Abstract
The Pyralidae pests, such as corn borer and rice leaf roller, are main pests in economic crops. The timely detection and identification of Pyralidae pests is a critical task for agriculturists and farmers. However, the traditional identification of pests by humans is labor intensive and inefficient. To tackle the challenges, a pest monitoring autonomous robot vehicle and a method to recognize Pyralidae pests are presented in this paper. Firstly, the robot on autonomous vehicle collects images by performing camera sensing in natural farm scene. Secondly, the total probability image can be obtained by using inverse histogram mapping, and then the object contour of Pyralidae pests can be extracted quickly and accurately with the constrained Otsu method. Finally, by employing Hu moment and the perimeter and area characteristics, the correct contours of objects can be drawn, and the recognition results can be obtained by comparing them with the reference templates of Pyralidae pests. Additionally, the moving speed of the mechanical arms on the vehicle can be adjusted adaptively by interacting with the recognition algorithm. The experimental results demonstrate that the robot vehicle can automatically capture pest images, and can achieve 94.3$\%$ recognition accuracy in natural farm planting scene.
Abstract (translated)
吡利达类害虫如玉米螟、水稻压叶机等是经济作物的主要害虫。及时发现和鉴定吡利达害虫是农业工作者和农民的一项重要任务。然而，传统的人类害虫鉴定是劳动密集和效率低下的。为解决这一难题，本文提出了一种害虫监测自主机器人车辆和一种吡利达害虫识别方法。首先，自主车辆上的机器人通过在自然农场场景中进行摄像头感知来采集图像。其次，利用逆直方图映射得到总概率图像，然后利用约束OTSU方法快速、准确地提取吡立达害虫的目标轮廓。最后，利用Hu矩和周长和面积特征，绘制出目标的正确轮廓，并与吡立达害虫的参考模板进行比较，得到识别结果。另外，通过与识别算法的交互作用，可以自适应地调整机械臂在车辆上的运动速度。实验结果表明，该机器人能够自动捕捉害虫图像，在自然种植环境下，能够达到94.3美元\%$的识别精度。
URL
https://arxiv.org/abs/1903.10827
