Artificial Intelligence in Agriculture

The government has started a number of initiatives employing artificial intelligence (AI) and the internet of things (IoT) in agriculture.

• Precision farming, agricultural drones and hopping systems, livestock monitoring, climate monitoring, smart greenhouses, AI and IoT-based computer imagery, and other applications are examples.
• To encourage innovation and agripreneurship by providing financial support and fostering the incubation ecosystem, the Department of Agriculture and Farmers Welfare nominated 5 Knowledge Partners and 24 Agribusiness Incubators.

Various Government Initiatives are as follow :

A National Mission on Interdisciplinary Cyber Physical Systems (NM-ICPS)

• It is being carried out by the Department of Science and Technology (DST).
• 25 Technology Innovation Hubs (TIHs) have been established across the nation in prestigious institutions of national significance in advanced technology verticals as part of the Mission.
• Funding is provided to State Governments for Digital Agriculture Projects Using Emerging Technologies Like Artificial Intelligence and Machine Learning (AI/ML), Internet of Things (IOT), Block Chain, etc. under the National e-Governance Plan in Agriculture (NeGPA) programme under the Department of Agriculture & Farmers' Welfare.
• Since 2018–19, the Rashtriya Krishi Vikas Yojana (RKVY) has run a programme dubbed "Innovation and Agri-Entrepreneurship Development" with the aim of fostering the incubation ecosystem and fostering innovation and agripreneurship.

What is artificial intelligence?

• Artificial intelligence (AI) is the emulation of human intelligence in devices that have been designed to behave and think like humans.
• Artificial intelligence has three main objectives: learning, reasoning, and perception. Example Self-driving cars, virtual assistants,etc.

Use of Artificial Intelligence in agriculture:

Use for predicting the weather:

• Using weather forecasting and artificial intelligence, farmers can plan the sort of crop that can be cultivated and the best time to plant seeds by analysing meteorological conditions.

Soil health surveillance system:

• The kind of soil and nutrition of the soil have a significant impact on the crops that are grown and their quality.
• Due to rising soil degradation, it is difficult to assess the soil's quality.

Evaluation of crop health & Pest Management:

• An AI-equipped drone can gather data from fields, which is then sent from the drone to a computer via a USB drive and examined by specialists from the government's agricultural department.
• An in-depth report on the state of the farm's health can be created by analysing the photographs that were captured.
• By identifying pests and germs, farmers can utilise pest control technologies and other timely measures to take the necessary action.

Predictive analytics and precision agriculture:

• By giving farmers appropriate guidance concerning water management, crop rotation, timely harvesting, the sort of crop to be grown, optimal planting, pest attacks, and nutrition management, AI applications can aid in correct and regulated farming.
• With equipment as basic as an SMS-enabled phone and the Sowing App, even the last-mile farmer may benefit from artificial intelligence.

AI-powered pest detection tool:

• AI systems employ satellite photos and historical data to determine whether any insects have landed and, if so, which species—such as locusts, grasshoppers, and others—have done so.
• So that farmers may take the necessary precautions, it can then send alerts to their smartphones.

Grading of Agricultural Products:

• An accurate and trustworthy way for grading fresh products (fruits, grains, vegetables, cotton, etc.) that are characterised by colour, size, and form is an automated quality analysis of photographs of food products.
• Without requiring any manual intervention, the farmer can use his phone to take a picture and instantly assess the quality of his produce.

Finding weeds:

• AI can effectively be used for differentiating between weeds and crop seedlings. It is one of the most efficient non-chemical ways of reducing weeds.

Irrigation:

• AI helps in irrigating agricultural fields. It can monitor and analyze the soil moisture and other conditions, such as aridity or humidity in the surrounding atmosphere, and then open water valves to provide water to the fields automatically, without any human involvement.

Agriculture Insurance:

• The AI-enabled agriculture insurance can help in the faster assessment of damaged crops and faster reimbursement of insurance to the farmers.

Conclusion

• Farmers' access to markets, inputs, data, advice, loans, and insurance will be improved through the use of AI in agriculture.
• An effective supply chain that is demand-driven and efficient may be built with the use of timely, reliable data and analytics.
• With the recent reforms in the agriculture industry, there is a chance that contract farming investments will expand and technology will be introduced for higher yields and productivity. This will encourage the use of AI in agriculture even more. Moreover, in order to assist
• With the recent reforms in the agriculture industry, there is a chance that contract farming investments will expand and technology will be introduced for higher yields and productivity. This will encourage the use of AI in agriculture even more. Increased investments are also required from the public and private sectors to assist these AI solutions scale.