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{199593,
        author = {Eshika Raut and Soniya Bhomlekar and Sanika Mankumbare and Mayur Markad and Sneha Tirth and Samreen Shaikh},
        title = {Smart Agriculture System for Plant Health Forecasting Using IoT and Artificial Intelligence},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15502-15508},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199593},
        abstract = {In order to enable real-time crop condition monitoring, analysis, and prediction, this study provides an enhanced plant health forecasting system for precision agriculture that combines Artificial Intelligence (AI) approaches with Internet of Things (IoT) sensor networks. Using dispersed IoT sensors, the system gathers vital environmental and soil characteristics, including temperature, humidity, soil moisture, pH levels, and light intensity. To guarantee data quality and dependability, these data are sent to cloud or edge computing platforms where they go through preprocessing procedures like noise filtering, normalization, and management of missing values. Long Short-Term Memory (LSTM) networks, Random Forest algorithms, regression approaches, and other machine learning and deep learning models are used to examine the processed data. By predicting future plant health and identifying patterns in environmental variables, these models make it possible to detect stress, disease, and nutritional deficiencies early. The system uses these forecasts to produce real-time alerts and practical suggestions that help farmers make well-informed decisions about crop protection, fertilization, and irrigation.
The suggested approach increases agricultural productivity, lowers crop losses, and raises yield quality by fusing continuous sensing with predictive analytics. IoT and AI integration shows great promise for making traditional farming more intelligent, data-driven, and sustainable.},
        keywords = {},
        month = {April},
        }