+ Artificial Intelligence in Indian Agriculture - CII Blog

The agriculture and allied sectors are considered the bedrock of India’s economy. With farming employing almost half of India’s workforce, Agri Gross Domestic Product (GDP) can be considered the engine of growth for the economy.

The global need to produce 50% more food by 2050 cannot be accomplished if only 4% of the land is under cultivation.The vulnerabilities arising from climate change, coupled with the risk of increased dependency on unsustainable agriculture practices, can lead to agricultural distress.

Artificial Intelligence (AI), along with other digital technologies, will play a key role in modernizing agricultural activities and realising the goal of doubling the farmer’s income by 2022. The global ‘AI in agriculture’ market size is expected to be worth USD 2.6 billion by 2025.

India’s national AI strategy also identifies agriculture as one of the key areas where AI can enable development and greater inclusion. AI-enabled solutions in agriculture help farmers improve crop productivity and reduce wastage. The Confederation of Indian Industry (CII) is working towards developing the agriculture sector in line with the country’s aspiration. Upgrading the technology quotient in agriculture is a key focus area. A CII-Deloitte report on ‘Artificial Intelligence – Augmenting Human Intelligence’ also highlights the role of AI in agriculture.

So how can AI help in agriculture?

Improving crop productivity – Climate change has resulted in making traditional agricultural know-how outdated, especially for forecasting weather patterns that determine farming practices for the season. The usage of predictive analysis with the help of AI could be extremely helpful for farmers. It could help determine appropriate crops to grow in a favourable climate on a productive terrain and the sowing methodology to enhance productivity and reduce costs.

In Andhra Pradesh, India, with the help of a sowing app powered by AI developed by International Crops Research Institute for the Semiarid Tropics and Microsoft, a 30% higher average in yield per hectare has been seen.

Soil health monitoring – Along with favourable weather conditions, soil health comprising of an adequate level of moisture and nutrient holds the key to getting the best yield. Distributed soil monitoring performed via image recognition and deep learning models can be used to take corrective measures to restore soil health.

Historical data about monsoons, local snapshots of the farm, crop-output information, history of soil health, and more serve as inputs for the creation of AI models. These models provide vital information about the farmland, assisting farmers in planning activities related to soil restoration, crop growth, farm watering, etc.

Optimization of pest and weed management – AI can be used for predicting the behaviour of pests which can be beneficial for advanced planning of pest control. Efficient pest management leads to lower crop and environmental damage. A combination of remotely sensed data, efficient image classification tools, weather data, and other relevant data points can be used to distinguish the weed from the crop. This will confine the usage of weedicide only to the areas that require treatment.

Remote satellites can monitor crop health and also warn against pest attacks. An AI-supported technology called ‘See & Spray’ developed by a US company is a weed controlling technology that can reduce expenditure on weedicides by 90%.

Water Management – Efficient water management in agriculture can have a huge impact on the looming problem of water scarcity. Water usage in agricultural land can be optimized by using thermal imaging cameras that continuously monitor if crops are getting sufficient amount of water.

AI, coupled with appropriate image classification models, when used in agriculture can result in improving yield production, reducing manual intervention, and decreasing instances of crop diseases.

Price realization for farmers – Only about 6 percent of farmers in India get benefits of Minimum Selling Prices (MSP). A better price realization for farmers is possible through an effective price discovery model. Predictive modelling using AI can be instrumental in presenting more accurate demand-supply information and predicting demand for agricultural produce to farmers.

With more than 500+ AgriTech start-ups in India, the agritech momentum is gaining pace in India. Many of these start-ups are leveraging technologies like AI, machine learning, etc. for improving efficiency, yield, speeding up agricultural finance, and other functions that are vital for India’s agricultural growth.