Machine Learning-based System for Sugarcane Disease Detection
Keywords:
Disease detection, Logistic regression, Machine learning, Principal component analysis (PCA), Sugarcane agricultureAbstract
Sugarcane is a crucial industrial crop for sugar and bioethanol production, and its yield and quality are highly dependent on early disease detection and effective treatment strategies. However, sugarcane production is severely affected by numerous diseases such as red rot, smut, mosaic, and rust, which can lead to significant yield losses. Traditional methods for disease identification rely heavily on manual observation and expert knowledge, which are time-consuming and prone to human error. In recent years, machine learning (ML) and deep learning (DL) approaches have gained prominence in agricultural research due to their ability to automate and improve disease detection accuracy. With advancements in artificial intelligence and remote sensing, precision agriculture has become instrumental in improving crop health management. This paper reviews the development of a machine learning-based system for sugarcane disease detection that integrates convolutional neural networks (CNNs), principal component analysis (PCA), and logistic regression (LR) for robust classification. The proposed system aims to identify major sugarcane diseases from image data while providing an automated advisory module for treatment recommendations. Existing works in sugarcane yield prediction, disease classification, hyperspectral imaging, and robotic precision spraying are reviewed to position the proposed system within current research trends. The review highlights how PCA enhances feature selection and reduces computational complexity, while logistic regression ensures interpretable, efficient classification. The study concludes that the integration of statistical and machine learning techniques can significantly improve disease detection accuracy and support data-driven decision-making in sugarcane farming.
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