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@article{186808,
        author = {Nikhil Shelke and Rohan Pawar and Manas Torane and Pranay Satpute and Prof. Swati Gade},
        title = {Crop Yield Prediction Driven by ML & DL},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {6489-6492},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186808},
        abstract = {The approach and implementation of an integrated Machine Learning system are presented for modernizing agriculture. It uses Machine Learning to overcome various challenges faced by farmers due to unfavourable weather, pest diseases, resource constraints, and methods that are inefficient. The proposed solution aims to provide data-driven insights to help farmers with the selection of crops. At the core, Random Forest Regression is used here to estimate potential yield and suggest the best crop for a given soil type at any specific real-time weather condition. It also features a CNN for accurate detection of diseases from user-uploaded images of crops. The application integrates the Open Weather API to fetch real-time climate data and Gemini API for contextual understanding and personalized fertilizer recommendations. This presented system combines these intelligent modules to come up with reliable, context-aware, and data-driven suggestions for farmers, thereby increasing productivity and ensuring sustainability.},
        keywords = {Random Forest, CNN, Crop Yield Prediction, Fertilizer Recommendation, Disease Detection, Smart Agriculture.},
        month = {November},
        }