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Weed Detection in Maize Fields by UAV Images Based on Crop Row Preprocessing and Improved YOLOv4

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  • Haotian Pei

    (Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
    Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)

  • Youqiang Sun

    (Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)

  • He Huang

    (Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)

  • Wei Zhang

    (Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
    Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)

  • Jiajia Sheng

    (Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
    Science Island Branch of Graduate School, University of Science and Technology of China, Hefei 230026, China)

  • Zhiying Zhang

    (Institute of Science and Technology Information, Jiangsu University, Zhenjiang 212013, China)

Abstract

Effective maize and weed detection plays an important role in farmland management, which helps to improve yield and save herbicide resources. Due to their convenience and high resolution, Unmanned Aerial Vehicles (UAVs) are widely used in weed detection. However, there are some challenging problems in weed detection: (i) the cost of labeling is high, the image contains many plants, and annotation of the image is time-consuming and labor-intensive; (ii) the number of maize is much larger than the number of weed in the field, and this imbalance of samples leads to decreased recognition accuracy; and (iii) maize and weed have similar colors, textures, and shapes, which are difficult to identify when an UAV flies at a comparatively high altitude. To solve these problems, we propose a new weed detection framework in this paper. First, to balance the samples and reduce the cost of labeling, a lightweight model YOLOv4-Tiny was exploited to detect and mask the maize rows so that it was only necessary to label weeds on the masked image. Second, the improved YOLOv4 was used as a weed detection model. We introduced the Meta-ACON activation function, added the Convolutional Block Attention Module (CBAM), and replaced the Non-Maximum Suppression (NMS) with Soft Non-Maximum Suppression (Soft-NMS). Moreover, the distributions and counts of weeds were analyzed, which was useful for variable herbicide spraying. The results showed that the total number of labels for 1000 images decrease by half, from 33,572 to 17,126. The improved YOLOv4 had a mean average precision ( mAP ) of 86.89%.

Suggested Citation

  • Haotian Pei & Youqiang Sun & He Huang & Wei Zhang & Jiajia Sheng & Zhiying Zhang, 2022. "Weed Detection in Maize Fields by UAV Images Based on Crop Row Preprocessing and Improved YOLOv4," Agriculture, MDPI, vol. 12(7), pages 1-18, July.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:7:p:975-:d:856993
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    References listed on IDEAS

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    1. Nur Adibah Mohidem & Nik Norasma Che’Ya & Abdul Shukor Juraimi & Wan Fazilah Fazlil Ilahi & Muhammad Huzaifah Mohd Roslim & Nursyazyla Sulaiman & Mohammadmehdi Saberioon & Nisfariza Mohd Noor, 2021. "How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?," Agriculture, MDPI, vol. 11(10), pages 1-27, October.
    2. Nahina Islam & Md Mamunur Rashid & Santoso Wibowo & Cheng-Yuan Xu & Ahsan Morshed & Saleh A. Wasimi & Steven Moore & Sk Mostafizur Rahman, 2021. "Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm," Agriculture, MDPI, vol. 11(5), pages 1-13, April.
    3. Brahim Jabir & Noureddine Falih, 2022. "A New Hybrid Model of Deep Learning ResNeXt-SVM for Weed Detection: Case Study," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 18(2), pages 1-18, April.
    4. Xiaoyu Li & Yuefeng Du & Lin Yao & Jun Wu & Lei Liu, 2021. "Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm," Agriculture, MDPI, vol. 11(12), pages 1-17, December.
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    Cited by:

    1. Colette de Villiers & Cilence Munghemezulu & Zinhle Mashaba-Munghemezulu & George J. Chirima & Solomon G. Tesfamichael, 2023. "Weed Detection in Rainfed Maize Crops Using UAV and PlanetScope Imagery," Sustainability, MDPI, vol. 15(18), pages 1-22, September.

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