Utilising AI in agriculture is essential to boosting output on limited land resources. This is due to the expanding global population and rising food demand. Numerous factors, such as crop rotation, rainfall, temperature, moisture content, and soil nutrient level, affect farming. These factors have led to the increase in the usage of artificial intelligence-based technology in the field of agriculture. The agriculture industry has started using artificial intelligence technology to enhance a variety of agriculture-related operations across the whole food supply chain.

In this blog, we will explore the role and impact of AI in agriculture in greater detail.

The Use of AI in Agriculture

In many fields and businesses, artificial intelligence is the newest buzzword. For a variety of reasons, it is generating a lot of enthusiasm. Here, we are examining its application in the farming and agricultural sectors.

What is Artificial Intelligence?

Artificial intelligence is abbreviated as “AI”. Artificial intelligence is the science of creating machines that can think like humans. It is capable of actions deemed “smart”. AI technology is capable of analysing large amounts of data in a variety of ways, unlike humans. AI seeks to mimic human decision-making, pattern recognition, and situational analysis. 

Benefits of Using AI in Agriculture

Below are the benefits of using AI in agriculture:

• Data-Driven Choices: Data is everything in the contemporary world. Data is used by organisations in the agricultural industry to get detailed insights into every aspect of farming, from comprehending each acre of a field to tracking the whole supply chain for food to acquiring profound insights into the process of yield development. Predictive analytics driven by AI is already opening doors for agribusinesses. By using AI in agriculture, farmers can collect and process more data faster. AI is also capable of forecasting pricing, analysing market demand, and identifying the best periods to plant and harvest.
• Savings on Expenses: Farmers are always looking to increase agricultural productivity. Precision farming, when paired with AI, may help farmers produce more crops using fewer resources. AI in agriculture maximises yields while reducing costs by combining the latest data management techniques, variable rate technologies, and soil management techniques. 

Recent Technology in Agriculture Using AI

Here is the array of recent technology in agriculture using AI:

• Smart irrigation systems
• AI-enabled soil sensors
• Crop and livestock management apps
• Satellite-based crop monitoring

How is AI Used in Agriculture?

From predicting weather patterns to increasing agricultural yields and simplifying resource management, artificial intelligence has a big impact on agriculture. Consider gadgets that can monitor crop health, evaluate soil conditions, monitor dairy cows to make sure they produce milk, and even harvest goods by themselves. AI in agriculture enables farmers to make data-driven decisions that increase efficiency and sustainability. In light of growing costs, AI may help agricultural decision-makers increase productivity while decreasing waste if applied properly.

Also Read: Guide On KRMU’s B.Sc. Agriculture Admission 2025

How Does AI Help in Agriculture?

AI has a wide range of possible applications in agriculture. Discussed below are a handful of them:

Analysis of Crops, Soil, and Fields

Without a doubt, the future of global agriculture depends on the health of the soil. By analysing sensor and image data, artificial intelligence (AI) can be used to evaluate the health of soil. This involves evaluating the moisture and nutritional value as well as identifying the areas that require modification. By ensuring that each region of a field receives precisely what it needs for maximum development, such thorough soil analysis helps to customise the fertiliser and irrigation requirements for various portions of the field, saving waste and increasing crop yields overall.

AI in agriculture reduces the need for blanket treatments by enabling the exact administration of pesticides and fertilisers through in-depth analysis of crop and soil data. By avoiding over-application, which may be detrimental to plants and soil health, this focused strategy not only reduces expenses and environmental effects but also encourages healthier crop development.

AI-enabled drones and satellites can take high-resolution pictures of the crops, which AI systems may then analyse to track plant growth and health and identify any indications of illness, stress, or even insect infestation. Early detection reduces the possibility of crop losses by enabling farmers to handle problems quickly. Additionally, this technology can accurately forecast production and track plant development, which helps with logistics and harvest preparation.

Monitoring and Management of Livestock and Dairy Health

Artificial intelligence is becoming more and more important in changing livestock management by improving the general productivity, welfare, and health of animals. AI integration in livestock health management includes a number of cutting-edge tools and methods intended to track, identify, and forecast animal health problems.

Applications of artificial intelligence in cattle health include monitoring temperature, heart rate, and activity levels using sensors on the animals. AI can identify any anomalies that could worry livestock managers by studying and monitoring this data, which enables any issues to be addressed quickly before they get worse.

By incorporating artificial intelligence into the milking process, the dairy industry can both maximise milk production efficiency and identify any cow problems that could be affecting milk output.

Unmanned Agriculture Machinery

Often called “smart farming”, artificial intelligence can be used to operate unmanned agricultural machinery, providing solutions that can significantly increase productivity, lower labour costs, and improve overall farming precision—all of which translate into higher long-term profitability for the farms themselves. Robotic harvesters and self-driving tractors are two examples of agricultural equipment that use artificial intelligence.

AI machinery is really capable of a broad range of duties on farms, including properly navigating fields using GPS and AI, monitoring and analysing crops to alert users to any irregularities so they can take quick action and avoid any interruptions, and much more.

AI may also be utilised in machinery to disperse seeds and fertiliser. It will minimise waste and guarantee that the farm is running as effectively as possible by employing accurate data, mapping, and spreading methods.

AI is even capable of making highly accurate yield predictions. This implies that farmers may utilise this information to make future plans and decisions.

Finding Irrigation System Faults or Leaks

AI is essential for identifying irrigation system leaks. Algorithms can find trends and irregularities in data that point to possible breaches. It is possible to train machine learning (ML) models to identify certain leak indicators, including variations in water pressure or flow. Early identification made possible by real-time monitoring and analysis helps to avoid water waste and possible crop damage.

In order to pinpoint regions with high water use, AI also takes meteorological information and crop water requirements into account. AI improves water efficiency and helps farms save resources by automating leak detection and sending out alarms.

Identifying Plant Illnesses and Pests

Computer vision can identify pests or illnesses in addition to crop growth and soil quality. In agriculture projects, AI is used to scan photos for insects, mould, rot, and other crop health hazards. This, when combined with alarm systems, enables farmers to take prompt action to eradicate pests or isolate crops to stop the spread of disease.

Apple black rot may be detected with over 90% accuracy using AI technologies in agriculture. With the same level of precision, it can also recognise insects such as flies, bees, moths, etc. To get the required amount of the training data set to train the algorithm with, researchers had to first gather pictures of these insects.

Applying Pesticides Intelligently

Farmers are already well aware that there is an opportunity to optimise the use of pesticides. Unfortunately, there are significant drawbacks to both automated and manual application procedures. Although it may be labour-intensive and slow, manually applying pesticides allows for more precision in addressing certain regions. Although automated pesticide spraying is faster and requires less work, it frequently lacks precision, which can contaminate the environment.

Drones with AI capabilities combine the finest features of each strategy without sacrificing any of their disadvantages. The amount of insecticide that should be sprayed on each area is determined by drones using computer vision. Even while this technology is still in its infancy, it is getting increasingly accurate.

Predictive Analytics and Yield Mapping

Yield mapping analyses massive datasets in real time using machine learning methods and is one of the known features of AI in agriculture. This facilitates improved planning by assisting farmers in comprehending the trends and traits of their crops. By integrating methods such as 3D mapping, sensor data, and drone data, farmers are able to forecast soil yields for certain crops. Multiple drone flights are used to gather data, which allows for more accurate analysis using algorithms.

Farmers’ Chatbots

Chatbots can serve as a conduit between farmers and their distributors or consumers. These conversational agents may be used by farmers to purchase supplies, check inventory levels, and get answers to queries about the goods or services they supply.

Managing databases of data on crops and soil conditions is another area where chatbots might be helpful. They carry out agricultural activities in a manner similar to those of virtual farm assistants. Based on data, they provide farmers tailored guidance and suggestions. In order to comprehend farmers’ enquiries and offer real-time insights on weather, market pricing, and other agricultural data, the platform makes use of machine learning algorithms and natural language processing. 

Also Read: Top B.Sc. Agriculture Colleges in Delhi NCR – Apply Now!

Artificial Intelligence in Agriculture in India

Agriculture today needs technology, especially artificial intelligence, to solve challenges like climate change, low productivity, soil damage, and unpredictable weather. Universities like K.R. Mangalam University (KRMU) are creating future-ready professionals by combining modern technology with agricultural and data-driven learning.

Government & Startup Support

India is promoting “Smart Agriculture” through:

• Digital India initiatives
• AI startups in agritech
• Drone-as-a-Service (DaaS) model
• Smart farming subsidies

The Future of AI in Indian Farming

As more farmers adopt technology, AI will:

• Boost food production
• Reduce wastage
• Improve farmer income
• Strengthen India’s agricultural economy

How Do Universities Prepare Students for AI in Agriculture?

K.R. Mangalam University is creating future-ready professionals by combining modern technology with agricultural and data-driven learning.

Industry-Focused Curriculum

KRMU designs its courses to match real market needs.

• Subjects such as data science, machine learning, IoT, robotics, and big data analytics.
• Students learn how AI is applied directly to farming—crop prediction, soil testing, weather analysis, drone usage, etc.

Students don’t just study theory—they practise real solutions used in modern agriculture.

Hands-On Research & Labs

• KRMU has advanced labs for AI, IoT, GIS, and smart systems.
• Here, practical experimentation with sensors, drones, automation tools, and software is conducted.
• Here, students can work on projects including crop disease recognition and soil health prediction AI models.

This helps them build real skills needed in agritech companies.

Collaboration With Industry Experts

KRMU invites experts from:

• Agri-tech startups
• AI companies
• Research centres
• IoT and robotics organisations

Students get training from professionals working in real agricultural innovation.

Internships & Live Projects

Students get internship opportunities in:

• Smart farming organisations
• Food technology companies
• Drone & agri-AI startups
• Government agriculture programmes

This gives them real workplace exposure and strengthens employability.

Skill Development in Emerging Technologies

Here, students learn:

• AI tools & algorithms
• Data analysis for crop prediction
• Remote sensing & satellite data interpretation
• Smart irrigation & automation systems

These are the same technologies being adopted in farms across India.

Placement Support in Agritech & Tech Companies

KRMU has strong placement support that helps students to connect: 

• AI start-ups
• Food processing companies
• Agricultural technology firms
• IT companies with agri-analytics roles

Careers students can pursue:

• AI Analyst
• Agriculture Data Scientist
• Drone Technology Specialist
• IoT Project Engineer
• Precision Agriculture Consultant

Encouraging Innovation & Entrepreneurship

Students with startup ideas receive:

• Guidance from the incubation cell
• Mentorship from industry leaders
• Support for launching agri-tech solutions

KRMU promotes young innovators who want to solve farming challenges with AI technology.

Conclusion

AI is redefining agriculture by turning a conventional process into a smart, data-driven process. With AI’s growing impact, the future of AI in agriculture in India looks great. K.R. Mangalam University empowers students not just to study AI but to apply it in real agriculture. This means helping Indian farmers work smarter, save resources, and increase productivity. With industry partnerships, practical labs, internships, and a modern curriculum, KRMU is preparing the next generation of tech-driven agricultural professionals. These professionals can contribute to AI in agriculture in India.

Also Read: AI Project Ideas for College Students 2025