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Detection of Key Organs in Tomato Based on Deep Migration Learning in a Complex Background

Author

Listed:
  • Jun Sun

    (School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China)

  • Xiaofei He

    (School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China)

  • Xiao Ge

    (School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China)

  • Xiaohong Wu

    (School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China)

  • Jifeng Shen

    (School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China)

  • Yingying Song

    (School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China)

Abstract

In the current natural environment, due to the complexity of the background and the high similarity of the color between immature green tomatoes and the plant, the occlusion of the key organs (flower and fruit) by the leaves and stems will lead to low recognition rates and poor generalizations of the detection model. Therefore, an improved tomato organ detection method based on convolutional neural network (CNN) has been proposed in this paper. Based on the original Faster R-CNN algorithm, Resnet-50 with residual blocks was used to replace the traditional vgg16 feature extraction network, and a K-means clustering method was used to adjust more appropriate anchor sizes than manual setting, to improve detection accuracy. The test results showed that the mean average precision (mAP) was significantly improved compared with the traditional Faster R-CNN model. The training model can be transplanted to the embedded system, which lays a theoretical foundation for the development of a precise targeting pesticide application system and an automatic picking device.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:8:y:2018:i:12:p:196-:d:189577
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    Citations

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    Cited by:

    1. Weidong Zhu & Jun Sun & Simin Wang & Jifeng Shen & Kaifeng Yang & Xin Zhou, 2022. "Identifying Field Crop Diseases Using Transformer-Embedded Convolutional Neural Network," Agriculture, MDPI, vol. 12(8), pages 1-19, July.
    2. Chung-Liang Chang & Bo-Xuan Xie & Sheng-Cheng Chung, 2021. "Mechanical Control with a Deep Learning Method for Precise Weeding on a Farm," Agriculture, MDPI, vol. 11(11), pages 1-21, October.
    3. Peng Wang & Jiang Liu & Lijia Xu & Peng Huang & Xiong Luo & Yan Hu & Zhiliang Kang, 2021. "Classification of Amanita Species Based on Bilinear Networks with Attention Mechanism," Agriculture, MDPI, vol. 11(5), pages 1-13, April.
    4. Piotr Boniecki & Krzysztof Koszela & Krzysztof Świerczyński & Jacek Skwarcz & Maciej Zaborowicz & Jacek Przybył, 2020. "Neural Visual Detection of Grain Weevil ( Sitophilus granarius L.)," Agriculture, MDPI, vol. 10(1), pages 1-9, January.
    5. Haiqing Wang & Shuqi Shang & Dongwei Wang & Xiaoning He & Kai Feng & Hao Zhu, 2022. "Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model," Agriculture, MDPI, vol. 12(7), pages 1-23, June.
    6. Piotr Boniecki & Maciej Zaborowicz & Agnieszka Pilarska & Hanna Piekarska-Boniecka, 2020. "Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN," Agriculture, MDPI, vol. 10(6), pages 1-9, June.
    7. 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.

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