At two ends of the world, in Tokyo and San Francisco, fully automated “vertical indoor farms” powered by artificial intelligence (AI) technologies and operated by robots have sprung up, bringing the idea of “next-generation control environment agriculture” to life.
As countries in the global North grapple with a shrinking agricultural labor force, robots are being tested and trained to pick fruit by tedious trial and error methods. Like manufacturing and services, a rising tide of data-driven disruption work on a new layer of knowledge activities enabled through AI technologies is also transforming agriculture. With the challenges of resource scarcity, industrial-scale food wastage and climate change becoming urgent imperatives for food security, digital innovations are being seen as game-changers to be able to address these issues. The future of food is unequivocally digital, and the future of digital is inevitably AI.
Broadly, AI can be defined as “a field of computer science dedicated to developing systems that can learn or be taught to make decisions and predictions within specific contexts.” AI applications can perform a host of intelligent behaviors such as process optimization and predictive modeling, based interaction modeling on pattern recognition, natural language processing, and machine translation. All these capabilities funnel into the power of data and algorithms, the key drivers of Industry 4.0. Consider the following facts. Deloitte estimates that by 2019, 70 percent of companies will acquire AI capabilities through cloud-based enterprise software. By 2025, more than 85 percent of all businesses will have effectively transitioned to the cloud. Cloud computing will thus be able to drive large-scale AI implementations with more assured returns on investment across verticals. This will have implications across a number of sectors, including education, healthcare, criminal justice, and agriculture.
Today, terms such as “digital farming,” “the use of new and advanced technologies integrated into a system to enable farmers and other stakeholders within the agricultural value chain to improve food production”7 and “precision agriculture,” where temporal, spatial and personal data are combined with other information to make informed decisions, from instruments and sensors that generate data and image recognition technologies that assay and grade crops and commodities, AI applications are being deployed in various aspects of agriculture.
Broadly, AI in agriculture spans three categories: • Agricultural robotics; This includes the development of autonomous and intelligent systems that can perform tasks and functions on farms such as sowing, irrigation, harvesting. For example: Blue River’s ‘See and Spray Herbicide Robot’. • Crop and soil monitoring: This involves the use of data through drones, sensors, GPS chips, etc. to monitor crop and soil health through computer vision and deep learning techniques • Data capture and processing. For example: Plantix, a deep learning application that can assess soil health through image recognition. • Predictive Analytics: This involves building predictive models and digital intelligence around a host of agro-parameters, including inputs, market prices and linkages, and can also apply to allied services, such as credit and insurance, fintech, logistics, etc.
Additionally, data and AI-based innovations are also rapidly transforming agriculture operations. Augmented reality, voice activated transactions, smart packaging, robotized warehouse management and omni-channel distribution are some of the advancements we can point to.
To understand this phenomenon in economic terms; between 2012 and 2017, global investments in digital technologies for food production tripled to reach an impressive US$10 billion. Data-based value propositions have been key to all of these ventures. We observe two major patterns in this sector: 1. The rise of agritech startups, fueled by venture capital, aspiring to claim a stake in the AI market and 2. Traditional giants that have reshaped their business models through datafication.
When Monsanto acquired digital agriculture startup, The Climate Corporation, in 2013, it took the first step towards redefining itself as a “data company.” Only a few years later, Bayer acquired Monsanto, expanding its intelligence capital – the latter combining data on soil and crops with its own knowledge in pharmacogenetics. Similarly, John Deere’s decision to expand the company’s investment in AI startup Blue River to power the development of unmanned tractors points to a next wave in agriculture. Deere’s website notes how their future market depends on AI; “As a leader in precision agriculture, John Deere recognizes the importance of technology to our customers. Machine learning is a key capability for Deere’s future.”
This trend is also observable from the other end. In 2018, Chinese digital company Huawei established an Agricultural Internet of Things Global Joint Innovation Center within the Qingdao Saline-Alkali Tolerant Rice Research and Development Center in China. The center is working on developing an “agricultural fertile soil platform” and is focused on developing smart agriculture solutions through IoT, big data and cloud computing. In the US, Alphabet Inc, Google’s parent company is investing in startups, such as Farmers Business Network and Bowery Farms.
In allied sectors, a growing global trend is expected to drive greater digitally enabled value additions to the farm to fork supply chain, with grocery e-tail emerging as an important market segment. Not least in the food retail market are agribusiness giants, such as Kellogg’s, which have now donned the hat of venture capital funds in an apparent bid to capture a piece of a digital ecosystem that is rapidly changing consumption habits.
These are not just developments in rich countries, but are indicative of the restructuring of the sector at a local as well as global level. Research by ETC Group has noted that data-based business value proposition has been the driving factor for the rapid transnational consolidation going on in the industrial food supply chain across various verticals, including farm machinery, seeds, agrochemicals and pesticides, with many incubating in startups in India such as Africa Regional Data Cube.
From scaling up crop science breakthroughs to solution building for effective resource optimization, it is clear that AI innovation will be the key way forward for global food production, more so in the Global South countries, where agriculture is still the economic mainstay. But how this innovation tide will be harnessed and made to work for the benefit of all actors, including small land-holders, women farmers, etc., remains a question and challenge for policy.
In subsequent sections, we examine current initiatives in AI in Indian agriculture—both private and state-led—and attempt to assess their potential to drive development. We begin with a brief overview of the state of agriculture in India, the Indian state’s policy thinking on the role of AI in agriculture, and the key constraints on its effective adoption and uptake. This is followed by an analysis of private sector trends in the domain. Finally, prospects for AI in agriculture are discussed, along with directions for policy. The insights offered in this paper draw from interviews with AI startup founders, digital platform companies, knowledge experts, and public officials, as well as secondary research. The framework we attempt here is not exhaustive and is limited to an initial scoping of current approaches to the domain.
The Current State of Indian Agriculture
AI in agriculture is seen as a key focus area for policy. This is perhaps logical, given that for decades, policy processes have failed to respond to an “agrarian crisis” marked by extreme hardship for most of the country’s farmers. Droughts, along with other factors including falling agricultural produce prices, low public investment in agriculture, and declining agricultural exports, have exacerbated this situation.
Grassroots movements have sought to highlight and draw attention to the issue. In 2017, farmers from the southern state of Tamil Nadu demonstrated in the nation’s capital over the lack of drought relief measures and rising farmer suicides. The year 2018 saw two “long marches” in which more than 50,000 farmers took to the streets to voice their concerns and advocate for the passage of long-overdue sectoral reforms outlined in the Ms Swaminathan Report, 2005.
Uncertainty remains a hallmark of the sector, with prospects particularly bleak for small and marginal producers, women and landless farmers, who comprise a large segment of the population whose livelihoods are tied to agriculture and today find themselves locked out of the sector altogether. Unreliable market linkages and access to inputs, volatility in commodity prices, depressed returns and high indebtedness (fuelled by a predatory microfinance industry) are contributing factors that make farming a high-risk proposition.
Incomes for farmers have been steadily declining since 2011 with the average annual income of a farmer and wage-labourers being 77,976 Indian rupees. On the other hand, non-agricultural activities now bring in close to 65 percent of rural household income. It is not surprising then that in a survey on the state of Indian agriculture, 76 percent of farmers reported quitting farming.
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