(Raw Data Set) Deep learning and computer vision for leaf miner infestation severity detection on muskmelon (Cucumis melo) leaves*

Abstract

Corp protection against pests is known to play a crucial role in developing efficient crop management strategies for Precision Agriculture. A recent estimation by Food and Agriculture Organization (FAO) shows that the perennial loss due to crop pests and diseases amounts to nearly 40% of agricultural crop production at a global level. Identifying pests and diseases and eradicating them without automation is laborious and time-consuming. Automation in detecting and identifying miners at the onset and their eradication is possible using deep learning (DL) and computer vision. This study aims to develop a Detectron2-based framework to detect and localize miner infestations on muskmelon leaves by developing a detection model that integrates DL and a computer vision library to enhance detection capabilities. The approach develops, experiments, and compares a region-based detector (Faster Region-based Convolutional Neural networks (RCNN)) with a region-free (RetinaNet) by training and validating the bounding box annotated custom dataset of leaf miner infected muskmelon leaves imaged using a smartphone camera. The results show that the RetinaNet-based detector outperforms the Faster R-CNN-based detector in recognizing the infestation severity levels, significantly increasing mean average precision and acquiring faster detection speeds.