[Answered] Artificial Intelligence powered solutions can enable farmers to do more with less and improve farm productivity. In the light of the statement, highlight the applications of AI for agriculture and discuss the challenges in its adoption and implementation.

As per NITI Aayog, by 2035, Artificial Intelligence has the potential to add $1 trillion to India’s economy.  AI can enable farmers to extract and analyse information such as weather, temperature, water consumption or soil conditions through data collected directly from their fields.

Applications of AI for agriculture:

• AI sensors can detect weed affected areas to precisely spray herbicides preventing over or under-use of herbicides.
• Predictive insights such as timing for sowing for maximum productivity can help farmers reduce impact by weather and help in achieving goal of doubling income.
• Al-driven robots can be used to harvest huge volumes faster; be trained on data for specific crop variety, weather conditions & location, taking into consideration by products to reduce wastage.
• Al and Machine learning can help monitor crop health, diagnose pest/soil defects and nutrient deficiency on a real­time and predictive basis aiding farmers obtain higher yields.
• Al-based solutions trained on prior info &classification of plant diseases can help control disease.
• Output yield estimates and price forecasts will help farmers obtain maximum profits.

Challenges in its adoption and implementation:

• Policy upgrade: Yet-to-mature data governance and data rights regime. Lack of enforcement of data regulations, privacy and transparency.
• Trust deficit & hand-holding gaps: Risk-aversion and resistance to change, lack of trust in technology and insufficient support of universities and academics in data digitization and digital agriculture.
• Language and literacy: Language barrier including high illiteracy rates, and the digital divide. Lack of formal, non-formal and informal education in data engineering, data analysis and data science and insufficient proficiency.
• Tech connectivity: Lack of supporting ICT and data infrastructure which includes data collection, transmission, and insufficient digitization. Deficient telecommunication networks and poor internet connectivity, low band width, irregular and erratic electricity supply, and lack of data standards.
• Finance and investing: Insufficient capital invest in ICT devices and data infrastructures, and also lack of public investment to bridge gaps in data engineering, data analysis and data science education.
• Lack of awareness and clarity regarding return on investment in Al systems and no financial assistance schemes for small farms to adopt and deploy ICT devices and embedded systems.

Artificial intelligence has a lot of potential in the 21st century, especially for India. Indian agriculture is dependent on the monsoon system. AI can help prediction of monsoon behaviour and impact of climate change on monsoon.