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@article{172336, author = {YATA PRASHANTH}, title = {Harnessing Artificial Intelligence for Sustainable Agriculture and Environmental Insights}, journal = {International Journal of Innovative Research in Technology}, year = {2025}, volume = {11}, number = {8}, pages = {2847-2858}, issn = {2349-6002}, url = {https://ijirt.org/article?manuscript=172336}, abstract = {Agriculture plays a vital role in the global economy and food security, but traditional farming practices often result in inefficient resource usage, low productivity, and environmental degradation. This paper proposes an AI-driven agricultural system that integrates real-time environmental updates to address these challenges and promote sustainable farming practices. By utilizing Artificial Intelligence (AI), Internet of Things (IoT) sensors, and satellite data, the system aims to optimize resource use, increase crop yields, and reduce environmental impact. The system collects and processes real-time data on weather conditions, soil health, moisture levels, temperature, and other environmental factors. AI models are used to generate personalized crop recommendations based on local conditions, predict irrigation needs, forecast pest outbreaks, and offer tailored farming practices. The integration of IoT devices allows for precise monitoring of soil conditions and irrigation schedules, ensuring that resources such as water, fertilizers, and pesticides are used efficiently. One of the primary features of the system is its ability to provide farmers with actionable insights through a user-friendly mobile or web interface. Farmers receive real-time notifications on weather changes, irrigation schedules, pest management, and crop selection. These features help reduce the dependency on manual labor and outdated practices, enabling farmers to make data-driven decisions. The proposed system offers several key benefits, including improved resource efficiency, increased crop productivity, and enhanced sustainability. It enables farmers to adapt to changing climate conditions, reduce waste, and optimize their operations. Furthermore, the system is designed to be scalable and adaptable to various regions, with continuous updates and machine learning models ensuring its relevance over time.}, keywords = {Artificial Intelligence in Agriculture, Precision Farming, Sustainable Agriculture, Real-Time Environmental Updates, Smart Farming Solutions}, month = {January}, }
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