Abstract

Digital agriculture and artificial intelligence (AI) are revolutionizing Indian agriculture by simplifying the process of utilizing the appropriate quantity of inputs, monitoring crops in real time, and connecting with markets. This is particularly beneficial for smallholders, as it enhances their productivity and resilience to climate change. India’s agricultural sector is the backbone of its economy, employing nearly 58% of the population and contributing around 17–18% to the national GDP. Despite its significance, Indian agriculture faces persistent challenges: fragmented landholdings, low productivity, climate vulnerability, and inefficient resource use. In this context, Digital Agriculture and Artificial Intelligence (AI) offer a transformative pathway to modernize farming, enhance sustainability, and empower farmers. This abstract delineates a comprehensive study design that utilizes quantitative econometric analysis and qualitative research to investigate the impact of digital AI interventions on quality of farmers' life, input efficiency, and agricultural productivity in India. The qualitative component investigates adoption pathways, barriers, and farmer perceptions through interviews and focus groups, while the quantitative component assesses causal effects using panel and instrumental variable techniques. In order to address the digital divide and optimize welfare benefits, policy proposals emphasize infrastructure, inclusive product design, and scalable institutional interventions. The study combines primary survey on approximately 2000 farmers across the country and secondary datasets.
Different facets of Digital agriculture in India
• Monitoring Crop Health: AI-powered systems look at satellite images, drone videos, and field photography to find early signs of disease, pest infestations, and lack of nutrients. AI is used to keep an eye on rice and wheat harvests, which makes it possible to act quickly and reduce agricultural losses.
• Smart Irrigation: A lack of water is a big worry. AI algorithms improve irrigation plans by looking at the moisture level in the soil, weather forecasts, and the specific water needs of each crop. The "Per Drop More Crop" initiative employs AI-enhanced drip and sprinkler systems to enhance water use efficiency.
• The National Insect Surveillance System uses AI and machine learning to find insect outbreaks that are linked to climate change. This makes it easier to respond quickly and lessens damage.
• Yield Prediction and Crop Planning: AI algorithms use past data, weather patterns, and soil conditions to guess how much of a crop will grow. These insights help farmers choose the best crops and plan their harvests to make the most money.
• Market Linkages and Price Forecasting: AI systems look at supply and demand and market patterns to predict prices. This helps farmers decide when and where to sell their crops.
• Chatbots and Advisory Services: Tools like the Kisan e-Mitra chatbot help farmers in several languages by answering questions on government programs, weather, and best practices.
• Pest Surveillance: The National Pest Surveillance System employs AI and machine learning to find pest outbreaks that are connected to climate change. This lets you respond quickly and limits the harm.
Government Initiatives Driving Digital Agriculture
• Digital Agriculture Mission (2021–2025): This flagship initiative aims to modernize farming through AI, blockchain, drones, and IoT. It focuses on creating a unified farmer database, enabling precision targeting of schemes, and promoting smart farming practices.
• AgriStack: A proposed digital ecosystem that integrates farmer data to streamline access to services, subsidies, and credit. It aims to personalize agricultural support and improve transparency.
• PM-Kisan & eNAM Platforms: These digital platforms facilitate direct benefit transfers and online agricultural marketing, reducing middlemen and improving farmer earnings.
Challenges in adopting digitalisation in agriculture
Despite the potentials, several challenges persist:
• Digital Divide: Many smallholder farmers lack access to smartphones, internet connectivity, and digital literacy.
• Data Privacy & Ownership: Concerns around who owns and controls farmer data remain unresolved, especially with centralized databases like AgriStack.
• Infrastructure Gaps: Limited rural connectivity, power supply issues, and lack of sensor networks hinder widespread adoption.
•Trust & Adoption: Farmers may be sceptical of new technologies, especially if they are not tailored to local contexts or explained in vernacular languages.
• Fragmented Landholdings: Precision farming tools often require scale, which is difficult to achieve in India’s fragmented agricultural landscape.
The authors acknowledge the constraints of digital agriculture, including challenges associated with cost, infrastructure, security, and knowledge deficiencies that must be resolved for effective deployment. The study has observed that digital agriculture has a bright future, with the potential to change how food is grown and eaten from farm to table, especially in India.