A Novel Approach for Red Rot Disease Detection in Sugarcane Plants Using Transfer Learning Based VGG16 Model

Abstract

Red rot in sugarcane is a devastating fungal disease caused by Colletotrichum falcatum. It leads to wilting, red discoloration, and yield losses, which can impact sugar production globally. Red rot hampers sugarcane production, causing yield reductions, and economic losses, and affecting the nation’s sugar industry demanding stringent disease management strategies. There is a need of a system that detects the red rot disease at an early stage before it spreads. Transfer learning enhances agricultural models, leveraging pre-trained data for improved crop predictions, resource optimization, and sustainability. In this article, we have proposed a transfer learning-based VGG16 model for the detection of red rot disease in sugarcane on a large dataset. The model achieves a remarkable 98.85% accuracy and an F1 score of 0.93 in early red rot detection in sugarcane. This work underscores the potency of leveraging re-trained models for crop disease identification, offering a promising avenue for proactive disease management. This research marks a substantial step towards enhancing crop yield and sustainability, presenting an accessible and impactful technological solution for early disease detection in sugarcane, ultimately benefiting agricultural practices.