India has benefitted immensely from the farming-technology pairing. Until the year 1990, India’s Green Revolution was at its glitzy best, but then IT and Corporate banking started taking its shine away. Reason: Rural employment was hard to manage as the urban economy saw a visible influx of population from the farming sector.
India’s population was 1,354,051,854 at the end of 2018. The median age of the country’s population is 27 years and 5 months. In 2017, 66.7% of the total population belonged to the rural sector. According to the latest report from the International Labour Organisation (ILO), India will again see its unemployment rate at 3.5 percent in 2019. That puts immense pressure on the existing job market and resources in the country. In a challenging economic scenario, we are forced to ask this question -Could AI ML and IoT solve the perennial job crisis in rural India?
According to the latest update from Broadband India Forum, IoT and AI hold the keys to India’s rural economy. The technology could provide close to 3 million jobs in the next 8-10 years. Out of which 2 million jobs would open up in primary and secondary agri-based sectors. It is evaluated at $9 billion for the next 5 years.
Once we understand the various ways AI help farming, we can move to the job crisis. There are numerous ways AI ML could benefit rural sector.
AI in Agriculture Market: Insights Up Front
How India could benefit from AI’s involvement in its agri-based sector?
Powered by AI in agriculture, rural India’s economy could turn into an emphatic ecosystem. AI ML and IoT could play a specific role at various steps in the supply chain of farming, crop planning, weather, employment, soil testing, and processing. In the US, farmers are already using primary AI and robotics in agriculture that helped to improve crop productivity ratio per acre of farming land. In the midst of matured Green Revolution and infancy of Industrial 5.0, India’s rural economy could beat the job crisis by deploying AI ML, IoT, and Robotic automation.
Why does the rural India job market continue to suffer?
Agriculture in India is still seasonal. A large part of the agri-based job exists in cycles of crop plantation and harvesting. Nearly 80% of the farm workers and related workforce are engaged and disengaged based on the seasonal aspects. By leveraging AI ML capabilities offered by IBM and Microsoft are yet to be fully capitalized in India rural sector.
But, things are changing.
Recently, India’s Agriculture Ministry reached out to IBM Watson for weather forecasting. As per the press release —
“Ministry of Agriculture and Farmers Welfare signed a Statement of Intent (SoI) for undertaking a pilot study in 3 districts of Bhopal, Rajkot and Nanded in the States of Madhya Pradesh, Gujarat and Maharashtra respectively, with IBM India Private Limited. IBM’s Watson Decision Platform will give solution in the field of agriculture through Artificial Intelligence (AI) and weather technology at village level/ farm level to provide weather forecast and soil moisture information on a pro bono basis to help farmers for taking decisions regarding water and crop management for better production and productivity.”
IBM Watson could play a major role in the way AI ML and IoT could help analyze the impact of weather and soil moisture. In the end, it’s how farmers are made aware of these technologies. Farmers could be the new data scientists, especially with the right information and data available in their repository.
Here are a few flagship farming roles that rural population could find themselves employed in by 2025.
Drones are the future of agriculture. IoT and AI-enabled drones could help identify the various areas that need immediate human attention. Drone manufacturers can employ rural population, train them and help manage the agriculture drones. These drone operators can help to identify pests, flooding, drought, and crop wastage. One drone operator could cover 250 acres of land depending on the computer vision and image recognition capabilities offered by IoT-Drone makers.
Precision Animal Husbandry and Agriculture
Also called PA, we could see Animal Husbandry and Agriculture powering the job market in rural India. Precision Agriculture could help identify the best mating seasons for various cattle and avian population. The production of milk, fodder, eggs, meat, and leather could grow exponentially with the inclusion of Predictive science in farming and animal husbandry. Companies like Intel, Google AI, IBM Watson, and Microsoft could play a greater role in bringing PA to the center of Agricultural AI ecosystem.
Executives in Micro-Seeding Projects
Like Micro-financing, we would find at least one-fifth of the rural India population engaged in micro-seeding projects. The executives in Micro-seeding projects could come largely from the educated segment of the rural population who are basic Science graduates. These Science graduates could be trained in government-funded AI IoT programs. AI Machine Learning use case in agriculture would open the job market for educated youth with rewarding career opportunities aligned with the rural and agri-based infrastructure.
Data Visualizers for Farm Planning
Indian farming remains highly unstructured, largely because of the piece-land reservations. Add to it the challenges of deforestation, urban chaos, and river line transitions. Data visualization companies could employ a large part of the rural population who understand the farm planning roadmap better than others. By leveraging Robotics in Agriculture and Machine Learning use cases in Agriculture, we could see data visualization becoming the center stone of every farm planning project in India.
Programs like Microsoft AI for Earth is an example. The rural economy could be trained to work with AI for Earth projects to assess the risk of drought, flooding, and wildfire.
Planning the Next Green Revolution
The next Green Revolution is already underway and the best thing about this revolution is its Digital Imprint powered by AI, ML, and Robotics in Agriculture. When the job market opportunities sponge in the traditional challenges, we will see the rural economy run more efficiently to produce not just high-yielding crops but also well-paid agri-based careers in India.
To conclude part one discussing the role of job crisis, we can optimistically say that AI ML and IoT can no longer be ignored for the semi-urban and rural sectors in India.
We will train our eyes on other growing economies in Asia, Africa, and South America to see how they plan to deal with the job crisis using latest technologies and AI RADAR companies.