Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{204432,
        author = {UMARANI.S and R.LOGAMBAL},
        title = {MODERNIZING PADDY FARMING THROUGH MACHINE LEARNING TECHNOLOGIES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2396-2401},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204432},
        abstract = {This paper suggests an advanced strategy utilizing machine learning algorithms to address the issues facing the agricultural industry, including the unpredictable nature of the climate and the declining availability of water, which affects crop production. The study finds possibilities for improvement by analyzing existing algorithms, such as boosting, RF, MLP, SVM, KNN, and logistic regression, with corresponding accuracy scores. In addition to highlighting ensemble approaches and feature engineering, the study presents a new algorithm that maximizes prediction accuracy. The efficacy of the suggested method in handling the intricacies of agricultural production prediction is proven by extensive validation against benchmark models. This study advances the development of smart farming techniques by providing a reliable way to improve the accuracy and productivity of rice production and, therefore, all agricultural activities.},
        keywords = {Paddy Rice, Machine Learning, Smart Farming},
        month = {June},
        }