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Maize tassel number and tasseling stage monitoring based on near-ground and UAV RGB images by improved YoloV8

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Abstract

The monitoring of the tassel number and tasseling time reflects the maize growth and is necessary for crop management. However, it mainly depends on field observations, which is very labor intensive and may be biased by human errors. Tassel detection remains challenging due to the varying appearance of tassels across maize varieties, tasseling stages, and spatial resolutions. Moreover, the capability of the deep learning model for monitoring tassel number change and the time of entering tasseling stage has not been explored. In this study, we propose a novel approach for fast tassel detection using PConv (Partial Convolution) within YoloV8 series, named PConv-YoloV8 series. Compared to seven state-of-the-art deep learning methods, PConv-YoloV8 × 6 best trades off detection accuracy with the number of parameters (Parameters = 52.50 MB, AP = 0.950, R2 = 0.92, rRMSE = 9.08%). The potential of PConv-YoloV8 × 6 to provide an accurate detection of tassels in complex situations from near-ground and UAV images were comprehensively studied. PConv-YoloV8 × 6 maintained an excellent detection accuracy for maize at different tasseling stages (AP = 0.826–0.972, R2 = 0.83–0.92, RMSE = 1.94–3.01, rRMSE = 21.06%-7.09%), for different varieties (AP = 0.901–0.978, R2 = 0.77–0.97, RMSE = 1.39–3.16, rRMSE = 11.72%-5.06%), at different resolutions (AP = 0.921–0.956, R2 = 0.84–0.93, rRMSE = 8.72%-17.71%), and on UAV images with different resolutions (AP = 0.918–0.968, R2 = 0.98–0.99, rRMSE = 6.43%-12.76%), which proved the robustness of the model. The tasseling number and the time of entering tasseling stage detected from images were basically consistent with the trends observed in the manually labeled results. This study provides an effective method to monitor the tassel number and the time of entering the tasseling stage. A new maize tassel detection dataset (18260 tassels in 729 near-ground images and 20835 tassels in 144 UAV images) is created. Future studies will focus on making more lightweight models and achieving real-time detection capabilities.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This research was supported by Nanfan special project, CAAS (YBXM2305), National Natural Science Foundation of China (42071426, 42301427), Key Cultivation Program of Xinjiang Academy of Agricultural Sciences (xjkcpy-2020003), Research and Application of Key Technologies of Smart Brain for Farm Decision-Making Platform (2021ZXJ05A03), the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences.

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  1. Xun Yu and Dameng Yin have contributed equally to this work.

Authors and Affiliations

  1. National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024, People’s Republic of China

    Xun Yu, Dameng Yin & Xiuliang Jin

  2. Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, People’s Republic of China

    Xun Yu, Dameng Yin, Honggen Xu, Chenwei Nie, Yi Bai, Bo Ming & Xiuliang Jin

  3. International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico

    Francisco Pinto Espinosa

  4. Technical University of Munich, 85350, Freising, Germany

    Urs Schmidhalter

  5. Washington State University, Pullman, WA, 99164, USA

    Sindhuja Sankaran

  6. State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu, 610065, People’s Republic of China

    Ningbo Cui

  7. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, People’s Republic of China

    Wenbin Wu

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  1. Xun Yu
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Yu, X., Yin, D., Xu, H. et al. Maize tassel number and tasseling stage monitoring based on near-ground and UAV RGB images by improved YoloV8. Precision Agric (2024). https://doi.org/10.1007/s11119-024-10135-y

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