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@article{179284,
        author = {Aditya Nimankar and Karishma Misal and Chetna Nagpure and Prajakta Nakhate and Divyank khobragade},
        title = {Agriculture Yield Prediction Using Edge Computing and  Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7151-7158},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179284},
        abstract = {When it comes to international trade and food security, agriculture is vital. Yield prediction has changed drastically since the introduction of powerful computational methods. In this study, we survey all the new developments in agricultural output prediction that have made use of edge computing and machine learning (ML). The effectiveness of various ML techniques in yield forecasting is evaluated in this research. These algorithms include regression models, deep learning approaches, ensemble methods, and support vector machines. Also covered is the role of edge computing in improving precision agriculture decision-making through real-time data processing with reduced latency. It also delves into the integration of smart farming based on the Internet of Things, monitoring using drones, and decision support systems driven by artificial intelligence. Data integration, computational limitations, and scalability are still problems, despite the fact that machine learning and edge computing provide good solutions. Improving the quality of datasets, investigating federated learning methods, and optimizing ML models for edge devices are all areas that might use more investigation in the future. The purpose of this review is to offer helpful information on how to improve agricultural sustainability, food security, and productivity by using modern technology.},
        keywords = {},
        month = {May},
        }