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Automatic monitoring of flying vegetable insect pests using an RGB camera and YOLO-SIP detector
Precision Agriculture ( IF 6.2 ) Pub Date : 2022-09-02 , DOI: 10.1007/s11119-022-09952-w
Qingwen Guo, Chuntao Wang, Deqin Xiao, Qiong Huang

Pests cause heavy crop losses, so it is vital to conduct early pest management and control in precision agriculture. In general, pest monitoring is a foundation for early pest management and control. Conventional pest monitoring using manual sampling and detection is time consuming and labour intensive. Therefore, many studies have explored how to achieve automatic pest monitoring. However, few works have focused on automatic monitoring of flying vegetable insect pests. To close this gap, this study developed an automatic monitoring scheme for flying vegetable insect pests based on two hypotheses: (1) yellow sticky traps could provide reliable information to assess population density of flying vegetable insect pests, and (2) a computer-vision-based detector could accurately detect pests in images. Specifically, yellow sticky traps were exploited to sample flying vegetable insect pests, and an RGB camera was adopted to capture yellow-sticky-trap images; and a computer-vision-based detector called “YOLO for Small Insect Pests” (YOLO-SIP) was used to detect pests in captured images. The hypotheses were tested by using the Heuristics engineering method, installing yellow sticky traps and RGB cameras in vegetable fields, constructing a manually labelled image dataset, and applying YOLO-SIP to the constructed dataset with the mean average precision (mAP), average mean absolute error (aMAE), and average mean square error (aMSE) metrics. Experiments showed that the proposed scheme captured yellow-sticky-trap images automatically and obtained an mAP of 84.22%, an aMAE of 0.422, and an aMSE of 1.126. Thus, the proposed scheme is promising for the automatic monitoring of flying vegetable insect pests.



中文翻译:

使用 RGB 相机和 YOLO-SIP 检测器自动监测飞行蔬菜害虫

病虫害造成严重的作物损失,因此在精准农业中进行早期病虫害管理和控制至关重要。一般来说,害虫监测是早期害虫管理和控制的基础。使用手动采样和检测的传统害虫监测既费时又费力。因此,许多研究探索了如何实现害虫自动监测。然而,很少有工作专注于飞行蔬菜害虫的自动监测。为了弥补这一差距,本研究开发了一种基于两个假设的飞行蔬菜害虫自动监测方案:(1)黄色粘性陷阱可以提供可靠的信息来评估飞行蔬菜害虫的种群密度,以及(2)计算机视觉基于检测器的检测器可以准确检测图像中的害虫。具体来说,利用黄粘诱捕器对飞行蔬菜害虫进行采样,并采用RGB相机拍摄黄粘诱捕器图像;一个名为“YOLO for Small Insect Pests”(YOLO-SIP)的基于计算机视觉的检测器用于检测捕获图像中的害虫。通过使用启发式工程方法,在菜地中安装黄色粘性陷阱和 RGB 相机,构建手动标记的图像数据集,并将 YOLO-SIP 应用到构建的数据集的平均平均精度 (mAP)、平均绝对绝对值,对假设进行了测试。误差 (aMAE) 和平均均方误差 (aMSE) 指标。实验表明,该方案自动捕获黄色粘性陷阱图像,获得了 84.22% 的 mAP、0.422 的 aMAE 和 1.126 的 aMSE。因此,

更新日期:2022-09-02
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