Sustainable Rice Farming: Smart Solutions for Effective Crop Management

Data has become a critical element in modern agriculture, aiding growers in making crucial decisions. Current advances in ICT are revolutionizing agriculture with the emergence of smart farms. Technologies like Remote Sensing and Proximity Sensing (Internet of Things IoT) are driving Agriculture 4.0, generating vast amounts of valuable and precise information. This data, available at the field level, needs to be analyzed using data analytics to extract meaningful insights. The next era, Agriculture 5.0, involves the integration of Artificial Intelligence platforms with machine/deep learning algorithms to provide farmers with intelligent decisions. These platforms offer detailed information on soil, crop status, and environmental conditions, enabling precise applications of phytosanitary products. This results in reduced use of herbicides and pesticides, improved water use efficiency, and increased crop yield. Rice is a major food crop in India, but its average productivity is still low due to the diversity in growing environments and production constraints. Site-specific management technologies are required for individual farms and fields through the adoption of precision technologies to improve rice productivity. Innovative precision technologies like the Internet of Things (IoT), Remote Sensing, GIS, and integrated crop models are needed to enhance the quality of farmers' decisions. The ICAR-IIRR (Indian Council of Agricultural Research - Indian Institute of Rice Research) has developed a Spatial Rice Decision Support System (SRDSS) by integrating the Oryza2000 crop model with Remote Sensing and GIS technologies. The paper focuses on the integration of Remote Sensing, GIS, weather sensors, crop models, and AI-based climate models with SRDSS to generate site-specific management advisories for achieving optimum yield in rice crops. In summary, the ICAR-IIRR has developed smart technologies that integrate with the Rice Decision Support System, enabling farmers to make informed decisions based on intelligent precision models, historical databases, and logical patterns generated by Artificial Intelligence. These models facilitate site-specific choices on varieties, sowing dates, inputs, forecasting pests and diseases, and more, enhancing rice productivity and sustainable rice farming.