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

• Published in: Computers & electrical engineering 2023-09, Vol.110, p.108843, Article 108843
• Main Authors: Math, RajinderKumar M., Dharwadkar, Nagaraj V.
• Format: Article
• Language: English
• Subjects: Computer vision; Convolutional neural networks; Deep learning; Detectron2; Faster R-CNN; Leaf miner; Pest detection; RetinaNet
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2023.108843

•Manual pest idenatification and eradication procedures are time-consuming and labour-intensive, emphasising the need for automation.•An integrated approach using deep learning and computer vision helps to automatically locate and identify the severity levels of pest infestations.•An open-source annotation tool called LabelMe is used to annotate bounding boxes around the infected areas of muskmelon leaves.•The study develops, evaluates, and compares the performance of the RetinaNet-based detector and the Faster R-CNN-based detector under the Detectron2 framework.•The RetinaNet-based detector achieves higher mean average precision, enabling faster and more accurate detection of miner infestations. 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 (R-CNN)) 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. [Display omitted]