International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The nutritional value of mulberry leaves plays a pivotal role in sericulture, directly impacting silkworm development and silk production. Traditional methods of assessing leaf health rely on manual inspection, which is often subjective, labor-intensive, and impractical for large-scale monitoring. This paper introduces a real-time deep learning system for detecting nutrient deficiencies in mulberry leaves, combining YOLOv8-based instance segmentation with LAB color space clustering. The model efficiently detects and classifies nitrogen, potassium, and magnesium deficiencies by analyzing subtle color variations in leaf tissue. Experimental results show that the proposed system achieves superior accuracy and precision compared to conventional techniques. Moreover, it delivers rapid and scalable performance suitable for field-level deployment. To enhance usability, the detection model is integrated into a user-friendly interface, empowering sericulture farmers to make informed, data-driven decisions for improving leaf quality. This automated solution aims to increase productivity, reduce losses, and support sustainable silk farming through optimized nutrient management.

Keywords: YOLOv8, Instance Segmentation, Mulberry Leaves, Nutrient Deficiency Detection, LAB Color Space, Deep Learning, Sericulture, Sustainable Agriculture.

Downloads: | DOI: 10.17148/IJARCCE.2026.15218