YOLO SSPD: a small target cotton boll detection model during the boll-spitting period based on space-to-depth convolution.

Abstract

Introduction Cotton yield estimation is crucial in the agricultural process, where the accuracy of boll detection during the flocculation period significantly influences yield estimations in cotton fields. Unmanned Aerial Vehicles (UAVs) are frequently employed for plant detection and counting due to their cost-effectiveness and adaptability. Methods Addressing the challenges of small target cotton bolls and low resolution of UAVs, this paper introduces a method based on the YOLO v8 framework for transfer learning, named YOLO small-scale pyramid depth-aware detection (SSPD). The method combines space-to-depth and non-strided convolution (SPD-Conv) and a small target detector head, and also integrates a simple, parameter-free attentional mechanism (SimAM) that significantly improves target boll detection accuracy. Results The YOLO SSPD achieved a boll detection accuracy of 0.874 on UAV-scale imagery. It also recorded a coefficient of determination (R 2 ) of 0.86, with a root mean square error (RMSE) of 12.38 and a relative root mean square error (RRMSE) of 11.19% for boll counts. Discussion The findings indicate that YOLO SSPD can significantly improve the accuracy of cotton boll detection on UAV imagery, thereby supporting the cotton production process. This method offers a robust solution for high-precision cotton monitoring, enhancing the reliability of cotton yield estimates.

Plant phenotyping relevance

UAV画像から綿花のbollを検出・計数する新規YOLOベース手法を開発し、検出精度とboll数推定性能を評価しており、植物形質取得手法が中心である。

Code and data availability

The article describes a custom cotton boll UAV image dataset and the YOLO SSPD model, but the supplied blocks contain no data availability statement, no public deposit of the authors' images, annotations, code, or trained checkpoints. All URLs mentioned (Faster R-CNN, YOLOv5/v7/v8, SPD-Conv, pytorch-grad-cam) are third

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