Observational analysis showcases AI's role in enhancing climate resilience in paddy farming, highlighting challenges and solutions.
Paddy cultivation in coastal Odisha represents both a lifeline for rural communities and a sector increasingly vulnerable to climate change. Frequent cyclones, saline water intrusion, erratic monsoon patterns, and soil degradation have made traditional farming practices less reliable and sustainable. In this context, Artificial Intelligence (AI)-driven precision farming emerges as a transformative approach to enhance climate resilience, resource efficiency, and yield stability. This article examines the potential and practical applications of AI-enabled technologies in coastal paddy ecosystems, drawing insights from on-going practices, pilot projects, and farmer experiences in Odisha. AI-powered tools such as satellite-based remote sensing drone imagery and Internet of Things (IoT) sensors enable real-time monitoring of soil moisture, nutrient levels, and crop health. Machine learning algorithms facilitate predictive analytics for weather forecasting, pest and disease outbreaks, and yield estimation, thus empowering farmers to make data-driven decisions. Automated irrigation systems and AI-based advisory platforms further optimize input usage, reducing water wastage and minimizing dependency on chemical fertilizers and pesticides. These interventions collectively strengthen the adaptive capacity of smallholder farmers, reduce crop losses, and ensure more sustainable production under climate uncertainties. The article also highlights the socio-economic dimensions of AI adoption in precision farming. While technological innovations offer immense promise, challenges such as limited digital literacy, infrastructural gaps, high initial costs, and uneven access to technology remain significant barriers in coastal Odisha. Therefore, integrating AI-driven farming with community-based training, government support schemes, and localized research is essential for equitable and scalable adoption.
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Sahoo et al. (2025) studied this question.
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