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Recognition for Stems of Tomato Plants at Night Based on a Hybrid Joint Neural Network

Author

Listed:
  • Rong Xiang

    (College of Quality and Safety Engineering, China Jiliang University, No. 258, Xueyuan Street, Higher Education Zone of Xiasha, Hangzhou 310018, China)

  • Maochen Zhang

    (College of Quality and Safety Engineering, China Jiliang University, No. 258, Xueyuan Street, Higher Education Zone of Xiasha, Hangzhou 310018, China)

  • Jielan Zhang

    (College of Quality and Safety Engineering, China Jiliang University, No. 258, Xueyuan Street, Higher Education Zone of Xiasha, Hangzhou 310018, China)

Abstract

Recognition of plant stems is vital to automating multiple processes in fruit and vegetable production. The colour similarity between stems and leaves of tomato plants presents a considerable challenge for recognising stems in colour images. With duality relation in edge pairs as a basis, we designed a recognition algorithm for stems of tomato plants based on a hybrid joint neural network, which was composed of the duality edge method and deep learning models. Pixel-level metrics were designed to evaluate the performance of the neural network. Tests showed that the proposed algorithm has performs well at detecting thin and long objects even if the objects have similar colour to backgrounds. Compared with other methods based on colour images, the hybrid joint neural network can recognise the main and lateral stems and has less false negatives and positives. The proposed method has low hardware cost and can be used in the automation of fruit and vegetable production, such as in automatic targeted fertilisation and spraying, deleafing, branch pruning, clustered fruit harvesting and harvesting with trunk shake, obstacle avoidance, and navigation.

Suggested Citation

  • Rong Xiang & Maochen Zhang & Jielan Zhang, 2022. "Recognition for Stems of Tomato Plants at Night Based on a Hybrid Joint Neural Network," Agriculture, MDPI, vol. 12(6), pages 1-21, May.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:6:p:743-:d:822910
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    Citations

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    Cited by:

    1. Peichao Cong & Hao Feng & Kunfeng Lv & Jiachao Zhou & Shanda Li, 2023. "MYOLO: A Lightweight Fresh Shiitake Mushroom Detection Model Based on YOLOv3," Agriculture, MDPI, vol. 13(2), pages 1-23, February.
    2. Ranbing Yang & Yuming Zhai & Jian Zhang & Huan Zhang & Guangbo Tian & Jian Zhang & Peichen Huang & Lin Li, 2022. "Potato Visual Navigation Line Detection Based on Deep Learning and Feature Midpoint Adaptation," Agriculture, MDPI, vol. 12(9), pages 1-17, September.

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