Guangdong Polytechnic Normal University

Zhiping Tan

Yu Lin Tang

Jiahao Li

Qiwei Guo

Jiepeng Yang

Huasheng Huang

Yiqing Fu

Jinfei Zhao

Shaoming Luo

Weizhao Chen

Jiajun Zhuang

Chaojun Hou

Aimin Miao

Xincai Zhuang

Xiaodi Zhang

The present invention relates to the technical field of field crop cultivation, more particularly to a training method, an evaluation method, an electronic device and a storage medium. According to the present invention, a multispectral three-dimensional point cloud map is obtained through depth information and multispectral information, and the multispectral three-dimensional point cloud map is analyzed by utilizing an FVNet three-dimensional target detection algorithm, thereby acquiring crop feature information. Thus, more comprehensive crop information can be obtained, and a crop state evaluation model constructed based on an artificial neural network can be further trained with the crop feature information.

Training method, evaluation method, electronic device and storage medium

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

Disclosed is a method for detecting an infection stage of anthracnose pathogen with pre-analysis capacity, comprising: obtaining a plurality of sample sensing data sequences; obtaining a sample citrus leaf image; obtaining a first prediction result; if the first prediction result is that the sample citrus crop is not infected by anthracnose, obtaining sample Raman spectral data and sample hyperspectral data; obtaining a first judgment result, and obtaining a second judgment result; performing labeling to obtain second training data; training a neural network model to obtain a second anthracnose prediction model; obtaining a plurality of to-be-analyzed sensing data sequences; obtaining a to-be-analyzed citrus leaf image; obtaining a second prediction result; if the second prediction result is that the to-be-analyzed citrus crop is not infected by anthracnose, obtaining a third prediction result.

Method for detecting infection stage of anthracnose pathogenic with pre-analysis capacity

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Guangzhou, China

Zhiping Tan