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@article{197436,
        author = {Pradnya Somnath Sanap and Dr.Varsha H.Patil and Dr.Swati A.Bhavsar},
        title = {Review of Deep Learning-Based Multi-Class Classification of Mango, Guava, and Pomegranate Leaf Diseases},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5843-5848},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197436},
        abstract = {Early identification of plant diseases is crucial to maintaining agricultural productivity and preventing economic losses. In recent years, the emergence of deep learning has made it possible to design efficient image-based systems capable of diagnosing diseases across different crops. This review presents an analytical overview of the techniques applied to the multi-class classification of mango, guava, and pomegranate fruit and leaf diseases. It compiles information on existing public datasets, preprocessing methods, and deep learning architectures such as convolutional neural networks (CNNs), transfer learning models, and object detection approaches like YOLO. It also considers privacy-oriented methods such as federated learning. The study compares reported accuracies, computational complexity, and generalization capabilities while noting persistent gaps, including the absence of large cross-domain datasets and limited deployment on low-cost hardware. The observations summarized here aim to guide future research toward developing explainable, lightweight, and scalable deep learning solutions for disease detection in horticultural crops.},
        keywords = {Plant disease detection, Deep learning, multi-class classification, Mango, Guava, Pomegranate , Federated learning},
        month = {April},
        }