Deep Learning based Betelvine leaf Disease Detection (Piper BetleL.)

Environment influences plants in various forms of stress that affects plant's growth and development. Biotic Stress causes damage to plants with the help of living organisms, pests, and pathogens. This leads to a huge loss of agricultural productivity and sustainability that has a serious impact on the world economy. Thus, at the outset of growth, it is highly desirable to automate the process of disease identification and diagnosis. At present, computer vision using deep learning methods have achieved considerable success with object detection tasks. The goal of this work is to develop an innovative, ground-breaking framework for the detection of diseases based on a deep convolution neural network through the transfer learning technique. For this work, initially, we collected a dataset that contains images of 1,014 Betelvine leaves with healthy and diseased categories in the cultivated betel vine crops and annotated manually for training. Mask-RCNN instance segmentation model is adopted for detecting Betelvine diseased leaves effectively. Eventually, the trained model achieved mean Average Precision (mAP) of 84%, using an updated Mask-RCNN model by fine-tuning the parameters with a swish as activation function and RAdam as the optimizer.