Optimized Curry Leaf Disease Detection using Enhanced Faster R-CNN Deep Learning Model
DOI:
https://doi.org/10.15662/IJEETR.2026.0802398Keywords:
Curry Leaf Disease Detection, Deep Learning, Faster R-CNN, Precision Agriculture, Computer VisionAbstract
Plant diseases significantly reduce crop productivity and quality in agricultural systems. Early identification of plant diseases is essential for effective crop management and sustainable farming practices. Curry leaf plants (Murraya koenigii) are widely used in culinary and medicinal applications but are highly susceptible to several leaf diseases that affect plant growth and yield. Traditional disease detection methods rely on manual visual inspection, which is time-consuming and dependent on expert knowledge. This paper presents an automated deep learning-based system for curry leaf disease detection using an Enhanced Multi-Scale Attention Faster Region-based Convolutional Neural Network (EMSA-FRCNN). The proposed model integrates adaptive preprocessing, multi-scale feature extraction, and an attention-based feature refinement module to improve detection accuracy under complex environmental conditions. A ResNet-50 backbone is used for feature extraction, while a Region Proposal Network generates candidate disease regions. Experimental results demonstrate that the proposed method achieves an overall detection accuracy of 96.8%, outperforming traditional machine learning and standard CNN approaches. The proposed framework provides a reliable and scalable solution for intelligent agricultural monitoring systems.
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