Deep Learning-Based Leaf Region Segmentation Using High-Resolution Super HAD CCD and ISOCELL GW1 Sensors

Abstract

Super HAD CCD and ISOCELL GW1 imaging sensors are used for capturing images in high-resolution cameras nowadays. These high-resolution camera sensors were used in this work to acquire black gram plant leaf diseased images in natural cultivation fields. Segmenting plant leaf regions from the black gram cultivation field images is a preliminary step for disease identification and classification. It is also helpful for the farmers to assess the plants’ health and identify the diseases in their early stages. Even though plant leaf region segmentation has been effectively handled in many contributions, no universally applicable solution exists to solve all issues. Therefore, an approach for extracting leaf region from black gram plant leaf images is presented in this article. The novelty of the proposed method is that MobileNetV2 has been utilized as a backbone network for DeepLabv3+ layers to segment plant leaf regions. The DeepLabv3+ with MobileNetV2 segmentation model exhibited superior performance compared to the other models (SegNet, U-Net, DeepLabv3+ with ResNet18, ResNet50, Xception, and InceptionResNetV2) in terms of accuracy of 99.71%, Dice of 98.72%, and Jaccard/IoU of 97.47% when data augmentation was applied. The algorithms were developed and trained using MATLAB software. Each of the experimental trials reported in this article surpasses the prior findings.