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{203846,
        author = {Umamaheswararao Mogili},
        title = {Precision Agriculture for Sustainable Intensification: Leveraging Smart Technologies to Maximize Crop Yield and Minimize Resource Utilization},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {1448-1457},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203846},
        abstract = {Agriculture constitutes a fundamental pillar of global food security and economic sustainability. Nevertheless, conventional agricultural methodologies are frequently constrained by numerous operational inefficiencies, including suboptimal water resource utilization, inadequate real-time monitoring of crop and soil conditions, and excessive reliance on manual decision-making processes. These limitations often result in diminished agricultural productivity, inefficient allocation of critical resources, and increased environmental stress. In response to these challenges, the advent of precision agriculture has emerged as a transformative paradigm capable of enhancing agricultural efficiency, productivity, and sustainability through the integration of advanced technological frameworks. Consequently, data-driven decision-making can be implemented for optimized irrigation scheduling, nutrient management, fertilization strategies, and overall crop cultivation practices. This study emphasizes the development of an intelligent agricultural monitoring and management system designed to evaluate critical environmental and soil-related parameters, including soil moisture content, ambient temperature, relative humidity, and crop health indicators. The proposed framework enables continuous surveillance of agricultural conditions and supports timely intervention through real-time data analysis and predictive monitoring mechanisms. Experimental outcomes demonstrate that the adoption of precision technology within agricultural ecosystems substantially enhances crop productivity, minimizes unnecessary consumption of water and other agricultural inputs, and promotes environmentally sustainable farming methodologies. Furthermore, the proposed technology-driven agricultural framework illustrates the potential of intelligent monitoring systems to support precision-based cultivation, improve yield optimization, and contribute toward long-term ecological sustainability and resilient agricultural development.},
        keywords = {Precision Agriculture, Crop yield optimization, Machine Learning, CNN, Real-time Crop monitoring.},
        month = {June},
        }