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@article{169327,
        author = {Sravanth Kumar Tankari and Namrta Tanwar and Saikumar Swarnapudi and Jhansi Kottali and Lalitha Donavalli and Myskina Chowdary Raparla},
        title = {Precision Agriculture with Random Forests: A  Robust Approach to Guava Fruit Disease Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2162-2167},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169327},
        abstract = {One of the main causes of production losses and financial challenges for the worldwide agriculture industry is fruit diseases. This study offers a suggested and validated experimental approach for the detection and classification of apple diseases. The suggested image processing-based method consists of the following primary steps: Using the K-Means clustering technique, segment the images first; then, extract some cutting-edge characteristics from the segmented image; and last, classify the images into one of the classes using a Multi-class Support Vector Machine. Our testing results indicate that the proposed method may significantly improve the automated and precise identification of apple fruit illnesses. The recommended method can achieve up to 93% classification accuracy.},
        keywords = {K-Means Clustering; Local Binary Pattern; Multi- class Support Vector Machine; Texture Classification;},
        month = {November},
        }