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@article{173263,
        author = {VARUN RAJ},
        title = {PLANT DISEASE CLASSIFICATION USING ARTIFICIAL INTELLIGENCE: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {2588-2592},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173263},
        abstract = {Plant diseases are a significant threat to global agriculture, impacting crop yields and food security. Early detection and accurate classification of these diseases are crucial for minimizing their adverse effects. Traditional methods of plant disease diagnosis, often reliant on expert knowledge and manual inspection, are time-consuming and impractical for large-scale applications [1]. This paper investigates the use of machine learning (ML) algorithms for automating the detection and classification of plant diseases. A comparative study was conducted using four popular ML models: Random Forest (RF), Support Vector Machines (SVM), Gradient Boosting Machines (GBM), and Convolutional Neural Networks (CNN). The models were evaluated on the publicly available PlantVillage dataset, which contains labeled images of diseased and healthy plant leaves. Several performance metrics, including accuracy, precision, recall, F1-score, and AUC-ROC, were used to assess the efficacy of each algorithm. The results indicate that CNN outperforms traditional machine learning algorithms in terms of classification accuracy and robustness, achieving a high accuracy of 96.4%.[2] Random Forest and Gradient Boosting also demonstrated strong performance, while SVM showed relatively lower accuracy. This study highlights the potential of deep learning techniques for plant disease classification and offers insights into the strengths and limitations of different machine learning models for agricultural applications.},
        keywords = {Plant disease classification, machine learning, deep learning, Convolutional Neural Networks (CNN), Random Forest, Support Vector Machines (SVM), Gradient Boosting, image processing, agricultural automation, plant health monitoring, AUC-ROC, feature extraction, dataset, AI in agriculture.},
        month = {February},
        }