design key points: Stereogram.

A new and distinct variety of strawberry plant named ‘DrisStrawOneHundredNine’, particularly selected for its sweet flavor, overall fruit quality, long trusses, and open plant canopy, is disclosed.

The invention discloses a vision-based plant canopy coverage rate calculation method, system and device. The method comprises the following steps: acquiring an initial top surface plant image, an initial side surface plant image and a rotary side surface plant image through a top surface image acquisition device and a side surface image acquisition device; through plant leaf extraction, an initial side leaf image, a rotary side leaf image and a top leaf image are obtained; through skeleton line extraction, obtaining an initial skeleton line image and a rotating skeleton line image, and further obtaining plant stalk coordinates in the initial top surface plant image; acquiring an included angle with a plane where the side image acquisition device is located through the leaf vertex coordinate set and the plant stalk coordinate; and rotating the to-be-detected plant through the included angle, carrying out image acquisition to obtain a top surface plant image set, and carrying out projection and area analysis to obtain the plant canopy coverage rate. According to the invention, images of the side surfaces and the top surfaces of the plants are collected and analyzed, and the calculation accuracy and the calculation efficiency are improved.

1. The name of the design product: measurement box. 2. The use of the design product: used for multi-modal plant canopy photosynthetic phenotype measurement experiment. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view. 5. Other circumstances that need to be explained: the top cover plate and the A part of each side plate of the box are transparent materials.

A strawberry treatment platform includes an outer housing with at least one UV-C light array enclosed in the outer housing, and at least one strawberry plant manipulating assembly that extends downwardly from the outer housing. The strawberry treatment platform is structured so that, as the strawberry treatment platform moves across a strawberry plant bed, the plant manipulating assembly combs through the strawberry plants and creates a canopy gap in the strawberry bed plant canopy so that radiation from the UV-C light array treats the strawberry plants in the strawberry plant bed.