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

Komuravelli Prashanth, Jaladi Sri Harsha

Abstract:

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.