Driven by millions of smallholder farmers, India’s agriculture sector stands at the crossroads of mounting risk from climate volatility and pest outbreaks to rising input costs, undermining both farm productivity and livelihood security.
Pest related losses continue to pose a significant threat to India’s crop yields, impacting both food security and farm incomes across major agricultural regions. Smallholder farmers, who have limited access to timely interventions and resources bear the brunt of these losses. This underscores the urgent need for early detection of pest infestations, trend analysis, and proactive management supported by scalable and resilient strategies.
Traditionally, pest control methods in India have relied on manual field scouting, delayed advisories, and blanket application of pesticides. However, this reactive approach is increasingly ineffective in today’s dynamic agri-climatic environment. The unpredictable nature of pest cycles, exacerbated by changing weather patterns, has rendered conventional practices inadequate, often resulting in higher costs, crop damage, and significant health and environmental risks due to excessive pesticide use.
The emergence of Artificial intelligence (AI) is beginning to transform this landscape. Technologies such as drones, Internet of Things (IoT) sensors, edge computing and advanced data platforms are enabling the analysis of diverse datasets ranging from satellite imagery and local weather to soil health, and crop condition data. When these insights are processed through AI models, they generate timely, and actionable advisories for farmers. These real-time advisories, disseminated through mobile platforms and local networks, empower farmers to take swift and informed action, reducing input wastage and mitigating crop losses.
The Government of India has taken foundational steps to mainstream these innovations. Under the Digital Agriculture Mission, the National Pest Surveillance System (NPSS), has been launched as a national platform leveraging AI and geospatial data. The system is designed to provide district level pest forecasts, pre-emptive alerts, and early warnings building an intelligent surveillance infrastructure to enhance readiness and response.
AI-led transformation is no longer confined to pilot plots. Several Indian states have begun to implement these technologies at scale. Maharashtra has operationalised a structured pest surveillance and advisory system through its Crop Pest Surveillance and Advisory Project (CROPSAP). Integrated with the Maharashtra Virtually Integrated System to Access Agricultural Resources (MAHAVISTAAR AI App)1, this initiative delivers real-time advisories to farmers, enabling timely pest management decisions. Observations from cotton growing regions indicate a marked reduction in pest populations breaching threshold levels.
To build on this progress, Maharashtra has also launched the MahaAgri-AI Policy 2025–20292, which seeks to integrate artificial intelligence and emerging technologies, to address key challenges, including pest management.
Across other regions as well, AI-powered pest surveillance systems are being adopted for a range of crops including horticultural produce and field crops. These systems are helping enhance input use and preserve crop quality, offering clear evidence of the practical benefits AI can deliver in improving farm-level decision making.
What was once limited to pilot programmes is now influencing mainstream agriculture. AI-driven pest surveillance is being embedded within frameworks, supply chains, and on-ground agronomic practices. It is shifting the model from reaction to anticipation, enabling timely, targeted interventions that support sustainable and resilient farming.
This evolution marks a critical transition. As India continues to strengthen its digital agriculture infrastructure, the integration of AI into everyday farm decisions is poised to redefine how we manage productivity, risk and environmental sustainability, particularly for the smallholder farmers who form the backbone of Indian agriculture.
