温室樱桃番茄果实深度学习识别模型研究

doi:10.12190/j.issn.2096-1197.2023.05.12

Abstract

Abstract:

【Objective】Select deep learning cherry tomato fruit recognition models to achieve rapid and accurate detection of greenhouse cherry tomato fruits.【Methods】The training data set was constructed by collecting the cherry tomato fruit samples and annotating the data. YOLOv4 and YOLO v4-Tiny algorithms were used for model training. The precision，recall，mean average precision and comprehensive evaluation index F1 value of the trained models were analyzed.【Results】The precision of cherry tomato fruit recognition prediction was 100% and 96.84% for the trained YOLOv4 and YOLOv4-Tiny models，the recall rates were 91.12% and 90.53%，average precision was 96.32% and 94.18%，and the comprehensive evaluation index F1 values were 0.95 and 0.94，respectively.【Conclusion】YOLOv4 algorithm model was obviously better than YOLOv4-Tiny，could achieve accurate cherry tomato fruit recognition.

Key words: Deep learning, Cherry tomato, Fruit, Recognition, YOLOv4, YOLOv4-Tiny

CLC Number:

TP391.41

SHEN Chao, XIA Xiubo, YANG Wei, ZHANG Huanchun, LI Minzan. Research on deep learning recognition model of greenhouse cherry tomato fruit[J].Journal of Northern Agriculture, 2023, 51(5): 114-122.

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https://journal30.magtechjournal.com/bfnyxb/EN/Y2023/V51/I5/114

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References 27

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Research on deep learning recognition model of greenhouse cherry tomato fruit

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