Context

• Agriculture remains one of the world’s most pressing problems today. The increase in population has led to an increased demand for food. It is believed that 70 percent more food would need to be produced to meet the demands. 
• Therefore, a pressing need to find new ways to sustainably raise agricultural output, strengthen the international food supply system, cut down on food waste, and feed everyone hungry or malnourished has to be on the top of the priority list. 
• Therefore, the advancement of technology and particularly the use of this technology in countries across the globe to sustain and provide food security are imperative. 

Scope of AI

• Agriculture is a high-priority sector of the Indian economy, as 58 percent of the country’s families are dependent on it in some way, either directly or indirectly, for their means of subsistence. 
• Agriculture is one of the most fertile industries there are for artificial intelligence (AI) and machine learning (ML). AI, machine learning and the Internet of Things (IoT) sensors that provide real-time data for algorithms increase agricultural efficiencies, improve crop yields and reduce food production costs.

Areas with Maximum Potential 

• Cognitive computing has become the most disruptive technology in agricultural services as it can learn, understand, and interact with different environments to maximize productivity. 
• Microsoft is currently working with 175 farmers in Andhra Pradesh to provide agricultural, land and fertilizer advisory services. This initiative has already resulted in 30 per cent higher average yield per hectare. 
• Proximity sensing, remote sensing, IoT and image-based Precision Farming are being used for intelligent data integration related to historical meteorology, soil reports, recent research, rainfall, insect infections, along with drone imagery is being used for in-depth field analysis, crop monitoring and field surveys.
• Image recognition using artificial intelligence approaches for plant identification, pest infestation and disease diagnosis is also becoming prevalent. 
• Using AI and machine learning-based surveillance systems to monitor every crop field’s real-time video feed identifies animal or human breaches, sending an alert immediately can become very useful to prevent crop damages.
• Yield mapping to find patterns in large-scale data sets and optimizing irrigation systems to measure effectiveness of frequent crop irrigation is invaluable for crop planning.
• Today, there is a shortage of agricultural workers, making AI and machine learning-based smart tractors, agribots and robotics a viable option for many remote agricultural operations that struggle to find workers. These robots can harvest faster, locate and remove weeds more accurately, and thus reduce operating costs and dependence on labour. 
• In the meantime, farmers are already turning towards chatbots for help. Chatbots help farmers by answering their questions and provide advice and guidance on specific agriculture and yield related queries.
• The use of technology has also spread its wings to allied activities such as dairy farming. Artificial intelligence (AI) has emerged as one of the most important strategies ever developed for enhancing the genetics of farm animals. Its most prevalent use is in dairy cow breeding. The use of artificial insemination protects sires from contracting contagious illnesses, which in turn lowers the likelihood that a disease would spread.

Challenges

• Artificial Intelligence systems require a lot of data to train machines and make accurate predictions. It is difficult to find temporal data for large agricultural areas, although spatial data are easy to collect. 
• Since data infrastructure requires maturity, it takes time to develop a powerful machine learning model. 
• Another important disadvantage is the inflated cost of the many different solutions available in the agricultural market. Solutions need to be more affordable and open-source so that technology can be accessed even at the farm level. 

Way Forward

• With consistent efforts and scalable innovations by both the public and private sector, these technological interventions can completely overhaul agriculture and change the lives of farmers, for better.