Artificial Intelligence (AI) is transforming industries across the world, and the agriculture industry in India is no exception. Being one of the largest agricultural economies around the globe, the agri sector of India is faced with challenges of various sort: ranging from unpredictable climatic patterns to lack of rental lands to acting as poor labour providers and inefficient supply chains. AI provides a first boost in eradicating these issues from the very power to provide reliable and data-secondarily before crop production techniques with data accuracy.
This article discusses the AI applications and innovations in agricultural systems and uses, examples of their real-world applications in India, governmental initiatives taken to ensure high adoption of AI in agriculture systems and the challenges the face in ensuring high adoption.
Why Indian Agriculture needs Artificial intelligence?
Agriculture is cited as being the most significant sector of the national GDP (almost 18%), and is the employer of over 40% of the total labor force of the nation. However, this boundary is still notorious and efficient as far as the production potential, such as low productivity, unblockable yield and is affected by humidity and monsoon. Its crisis is exacerbated by a phenomenon that it is possible to elevate on an international scale as the climate changed earth, loss of farm lands and migration in the countryside.
Also, AI-incorporated ag-tech apps provide that empowering machinery and technologies for farmers that will take better circumstances: this comprises from weather forecasts and agricultural crop out puts to get an early head-up if there are any infestations. Apart from reducing the occurrence of human efforts, predictive analysis has also indicated results with maximizing the production level while conserving the resources.
Important Uses of Artificial Intelligence in Agriculture
a. Precision Farming
Precision farming technologies utilize the prowess of AI technologies like online sensors, satellite imagery, drones, to give real-time data on soil conditions, cropping and weather status. Machine learning models sift through this data and therefore help farmers to decide the amount of irrigation, fertilizer and pesticide they need while they are making decisions. So, it will lend a hand to the crop a better capacity to improve, and a reduction in input cost.
b. Crops will be Controlled by Predictive Analytics
AI models can assist to process the big data to get prediction of the optimum sowing and harvesting period. The forecasts are made after taking into account the historical weather patterns, rainfall patterns and temperature patterns so that the farmers can start planning in advance to reduce the climate change syndrome.
c. Pest and Disease Detection
The computer vision technology for pest attack images recognition based on AI, which can extract the preliminary information of the pest attack or plant disease image based on drone image and camera image.
d. Irrigation and management of water
Irrigation systems are demonstrated as an example of AI based irrigation system in which pressure data from soil moisture probe and weather pattern can automate the irrigation system. This is ensuring having a good crop hydration and at the same time not wasting water in a bigger way which is a perfect solution to the situation of drought in India.
e. Transforming Supply Chain with AI
AI can be used to streamline the supply chain for the agricultural industry by forecasting demands, optimally routing and minimizing post-harvest losses. Hence the farmers will receive all the live market prices, better price fixing and Electronic link between the farmers and buyers.
Significant Trends of AI in Indian Agriculture
India sees a big influx of agri-tech start-ups, and AI has been playing the most in meaning in resolving problems on their ground. Some positive illustrations are as follows:
- CropIn – AI based platform which offers crop based solutions like crop yield prediction, crop monitoring and sustainability measurement solutions by using AI.
- Fasal – sensor powered with Artificial Intelligence which give actionable data of irrigation, pest management and weather.
- Nexus AI – Provides a pest detection and advisory ranching service offering better protection for the crops using images.
- AgNext – It is focussed on providing quality determination in the food/agricultural commodities using AI technologies, making quality invisible in the transparent trade.
These agricultural technologies fall somewhere in between the traditional agriculture operations and digital agriculture. They offer farmers the benefit of what was put into the corporate offices of the large agribusinesses (all along).
Public Sector Modalities Ultimate Approach to AI for Development Agriculture
With the transformative potential of AI in agriculture now recognised by all, the Indian government has undertaken many initiatives in an effort to make the adoption of AI an easier one by way of learning-led programs:
- Digital Agriculture Mission 2021 – 2025: Google is building this agriculture digital ecosystem that would contain special focus on development of AI-based decision making products.
- National e-Governance Plan in Agriculture (NeGPA): NeGPA project has envisioned future utilisation of AI and Data Analytics in aiding to better planning and tracking of agricultural operations.
- Agriculture Electronics and Telecommunication (AET) Business Case Study NITI Aayog – AI for Agriculture Supports AI Research and interacting with the private sector to ensure that problems of Agriculture sector is resolved.
- Indian Council of Agricultural Research (ICAR): Models for predicting weather, considering pest issues (AI based crop forecasting) is put in place.
Benefits of the Introduction of AI in Indian Agriculture
Incorporating AI in the company of agriculture can make sure revolution of the whole ecology as follow:
- Reduced Costs: Reduced cost is one of the benefits of AI tools as it is efficient and uses devices such as fertilizers, water, and labor efficiently.
- Sustainability: Smart agriculture techniques are more sustainable due to the reduced need for the use of chemicals and natural resources.
- Market Access: With the help of AI-driven platforms the instrumental role of this platform connectivity between sellers and buyers with the producer and consumer interests are clinched appropriately and along with these the income of the farmer is also multiplied.
- Climate Resilience: Predictive analytics can help farmers have a competitive edge in the changing climate conditions, also, manage risk effectively.
However, these benefits aggregate towards itself to form the aspiration of “Digital Agriculture” – wherein healthy data-driven information is available to farmers for these optimum considerations.
Problems associated with Implementing AI in Agriculture
Despite this promise, unfortunately, there are certain bottlenecks on its ability’s application in mass Agriculture sectors in India as AI is new:
- Limited Digital Literacy: Limited digital knowledge on AI, especially among the rural farmers.
- High costs: Using of AI tools, sensor, drone etc are pricey and not seen economical for small and marginal farmers
- Data Availability – In certain regions, media has data that are limited, incomplete, and not as reliable in data about the agricultural industry.
- Cultural Resistance: Resistance to change in traditional means of farming practices and mistrust to new technology has been slow.
Conclusion
AI in agriculture India is not only a technological breakthrough, but a revolution holding the forward keys for food security, sustainability and prosperity of millions of Indian farmers. As the sector continues to evolve, though, collaboration among players including technology providers, policy makers and the agricultural community will need to play a prominent role in ensuring a compulsory and inclusive adoption.
By adopting AI, India can set an example for other countries in the world, and also take a step for providing India with smarter, sustainable and resilient agriculture.
