Hybrid Approach of Cotton Disease Detection for Enhanced Crop Health and Yield
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Date
2024-07
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Abstract
The well-being of cotton crops is of utmost importance for maintaining agricultural
productivity, and the early detection of diseases plays a critical role in achieving this objective. This study
introduces a comprehensive approach for creating a machine learning-based system capable of identifying
diseases in cotton plants through the analysis of leaf images. The research encompasses stages such as
acquiring the dataset, pre-processing the data, training the model, developing an ensemble model, evaluating
the models, and analyzing the results. Several machine-learning models are trained and evaluated to
determine how well they can classify cotton leaves as "Healthy" or "Diseased." These models include
Random Forest, Support Vector Machine (SVM), Multi-Class SVM, and an Ensemble model. This
investigation yields a practical and visually informative system for disease detection, which can contribute to
disease prevention, thereby enhancing both crop yield and quality. This work underscores the significance of
continuous improvement by periodically updating the models and explores the potential of advanced
techniques such as deep learning.