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@article{194183,
        author = {Vaidehi Narkhede and Siddhesh Bhosale and Rohit Choudhari and Aayush Mali},
        title = {GrowSure App: A Multilingual Multi-Crop Disease Detection and Solution},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {2920-2926},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194183},
        abstract = {Agricultural productivity worldwide is severely hampered by viral, bacterial, and fungal plant diseases, resulting in significant crop losses and economic hardship for farming communities. Conventional disease diagnosis relies heavily on manual expert inspection, which is time-consuming, error-prone, and often inaccessible to smallholder farmers in rural regions. This paper presents the GrowSure App, a comprehensive AI-powered and IoT-enabled mobile platform designed to address these challenges holistically. The proposed system leverages a fine-tuned Convolutional Neural Network (CNN) for multi-crop leaf disease classification, achieving an average detection accuracy of 92.5% across five crop types. A hybrid recommendation engine combining Collaborative Filtering (CF) and Content-Based Filtering (CBF) provides personalized treatment suggestions based on disease diagnosis, farmer history, and real-time environmental data. IoT sensors continuously monitor critical soil parameters moisture, pH, and temperature feeding contextual data into the advisory system for crop suitability recommendations. Additionally, Natural Language Processing (NLP) modules incorporating transformer-based translation models and Text-to-Speech (TTS) deliver agricultural guidance in English, Hindi, and Marathi, bridging the digital divide for linguistically diverse farming communities.},
        keywords = {plant disease detection; convolutional neural network; IoT; collaborative filtering; content-based filtering; multilingual support; precision agriculture; deep learning; mobile application},
        month = {March},
        }