Guangdong Polytechnic Normal University

Zhongkai University of Agriculture and Engineer

Chaojun Hou

Yu Lin Tang

Shaoming Luo

Jiajun Zhuang

Jiahao Li

Qiwei Guo

Huasheng Huang

Jiepeng Yang

Aimin Miao

Xincai Zhuang

Yiqing Fu

Xuan Chu

Jinfei Zhao

Weizhao Chen

Xiaodi Zhang

Weifeng Huang

Zhiping Tan

Zhenyu Zhong

Jintian Lin

Lixue Zhu

Xing Zhu

Huan Lei

Zaili Chen

A fruit picking method based on a three-dimensional parameter prediction model for a fruit comprises: performing first-time image acquisition processing on a to-be-picked fruit to obtain a first image; determining a first range; controlling a manipulator to perform first-time moving processing; performing intermittent gas injection treatment to lead to forced vibration of the to-be-picked fruit; performing second-time image acquisition processing many times to obtain a plurality of second images; screening out, by taking the first image as an reference object, two appointed second images deviating from an equilibrium position to the maximum extent; jointly inputting the images into a preset three-dimensional parameter prediction model for the fruit so as to obtain predicted three-dimensional parameters; controlling the manipulator to perform second-time moving processing; and performing cutting processing on a fruit stem position to make the to-be-picked fruit fall onto the manipulator.

Fruit picking method based on three-dimensional parameter prediction model for fruit

Disclosed is a fruit picking method based on a visual servo control robot, comprising: placing a throwing apparatus and a fixed photosensitive device at a first position to obtain a fixed photosensitive image; generating a first throwing path, a second throwing path, and a third throwing path; arranging recovery apparatuses; performing simultaneous rotational throwing processing to throw a first wireless photosensitive device, a second wireless photosensitive device, and a third wireless photosensitive device; receiving a first photosensitive image sequence and a second photosensitive image sequence of the first wireless photosensitive device and the second wireless photosensitive device; receiving a third photosensitive image sequence of the third wireless photosensitive device; generating a spatial position of a fruit on a to-be-picked fruit tree; and performing fruit picking processing using the visual servo control robot according to the spatial position of the fruit.

Fruit picking method based on visual servo control robot

A citrus identification method comprises: performing first-time image acquisition processing on a target citrus tree to obtain a first image; inputting the first image into a first citrus fruit identification model to be processed to obtain a first identification result sequence; performing area interception processing on the first image to obtain a citrus fruit area; obtaining roundness integrity numerical values; selecting an appointed roundness integrity numerical value, and acquiring a defect position of an appointed citrus fruit in the first image; determining a first spatial range and a second spatial range; performing both first spray injection treatment and second spray injection treatment; performing second-time image acquisition processing to obtain a second image; inputting the second image into a second citrus fruit identification model to be processed to obtain a second identification result; and generating a citrus fruit identification result.

Citrus identification method based on roundness integrity correction

Disclosed is an obstacle avoiding method and apparatus for an unmanned aerial vehicle based on a multi-signal acquisition and route planning model. The method comprises: conducting signal acquisition processing on a first environmental area to obtain an initial millimeter-wave radar signal, an initial laser radar signal, an initial image signal and an initial ultrasonic signal; generating an initial three-dimensional environmental model according to a preset dynamic environment real-time modeling method; acquiring a motion parameter and a body shape parameter of the unmanned aerial vehicle and inputting the parameters into an initial route planning model corresponding to the initial three-dimensional environmental model based on a genetic algorithm to process so as to obtain an output of the initial route planning model; judging whether the output is capable of avoiding an obstacle; if yes, generating an obstacle avoiding flight instruction to require the unmanned aerial vehicle to fly through the first environmental area.

Obstacle avoiding method and apparatus for unmanned aerial vehicle based on multi-signal acquisition and route planning model

The present application discloses a multilevel cooperation-based unmanned aerial vehicle spraying operation method. The method includes: acquiring a first sensing signal set; inputting the first sensing signal set into a prescription map generation model to acquire a first prescription map; generating the type of liquid medicine and corresponding quantity required by a to-be-treated plant, and selecting a specified unmanned aerial vehicle; transmitting a start instruction to make the specified unmanned aerial vehicle fly to an initial position; acquiring a second sensing signal set; inputting a motion parameter and a position parameter of the unmanned aerial vehicle, the remaining liquid storage quantity of the plurality of liquid medicine storage tanks and the second sensing signal set into a spraying strategy generation module for processing to acquire a first spraying strategy; performing aerial spraying operation performing cooperative ground spraying operation to realize multilevel fine spraying operation based on the prescription map.

Multilevel cooperation-based unmanned aerial vehicle spraying operation method and device

A method for improving health of an elderly person based on a 5G signal transmission technology includes: acquiring by a real terminal a first sensing signal set; modifying the first sensing signal set to obtain a second sensing signal set; transmitting the second sensing signal set to a predetermined intermediate terminal according to a predetermined 5G signal transmission technology; correspondingly transmitting by the intermediate terminal the second sensing signal set to a predetermined simulation apparatus, acquiring by the intermediate terminal physiological feature signals to obtain a physiological feature signal set; if the physiological feature signal set does not exceed a preset standard, then transmitting the second sensing signal set to an elderly-person terminal; correspondingly transmitting by the elderly-person terminal the second sensing signal set to a second restorer cluster.

Method for improving health of elderly person based on 5G signal transmission technology

Disclosed is a crop yield prediction method and device. The method includes: transmitting an image acquisition instruction to a preset aircraft terminal to acquire a ground image; transmitting a driving instruction at the same time, wherein a distance between a first rail vehicle and a second rail vehicle is inversely proportional to the driving time, a plurality of light channels are embedded between a first rail and a second rail, and a light transmittance of the light channels is able to be affected by the external environment to change; transmitting a light emitting instruction and recording initial data when multiple beams of light are emitted; transmitting a light receiving instruction and recording final data when the multiple beams of light are received; and inputting the ground image, the initial data and the final data into a crop yield prediction model for processing.

Crop yield prediction method and device based on multi-level variable analysis

The present application relates to the field of plant protection unmanned aerial vehicles, and discloses an obstacle-avoiding spraying method and device for a plant protection unmanned aerial vehicle, a computer device and a storage medium. Through special designs, the plant protection unmanned aerial vehicle is capable of avoiding obstacles for spraying operation, and the efficiency of the spraying operations is improved on the premise of preventing from blockage of the obstacles. It should be noted that the obstacle-avoiding spraying method for the plant protection unmanned aerial vehicle of the present application needs to be implemented jointly by three unmanned aerial vehicles, but the special part is that it is unnecessary for unmanned aerial vehicle terminals corresponding to the three unmanned aerial vehicles to communicate and interact with each other, such that the method has a wider application range and is easier to implement.

Obstacle-avoiding spraying method and device for plant protection unmanned aerial vehicle, computer device and storage medium

The present invention relates to a pest monitoring method based on machine vision. The method includes the following steps: arranging a pest trap at a place where pests gather, and setting an image acquisition device in front of the pest trap to acquire an image; identifying a pest in the acquired image, and obtaining a number of pests; extracting multiple suspicious pest images from a region of each identified pest in the image, and determining identification accuracy of each suspicious pest image, if the number of pests is greater than or equal to a preset threshold for the number of pests; and calculating a predicted level of pest damage based on the number of pests and the identification accuracy of each suspicious pest image. The present invention acquires a pest image automatically through the image acquisition device in front of the pest trap.

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Guangzhou, China

Chaojun Hou