Insect Detection and Identification using YOLO Algorithms on Soybean Crop

Abstract

In the current time, Indian agriculture is lagging in the use of advanced technological solutions in tackling various farming-related issues such as crop health, weed problems, crop diseases, etc. We intend to bridge this gap by proposing technological solutions to automatically detect insects in Soybean crops. Soybean (Glycine max) is an edible seed from an annual legume in the pea family (Fabaceae). The soybean is the world's most economically important bean, providing vegetable protein to millions of people as well as ingredients for hundreds of chemical goods. Object detection is a computer vision task that involves the identification of object class with its location in the image. We have employed three popular object detection algorithms for insect identification on Soybean crop fields. YOLO v3, v4, and v5 have been trained to detect and demarcate the insect presence on the field. The simulation results revealed that the YOLO v5 delivers the best insect detection accuracy with mean average precision (mAP) of 99.5% followed by YOLO v4 and v3.