Sowing the ‘AI’ seed for intelligent farming 

Source: The post is based on an article “Sowing the ‘AI’ seed for intelligent farming” published in The Times of India on 1^st August 2022.

Syllabus: GS 3 – E-technology in the aid of farmers.

Relevance: Application of the Artificial Intelligence in the Agriculture Sector

News: The Telangana government has promoted the use of Artificial Intelligence (AI) in its agricultural innovation program

John McCarthy, American computer scientist, first introduced the world to the term “artificial intelligence” at the 1955 Dartmouth Conference.

Why do we need AI in the agricultural sector?

India is expected to surpass China by 2023 to become the world’s most populated country. Therefore, there would be immense pressure to feed such a huge population base.

According to the Indian Council of Agricultural Research (ICAR), by 2030, the demand for pulses, cereals, rice, eggs, fruits, vegetables, and milk will be more than twice, in India, of what it was in 2000. While the Demand for food grains is expected to jump by more than 85%.

According to NITI Aayog, AI has the potential to add $1 trillion to India’s economy by 2035. And, as per some experts and academicians, a significant amount of this would be in the agriculture sector.

Application of the AI in agriculture

(1) It can help in efficient and cost-effective resource and yield management in the agricultural sector.

(2) AI, cloud computing, satellite imagery, and advanced analytics, in combination, can create an ecosystem for smart agriculture.

(3) It can be useful in prediction analysis. It will ensure the highest possible yields based on the seasonal forecast models. For example, it can enable farmers to extract and analyze information such as weather, temperature, water consumption, or soil conditions through data collected directly from their fields.

(4) It has the potential to address supply-demand mismatch in real-time. For example, a supply-demand engine or predictor that can map supply and demand can reduce this issue significantly.

(5) Artificial intelligence can help in precision farming by determining whether pesticides and weedicides should be used by detecting and targeting weeds in the identified buffer zone. This can lead to higher yields and reduced use of pesticides and weedicides.

(6) AI-based natural language translation facilitates the issuance and spread of Agri-advisories, weather forecasts, and early warnings for droughts in multiple vernacular languages.

(7) The use of image recognition using AI approaches for plant identification, pest infestation, and disease diagnosis is also becoming prevalent.

What are the challenges in the AI application to the agricultural sector?

(1) The lack of proper infrastructure and know how, faith in conventional styles of functioning, lack of awareness and scarcity of farmer capital,

(2) The fragmentation of land could also prove to be a hurdle for large-scale implementation of new technologies.

Measures Taken for the application of AI in agriculture

The ICAR is looking at cyber agro-physical systems to make Indian farming a viable, self-sustaining, and internationally competitive enterprises.

The NITI Aayog identifies agriculture as one of the focus areas as part of its national strategy for AI.

Several states are serious about AI in agriculture. For instance, (1) Karnataka has partnered with a leading MNC for agricultural produce, price-related information, and intelligence using predictive modeling, (2) Uttar Pradesh is collaborating with the Bill and Melinda Gates Foundation (BMGF) and the Tata Trust to set up the Indian agritech incubation network at IIT-Kanpur (3) Maharashtra has launched the Maha AgriTech project that is aimed at utilizing and promoting the application of satellites and drones to solve various agrarian problems.

What should be done?

It is high time that collaborative Agri-data stacks are created and MSME and large corporations invest in this space.

There is a need for the right mix of participation from public and private institutions. For example, Data coming in from the government side is not accurate, not updated frequently, and is noisy. Therefore, the private sector has incentives to make data accurate as they are making decisions based on it.