Abstract: With increased pressure on India to achieve food security in the face of climate variability and scarce resources, the incorporation of Artificial Intelligence (AI) in conventional farm systems is a potential solution. This study investigates the use of AI-based crop monitoring systems for improving crop yields as well as reducing agricultural losses in Indian agriculture. By combining machine learning methods, IoT networks, and real-time data from field sensors, farmers are now able to recognize problems such as pests, nutrient gaps, or crop diseases much earlier than before. This allows them to respond with timely and precise interventions instead of relying on guesswork.

For this study, insights were drawn from published case studies and secondary data, which show how these technologies are making agriculture more accurate, resource-efficient, and productive. Beyond operational efficiency, AI-driven crop monitoring offers long-term benefits for sustainable farming in India. For instance, precision detection of pests and diseases helps reduce unnecessary use of chemicals, which in turn protects soil health and lowers environmental damage.
Another advantage is the real-time irrigation guidance that minimizes water wastage a crucial aspect for regions already facing shortages. When such tools are adapted to local farming practices and traditional knowledge, they create a useful blend of modern analytics and indigenous wisdom. This hybrid approach not only supports higher yields but also makes farming systems more resilient to climate variations and market uncertainties.

Keywords: Artificial Intelligence (AI) in Agriculture, Crop Monitoring Systems, Precision Farming, Climate Resilience, Sustainable Agriculture, Resource Efficiency.

Downloads: | DOI: 10.17148/IARJSET.2025.12907