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@article{200188,
        author = {Tejas Parmeshwar Kawale and Aniket Uddhav Chaudhari},
        title = {AI in Agriculture: Predicting Crop Yields Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {712-716},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200188},
        abstract = {The application of Artificial Intelligence (AI) in the agricultural sector has revolutionized crop yield forecasting, providing valuable information to farmers and policymakers for informed decision-making. This study explores different Machine Learning (ML) approaches used to predict crop yields, using weather conditions, soil characteristics, and past agricultural data. This research examines various supervised and unsupervised learning techniques, including regression, decision trees, support vector machines, and deep learning, and evaluates their effectiveness and potential use cases in agriculture. Moreover, the study underscores the need for effective data gathering from various sources such as satellite data, IoT-enabled soil sensors, weather reports, and historical agricultural data. The paper discusses issues such as limited data sources, model overfitting, and computational constraints, and offers insights on how to improve the accuracy of predictions. Additionally, this research includes statistical methods and visualizations to outline trends, relationships, and the performance of AI-based models in predicting crop yields. This paper offers a comparative study of ML algorithms, bridging the theoretical and practical aspects of agricultural applications. This work builds on previous research in enhancing AI systems for sustainable agricultural practices, thereby enhancing food security and resource utilization. Finally, the paper concludes with perspectives on the latest trends, ethical implications and the future of AI-based agriculture.},
        keywords = {Machine Learning in Agriculture, Crop Yield Prediction, AI in Farming, Precision Agriculture, Data-Driven Agriculture, Sustainable Farming, Predictive Analytics in Agriculture.},
        month = {May},
        }