• Arati Prakash Patil
• Almas Amol Mahaldar

Microbialdiseases affecting fruits represent a significant challenge for farmers. Early detection of these diseases is crucial for effective crop management and plantation protection. Infrastructure constraints limit the ability to detect fruit diseases early. To deal with this challenge, a new approach based on artificial intelligence has been developed to detect and classify mango leaf diseases. The study is based on two methodological approaches. Firstly, we use machine learning algorithms, including Random Forests, Support Vector Machines (SVMs), and K-nearest neighbors KNN algorithm. In parallel, a second approach incorporates Convolutional Neural Networks (CNNs) to extract complex visual features from fruit images. These features are combined with the three machine learning algorithms mentioned above for classification

machine learning algorithm, deep learning, disease detection and classification, Convolutional Neural Network (CNN), feature extractor

"FRUIT DISEASE DETECTION USING A MACHINE LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 1, page no.a864-a867, January-2025, Available :http://www.jetir.org/papers/JETIR2501109.pdf

"FRUIT DISEASE DETECTION USING A MACHINE LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 1, page no. ppa864-a867, January-2025, Available at : http://www.jetir.org/papers/JETIR2501109.pdf