A Comparative Performance Analysis for detection of Red-Rot of Sugarcane
Keywords:
Red-Rot, Machine Learning (ML), Disease Detection, Ensemble Learning, XgboostAbstract
Red-rot disease caused by Colletotrichum falcatum is a significant threat for substantial economic losses in sugarcane industry worldwide. Early and accurate detection of disease is crucial for implementing timely control measures for substantial cultivation. This study presents a comparative performance analysis among various leverage machine learning approaches to effectively detect red-rot infections. The research rigorously evaluates the performance of each technique based on accuracy, precision, recall, F-measure, and other relevant error analysis. The data collection, preprocessing, and feature extraction methodologies are meticulously implemented to ensure the credibility and generalizability of the findings. The study's outcomes hold significant implications for precision agriculture and sustainable farming practices. The aim of this study is to implement accurate and efficient method for red-rot disease detection, which can empower farmers to enable targeted intervention to minimize crop losses and reliance on chemical treatments, which will contribute in global movement towards eco-friendly agriculture and enhancement of sugarcane industry. The results of study shed light on the strengths and limitations of each machine learning technique, aiding researchers and practitioners in selecting the most suitable approach for Red Rot detection.
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