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Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism

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  • Peng Wang

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China
    Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Xianyang 712100, China
    Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services, Xianyang 712100, China)

  • Tong Niu

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China
    Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Xianyang 712100, China
    Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services, Xianyang 712100, China)

  • Dongjian He

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China
    Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Xianyang 712100, China
    Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Services, Xianyang 712100, China)

Abstract

The information of tomato young fruits acquisition has an important impact on monitoring fruit growth, early control of pests and diseases and yield estimation. It is of great significance for timely removing young fruits with abnormal growth status, improving the fruits quality, and maintaining high and stable yields. Tomato young fruits are similar in color to the stems and leaves, and there are interference factors, such as fruits overlap, stems and leaves occlusion, and light influence. In order to improve the detection accuracy and efficiency of tomato young fruits, this paper proposes a method for detecting tomato young fruits with near color background based on improved Faster R-CNN with an attention mechanism. First, ResNet50 is used as the feature extraction backbone, and the feature map extracted is optimized through Convolutional Block Attention Module (CBAM). Then, Feature Pyramid Network (FPN) is used to integrate high-level semantic features into low-level detailed features to enhance the model sensitivity of scale. Finally, Soft Non-Maximum Suppression (Soft-NMS) is used to reduce the missed detection rate of overlapping fruits. The results show that the mean Average Precision (mAP) of the proposed method reaches 98.46%, and the average detection time per image is only 0.084 s, which can achieve the real-time and accurate detection of tomato young fruits. The research shows that the method in this paper can efficiently identify tomato young fruits, and provides a better solution for the detection of fruits with near color background.

Suggested Citation

  • Peng Wang & Tong Niu & Dongjian He, 2021. "Tomato Young Fruits Detection Method under Near Color Background Based on Improved Faster R-CNN with Attention Mechanism," Agriculture, MDPI, vol. 11(11), pages 1-13, October.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:11:p:1059-:d:666778
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    References listed on IDEAS

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    1. Jun Sun & Xiaofei He & Xiao Ge & Xiaohong Wu & Jifeng Shen & Yingying Song, 2018. "Detection of Key Organs in Tomato Based on Deep Migration Learning in a Complex Background," Agriculture, MDPI, vol. 8(12), pages 1-15, December.
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    Cited by:

    1. Meftah Salem M. Alfatni & Siti Khairunniza-Bejo & Mohammad Hamiruce B. Marhaban & Osama M. Ben Saaed & Aouache Mustapha & Abdul Rashid Mohamed Shariff, 2022. "Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis," Agriculture, MDPI, vol. 12(9), pages 1-28, September.
    2. Jingmin Shi & Fanhuai Shi & Xixia Huang, 2023. "Prediction of Maturity Date of Leafy Greens Based on Causal Inference and Convolutional Neural Network," Agriculture, MDPI, vol. 13(2), pages 1-16, February.

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