Towards Accurate Disease Segmentation in Plant Images: A Comprehensive Dataset Creation and Network Evaluation

Komuravelli Prashanth, Jaladi Sri Harsha, Sivapuram Arun Kumar, Jaladi Srilekha; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 7086-7094

Automated disease segmentation in plant images plays a crucial role in identifying and mitigating the impact of plant diseases on agricultural productivity. In this study, we address the problem of Northern Leaf Blight (NLB) disease segmentation in maize plants. We present a comprehensive dataset of 1000 plant images annotated with NLB disease regions. We employ the Mask R-CNN and Cascaded Mask R-CNN models with various backbone architectures to perform NLB disease segmentation. The experimental results demonstrate the effectiveness of the models in accurately delineating NLB disease regions. Specifically, the ResNet Strikes Back-50 backbone architecture achieves the highest mean average precision (mAP) score, indicating its ability to capture intricate details of NLB disease spots. Additionally, the cascaded approach enhances segmentation accuracy compared to the single-stage Mask R-CNN models. Our findings provide valuable insights into the performance of different backbone architectures and contribute to the development of automated NLB disease segmentation methods in plant images. The generated dataset and experimental results serve as a resource for further research in plant disease segmentation and management.