PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: The YOLO SSPD achieved a boll detection accuracy of 0.874 on UAV-scale imagery. It also recorded a coefficient of determination (R2) 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: The YOLO SSPD achieved a boll detection accuracy of 0.874 on UAV-scale imagery. It also recorded a coefficient of determination (R2) 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.
UNASSIGNED: 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.
摘要：
■棉花产量估算在农业过程中至关重要，絮凝期棉铃检测的准确性会显着影响棉田的产量估算。无人机（UAV）由于其成本效益和适应性而经常用于植物检测和计数。
■应对小目标棉铃和无人机分辨率低的挑战，本文介绍了一种基于YOLOv8框架的迁移学习方法,名为YOLO小规模金字塔深度感知检测(SSPD)。该方法结合了空间到深度和非跨步卷积（SPD-Conv）和小型目标探测器头，并且还集成了一个简单的，无参数注意机制(SimAM)，显著提高目标铃铛检测精度。
■YOLOSSPD在无人机尺度图像上实现了0.874的棉铃检测精度。它还记录了测定系数（R2）为0.86，均方根误差（RMSE）为12.38，相对均方根误差（RRMSE）为11.19％。
■研究结果表明，YOLOSSPD可以显着提高无人机图像上棉铃检测的准确性，从而支持棉花生产过程。该方法为高精度棉花监测提供了一个可靠的解决方案，提高棉花产量估算的可靠性。
■应对小目标棉铃和无人机分辨率低的挑战，本文介绍了一种基于YOLOv8框架的迁移学习方法,名为YOLO小规模金字塔深度感知检测(SSPD)。该方法结合了空间到深度和非跨步卷积（SPD-Conv）和小型目标探测器头，并且还集成了一个简单的，无参数注意机制(SimAM)，显著提高目标铃铛检测精度。
■YOLOSSPD在无人机尺度图像上实现了0.874的棉铃检测精度。它还记录了测定系数（R2）为0.86，均方根误差（RMSE）为12.38，相对均方根误差（RRMSE）为11.19％。
■研究结果表明，YOLOSSPD可以显着提高无人机图像上棉铃检测的准确性，从而支持棉花生产过程。该方法为高精度棉花监测提供了一个可靠的解决方案，提高棉花产量估算的可靠性。
