AI for Indian Farmers: Transforming Traditional Farmers into Technology-Driven Farmers
As per the Food and Agricultural Organisation(FAO) of the United Nation report in 2016,- Agriculture accounted for 23% of GDP and employed 59% of the country’s total workforce. However, India still has many growing concerns. As the Indian economy has diversified and grown, agriculture's contribution to GDP has steadily declined from 1951 to 2011. While achieving food sufficiency in production, India still accounts for a quarter of the world’s hungry people and is home to over 190 million undernourished people. The incidence of poverty is now pegged at nearly 30 percent. As per the Global Nutrition Report (2016), India ranks 114th out of 132 countries on under-5 stunting and 120th out of 130 countries on under-5 wasting, and 170th out of 185 countries on prevalence of anaemia. Anaemia continues to affect 50 percent of women including pregnant women and 60 percent of children in the country.
While agriculture in India has achieved grain self-sufficiency but the production is resource-intensive, cereal centric, and regionally biased. The resource-intensive ways of Indian agriculture have raised serious sustainability issues too and would definitely need a realignment and rethinking of policies.
India has enough food; does it have too many people working in agriculture? The pressure on land is an outcome of policy, which condemns most people to marginal farming. India needs a different set of solutions for agriculture and for those working the land.
Being an Engineer, when I read this report of FAO it made me think that one main reason for the intensive resource usage could be the way of farming. Most of the farmers in India are typically following a traditional approach or what they have been taught by their forefathers. But as climate change is happening it is becoming very hard for farmers to follow the traditional way of farming.
On the other hand, as the cost of cloud, sensors and computational power has come down drastically in recent years, Artificial Intelligence(AI) has come in to solve these dynamically changing problems with data-driven solutions in a cheaper and efficient way with precise accuracy.
Technology-Driven Farmers
The pace of technological advances has redefined farming over the years and has affected the agriculture industry in more ways than one. In recent years, Indian farmers are being guided using different techniques like the Internet of Things, AI, etc., to plan the usage of resources, chemical sprays, etc., which in turn optimizes the resource usage. The intelligent way of predictions using these AI-driven technologies is gradually transforming Indian Farmers to use crop-specific precise decision making in their field.
Some major applications of the AI-driven technologies in agriculture are:
- Microclimate Forecasting — India has different climate conditions due to its vast size and marked variations in terrain. Microclimate variables like temperature, humidity, precipitation are sensitive to land surface properties and land-atmosphere connections making microclimate forecasting more difficult. Getting the accurate microclimate around the crop canopy allows farmers to potentially minimize disease and pest incidence, effectively manage crop growth, optimise resources and plan optimal harvest time. Based on past days of data, different time-series model are being proposed in recent researches for better prediction of microclimate parameters like temperature, humidity, rainfall, etc. They outperform local weather forecasts available to farmers globally, either by the meteorological department or universities or some weather app, as they provide weather forecasts for larger areas. We at Fasal have also developed our own AI based microclimate forecast models the details of which could be found in our previously published article on AI based microclimate forecast and doppler radar based local rainfall prediction.
- Irrigation Management — As per the Ministry of Water Resources report, water use efficiency (WUE) in Agricultural sector is only 38% in comparison to developed countries with a WUE of 50–60%. Low WUE or overwatering not only wastes water but also damages roots and causes the plant to slowly starve to death. AI can help in developing precision irrigation model allowing the application of water and nutrients to the plant at the right time and place in small measured doses in order to provide it with optimal growing conditions. For more details please refer to our article on Fasal’s technology on irrigation management of vineyards based on real-time data to maintain suitable soil moisture levels at different crop stages.
- Disease/Pest Monitoring —Changes in climate are having a significant impact on agricultural pest/disease occurence. There is a serious risk of crop economic losses, as well as a challenge to human food security. Timing is crucial when it comes to protecting crops form pest/diseases and knowing what to expect before it infects their fields. Prevention is most often the best treatment option. The crop-specific diseases are closely related to microclimate changes and the use of modelling prediction tools could help in their early detection. In one of our previous articles, we explain how Fasal has developed dynamic models for Powdery Mildew by tracking pathogen growth and their development, and infection level in the crop and continuously notifying the farmer through the app. Fasal has now >50 different pest/disease models that help our farmers to take up preventive sprays at the right time, thereby, reducing the damage to the crop as well as reducing the unnecessary traditional way of repetitive sprays.
- Remote Sensing using Satellite Imagery — Images taken from satellites provide a means to assess crop conditions without physically touching them. These images can be used to identify nutrient deficiencies, diseases, water deficiency or water logging, weed infestations, insect damage, and plant populations. Addiitonally, these images can be used as base maps in variable rate applications of fertilizers and pesticides allowing farmers to treat only affected areas of a field. Problems within a field may be identified remotely before they can be visually identified. More details on how satellite imagery is being used for crop health assesment could be found here.
“I believe if a car can be driven without a driver why not an Indian farmer can change their gear to tech-driven farmers and eventually make ourselves as hunger-free country.”
