An Accenture study reveals that digital farming and connected farm services can impact 70 million Indian farmers in 2020, adding $9 billion to farmer incomes.
By Naveen P Singh & Ranjith PC
Technology in agriculture can be viewed as an essential input that influences crop productivity. Arable land across countries cannot be enlarged; the only viable option is to increase the productivity of land through technology. Sluggish technology adoption coupled with low pace of skilling have raised doubts over productivity concerns, thereby adding to woes of farmers.
Many farmers rely on experiential knowledge or traditional wisdom, which is less effective in curbing farming challenges. Artificial intelligence (AI) with simulated algorithmic computer models that mimic human behaviour should be considered. The process begins by imagining an installed application that guides farmers through the process of growing, sowing, harvesting and sale of produce. Repository of decentralised digital data helps in monitoring, implementation and enhancing grass-roots effectiveness of schemes. Globally, AI has provided wider opportunities to farmers, organisations and governments. In Tanzania, using Google open-source disease data, AI discovered diseases with 98% accuracy and deployed robotics to uproot weeds. It is early to gauge the impact of AI in numerical terms, but the technology possesses potential in bringing positive transformation across agricultural landscape.
In agriculture, prediction is still complex and elusive. Marketing is the critical factor in driving farmers from poverty to prosperity, with two fundamental aspects—grading and pricing. Produce needs to be graded according to physical standards through automated quality analysis of images to avoid any bias and export rejection. Algorithmic models composing of participants and market behaviour to price movements of various years tell us the likely price in the near-term, thereby avoiding distress among farmers. Another element in predicting price and streamlining the flow of agri-commodities is supply-chain management. It brings radical change in surplus absorption provided by AI-aided software that meticulously manages commodity exchange among the stakeholders in the market.
Nearly 89% of ground water extracted for agriculture is plagued with management concerns, raising doubts over its conjunctive. AI models store crop-specific moisture requirement and assess the moisture content in the fields using satellites and signal farmers through text messages for water requirement through auto-irrigation. In a situation of deteriorating soil health and yield gaps across different crops and regions, it is imperative to leverage AI to retain healthy soil to feed the generations ahead. The NITI Aayog’s recent caution about severe water crises is a signal in this direction.
Digitalisation of agriculture is on its path, even though it has been ranked bottom in the industry surveys on the state of digitalisation. Digital India reaching villages through OFC is an essential input in leveraging AI techniques in agriculture. With nearly 30 million smartphone-owning farmers and an expected increase of internet usage in rural India to 315 million by 2020, it is an easy way to connect, communicate and coordinate. An Accenture study reveals that digital farming and connected farm services can impact 70 million Indian farmers in 2020, adding $9 billion to farmer incomes. Initiatives to increase digital literacy in the rural landscape can be a game-changer and possible early realisation of doubling farmers’ income. NITI Aayog’s Statement of Intent (SoI) to develop and deploy AI to provide real-time advisory to farmers in aspirational districts is laudable at this juncture and should be extended across the length and breadth of the country.
Indeed, technology in India has travelled several miles touching the livelihood of 1.3 billion people. However, agriculture still remains underdeveloped in terms of bridging yield gaps and ameliorating supply-side constraints. Pragmatic assessments of demand and supply, market intelligence, crop competitiveness and regional crop planning can come handy with AI. Finally, for realising futuristic benefits of AI across all stakeholders in agriculture, synergy between central and state governments needs to be dovetailed.
Authors are with the ICAR-National Institute of Agricultural Economics and Policy Research, Delhi
Views are personal