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@article{171413,
        author = {Tejas Birajdar and Pratik Bhalerao and Omkar Doijad and Mahesh Dhumala and Priyanka Khalate},
        title = {Classification of Wilt Disease with an MLP Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {3138-3141},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171413},
        abstract = {The MLP-based Wilt Disease Classifier is a Deep learning system designed to detect wilt disease in plants using a Multilayer Perceptron (MLP) neural network. Wilt disease, caused by various pathogens, significantly affects crop yield, making early detection crucial for effective intervention. This classifier processes input features such as environmental factors and plant health indicators to accurately predict the presence of wilt. The MLP model, with its multiple layers of interconnected neurons, is trained on a dataset of infected and healthy plants, learning patterns that distinguish disease symptoms. The classifier's performance is evaluated based on accuracy, precision, recall, and F1 score, demonstrating its potential as a reliable tool for farmers and agricultural experts to mitigate crop losses through timely diagnosis.},
        keywords = {Crop Images, Deep learning, CNN},
        month = {December},
        }