Abstract
Agriculture is one of the main economic pursuits of the 64 Bangladeshi districts.: in Bangladesh, about seventy percent of the workforce rely on agriculture for their living. Bangladesh's gross national revenue is much enhanced by the country's rice farming but attacks of insect pests have a great impact on rice harvests. Different insect pests require different management measures, so the accurate identification of paddy field insects is a crucial task that allows, e.g., the application of the appropriate poison for specific insect pests. This will also prevent the wasteful use of ineffective insecticides. The main challenge addressed in this work is to detect and instantly segment small harmful insects in paddy fields. To address this problem, we have used deep convolutional neural network (DCNN) learning, based on Mask-RCNN, therefore enabling a technique for visual localisation and classification of agricultural pest insects. We have also developed our own dataset of harmful insect annotated images. In the proposed Mask-RCNN model we used a ResNet101 backbone, which can detect and segment at the same time. The proposed model achieves an AP@0.5 of 85.7%, a mAP of 63.8%, and an AR@10 of 68.5, therefore generating an anticipated accuracy of 75%. ResNet101 performs better on all measures. The suggested approach should be able to identify and classify the small harmful insects, with suitable accuracy, in real-world deployments.
