Specialist, Data Policy and Blockchain, C4IR India, World Economic Forum
Agtech Principal Consultant, PwC
- Data is transforming the agricultural sector.
- Artificial Intelligence for Agriculture Innovation, a multi-stakeholder group at the World Economic Forum, has identified 11 agritech frameworks and 24 use cases or pilots to be explored further through evidence-based learning.
- From developing and deploying cutting-edge technology to scaling up solutions to transform the agriculture sector, private players across the world are working closely with various stakeholders to disseminate innovation.
Data availability, accessibility and quality are increasingly becoming the three key aspects of digital innovation in any sector, and they are already transforming the agricultural sector. Now, the Artificial Intelligence for Agriculture Innovation (AI4AI) group, a multi-stakeholder group at the World Economic Forum, has identified a set of 11 frameworks and 24 use cases or pilots to be explored further through evidence-based learning. Further responses collected through consultations and surveys across key stakeholders in the agri-ecosystem reveal innovative use cases of data against four key stages – access to inputs, advisory, finance and markets. The innovative use cases of the data against the four key stages of the value chain are depicted below:
This blog provides an overview of private sector innovative use cases demonstrated by the startup ecosystem of India and Germany for data-driven digital agriculture transformation across the value chain. Aspects of data availability, accessibility and quality for each of the use cases are discussed.
“Innovation in agritech is growing across the world. We are on the cusp of a 4IR [Fourth Industrial Revolution], especially in agritech. There is immense potential for data and technology adoption that can solve for farmers and businesses at once,” says Amulya Patnaik, Partner, PwC.
How tech startups enable data-led innovation in agriculture
Private investments in agritech are becoming more significant worldwide. Globally, a total of around $49 billion has gone to fund about 23,300 start-ups. India has received $4.2 billion for agritech start-ups up until 2022. While governments are enabling data-based innovation, the private sector plays a unique role in delivering these innovations. Three such innovative use cases by startup innovators are discussed below:
Data enablement for last mile optimization
For farmers, the fruit and vegetable market works on less than 5-6% gross margins and is constrained by the high cost of logistics. Ninjacart developed an in-house platform that has the computing capability to solve large, multi-dimensional optimization problems. This solution includes a custom version of open-door logistics. This is an open-source tool that is configured to match with real-time scenarios of delivery times, traffic simulations, vehicle types, speeds, availability, etc. The firm claims that tech-enabled solution showed promising results in terms of as much as an 80.7% reduction in cost-per-kg from 2016 to 2022 and from Indian Rupees 10.1 per kg to Indian Rupees 1.95 per kg. For small retailers, producing less than 20/kg per day in sales, this translates to about Indian Rupees 30,000/year as direct savings and around Indian Rupees 50,000/year in indirect savings.
Incomplete census data, varying nomenclature across multiple departments and states and a lack of granular data were highlighted as major hurdles in scaling towards a nationwide solution. Interoperable data among private players and triangulation from various sources to achieve a higher quality of data is considered crucial for the optimization of solutions.
“Our goal is to address the challenges faced by the agriculture sector, such as inefficiencies in the market, lack of proper storage facilities and limited access to customers. We believe that by working closely with all the stakeholders in the agri-value chain, we can create a more sustainable and profitable ecosystem for everyone. By using technology, we are able to address these issues and create a win-win situation for all, where farmers get fair prices for their produce, retailers are able to source high-quality products at competitive prices and consumers get access to fresh and nutritious food,” says Kartheeswaran Kandasamy, CEO and Co-founder of Ninjacart.
Data enablement for detecting real-time crop damage
According to the Food and Agriculture Organization, on average, pests account for 20-40% of yield losses worldwide, costing the global economy $220 billion. Plantix allows farmers to detect diseases in real-time as soon as a sample crop photograph is uploaded. With the help of AI and an existing database of images, Plantix has built a tool for analysing the image of a crop, plant or leaf to diagnose crop damage. A robust database with the capability to diagnose 500+ plant conditions across 60 crop varieties is the core of the solution. The firm assures us that test conducted by ICRISAT in 2022 with over 8,100 samples, covering 25 crops and 247 related diseases, revealed that the current disease detection success is at a 93% accuracy, subject to a good quality upload. Utilization of tools, such as Big-Query and Tableau, help with processing and visualizing internal data.
The lack of an integrated database on crop loss, crop images, food shortages, insufficient formal channels to aggregate advisory recommendations for farmers and incomplete data and inconsistency were highlighted as major bottlenecks in the implementation of the solution.
“Agriculture in its entirety is very complex. To operate in an economically and ecologically sustainable manner, one needs – especially as a small farmer – permanent access to knowledge and immediate and precise solutions to problems, as well as a direct connection to required inputs, e.g. via local retailers. Plantix has created a holistic ecosystem that brings all these elements and actors together,” says Simone Strey, CEO and Co-Founder of Plantix.
Data enablement for farm mechanisation and advisory
Integrated solutions, such as farm mechanization services and a holistic advisory to farmers, are complex processes that need data from multiple sources. Nurture.farm uses a four-layer data collection, validation and modelling ecosystem using remote sensors, drones, IoT devices, GIS, satellite imaging, on-ground data collection and machine learning to unearth, verify and validate ground truth data to offer customized advisory to the farmer. Nurture.farm offers credit solutions to 80,000+ retailers helping them manage their cash flows effectively and its digital ecosystem and services are available to 2.58 million farmers across India.
A lack of data, such as historical yield data, plot-level farm intelligence, incomplete digitization of land records and inaccurate data from open public portals were highlighted as major challenges for expansion and scaleup of innovation.
From developing and deploying cutting-edge technology to actively scaling up solutions to transform the agriculture sector, private players across the world are working closely with various stakeholders to disseminate innovation. They are best placed to develop data-based solutions and improvise on solutions for responsible data collection, sharing and utilization. At the same time, they will need considerable support from policymakers, governance and regulatory bodies and from the farming community at large to achieve a paradigm shift in revolutionizing the agritech sector.
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The views expressed in this article are those of the author alone and not the World Economic Forum.