首页|A visual identification method for the apple growth forms in the orchard
A visual identification method for the apple growth forms in the orchard
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NSTL
Elsevier
? 2022 Elsevier B.V.The work aimed at the visual identification of the growth forms of fruits to facilitate the subsequent use of different harvesting mechanisms for different growth forms of fruits by robots. The improved YOLOv5 deep learning algorithm was used to propose a visual identification method for the growth forms of apples in the orchard. Specifically, the feature extraction module of the YOLOv5 algorithm imitated the BiFPN model to propose the BiFPN-S structure. The spread of features and feature reuse were enhanced to better fit features. The improved algorithm was called YOLOv5-B. The network SiLU activation function was replaced with the ACON-C activation function to improve its network performance. The COCO data set was used to pre-train the network, and then the data set of the work was trained by the transfer learning method. After the training, the generated optimal model was applied for the visual-identification test of the growth of apple fruits. The results showed that the improved algorithm model considered high accuracy and real-time performance, with the map reaching 98.4% and the F1 value of 0.928. The average accuracy of identifying the growth forms of apples for the test set was 98.45%, and the processing speed was 71 FPS.
AppleDeep learningGrowth formYolov5
Lv J.、Lu W.、Yang B.、Zou L.、Ma Z.、Xu H.、Han Y.、Xu L.、Rong H.
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School of Microelectronics and Control Engineering Changzhou University
School of Computer Science and Artificial Intelligence Changzhou University
School of Equipment Engineering Jiangsu Urban and Rural Construction College
School of Mechanical Engineering and Rail Transit Changzhou University
NSTL
Elsevier
