Learning an optical filter for green pepper automatic picking in agriculture

Liu, Xinzhi; Yu, Jun; Kurihara, Toru; Li, Ké; Shu, Zhan

doi:10.1016/j.compag.2021.106521

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…In real life, when observing things, human beings generally focus on the obvious characteristic parts of things quickly, while ignoring some irrelevant parts that affect the judgment results, so as to obtain information quickly and accurately and make accurate judgments. Attention mechanism should be added to neural algorithm because of this phenomenon, and the performance of network model will be greatly improved by adding only a few algorithms [10,13,21].…”

Section: Cbam Attenti On Mechanismmentioning

confidence: 99%

Pepper Target Recognition and Detection Based on Improved YOLO v4

Tan,

Chen,

Sun

et al. 2023

ITC

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In order to improve visual recognition accuracy of pepper and provide reliable technical support for agricultural production, an improved YOLOv4 algorithm for pepper target recognition and detection was proposed in this paper. By adding Mosaic data enhancement and CBAM (Conventional block attention module) attention mechanism to the primitive character extraction network, the method enhanced the learning ability of the target detection algorithm, made the network effectively suppress the interference features, and increased the attention to effective features. To improve the accuracy of identification. The improved network model was trained, verified and tested on the self-made data set. The results showed that the proposed algorithm could effectively improve the accuracy of pepper recognition under natural light, and finally improved the mean Average Precision (mAP) of the existing YOLOv4 algorithm from 88.95% to 98.36%.

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Section: Cbam Attenti On Mechanismmentioning

confidence: 99%