Agriculture has been one of the most fundamental practices that sustains human life, and the advancements in technology have made it possible to optimize the processes involved in Agriculture and allied activities. India’s backbone is still its farmers. Artificial Intelligence (AI) holds the key to revolutionising the lives of farmers in India and has been one of the latest technologies that have gained significant recognition in the agricultural sector.
There’s no doubt that crop yields, quality, and labour practices are more efficient now than they were 50 years ago. However, there’s still a major need and scope for improvements. The global human population is exploding, with an estimated 9.9 billion of us on the planet by 2050 and with food demand projected to leap by 35%—56% in that time. And that’s not to mention climatic changes that make resources like water and farmable land scarcer. From leveraging computer vision technology for crop and soil monitoring to disease detection and predictive analytics, the agriculture industry is entering a whole new phase of evolution—thanks to AI.
Not only is there potential, but also rapidly growing interest and investment:
- Forbes reports that global spending on “smart” agriculture, including AI and machine learning, is projected to triple to $15.3 billion by 2025.
- Research suggests that the market size of AI in agriculture should expect a compound annual growth rate (CAGR) of 20%, reaching $2.5 billion by 2026.
Finance Minister Sitharaman, in her speech during budget 2023-24, also mentioned building a “digital public infrastructure” for agriculture to enable “inclusive, farmer-centric solutions through relevant information services for crop planning and health, improved access to farm inputs, credit and insurance, help for crop estimation, market intelligence, and support for growth of Agri-tech industry and start-ups”. In fact, the overall allocation for the sector saw an increase of 4.7 per cent – from Rs 11,02,54.53 crore in the Revised Estimates (RE) of financial year (FY) 2022-23 to Rs 11,55,31.79 crore in 2023-24.
Advances in computer vision, mechatronics, artificial intelligence, and machine learning are enabling the development and deployment of remote sensing technologies to identify and manage plants, weeds, pests and diseases. This also provides a unique opportunity to develop intelligent seeding methods for precise fertilization. Artificial intelligence solutions can enable farmers not only to reduce wastage, but also improve quality and ensure faster market access for the produce.
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% higher average yield per hectare last year.
AI in agriculture involves the use of machine learning, computer vision, and robotics to optimize various aspects of farming. The technology helps farmers to analyse data, make informed decisions, and automate tasks that would be time-consuming and expensive when done manually.
Here we will be discussing some of the use cases of AI, Robotics and other technologies in Agriculture and allied activities.
Robots in farming have been gaining traction in recent years, as they offer the potential to revolutionize the agricultural industry. Robotic systems can be used to perform various tasks, such as planting, harvesting, weeding, and crop monitoring. Some of the way’s robots are being used in farming are:
- Planting and Harvesting: Robots can be used for precision planting and harvesting, which allows farmers to optimize their crop yields and reduce labour costs. For example, robots can be programmed to plant seeds at a specific depth and spacing, which can help ensure optimal growth and reduce waste.
- Automatic Weeding: Robots can be used for automated weeding, which can help reduce the use of herbicides and improve crop yields. Automated weeding robots can use computer vision and machine learning algorithms to identify and target weeds, while avoiding crops.
- Crop and Soil Monitoring: Robots can be used for crop monitoring, which can help farmers identify potential problems early and take corrective action. For example, robots can be equipped with sensors to monitor soil moisture levels, temperature, and nutrient levels.
- Dairy Farming: Robots can also be used in dairy farming to automate tasks such as milking cows and feeding them. Automated milking robots can use sensors to detect when a cow needs to be milked, and then automatically attach and detach milking machines.
- Insect and Plant disease monitoring: Using image recognition technology based on deep learning, we can now automate detection of plant diseases and pests. This works using image classification, detection, and image segmentation methods to build models that can “keep an eye” on plant health.
- Ariel survey and imaging: AI can analyse imagery from drones and satellites to help farmers monitor crops and herds. That way they can be notified immediately if something looks amiss without having to constantly observe the fields themselves. Aerial imaging is also useful for boosting the precision and efficiency of pesticide spraying. As mentioned previously, ensuring that pesticides only go where they’re intended saves money as well as the surrounding environment.
- Management of livestock: Farmers can use sensors to track the health and behaviour of their animals, and machine learning algorithms can analyse the data to detect patterns and identify potential health problems. This helps farmers to prevent the spread of disease, reduce the use of antibiotics, and improve the overall health of their animals.
One of the advantages of using robots in farming is that they can work 24X7, without the need for breaks or rest. This can help farmers improve their efficiency and reduce labour costs. However, the initial investment required to purchase and implement robotic systems can be significant, and there may be ongoing maintenance costs.
Robots are playing an increasingly important role in farming, and they have the potential to revolutionize the industry. Robotic systems can help farmers optimize their crop yields, reduce labour costs, and improve the overall efficiency of their operations. As the technology continues to evolve, we can expect to see even more innovative applications of robotics in agriculture.
One of the significant applications of AI in agriculture is precision farming. Precision farming involves the use of sensors, drones, and other technologies to collect data on soil conditions, weather patterns, and crop growth. The data is then analysed using machine learning algorithms to provide farmers with real-time information on crop health, irrigation, and fertilization. This helps farmers to optimize crop production, reduce costs, and improve yields.
AI is also being used in crop monitoring, disease detection, and pest management. Computer vision algorithms can analyse images of crops and identify signs of disease or pest infestation. This helps farmers to identify potential problems early and take corrective action before the situation worsens.
Supply chain optimization in agriculture involves using data-driven approaches and technologies to improve the efficiency of the various processes involved in the production and distribution of agricultural products. This includes activities such as procurement, transportation, storage, processing, and delivery of agricultural goods.
Some potential benefits of supply chain optimization in agriculture include:
- Reduced waste: By optimizing the supply chain, farmers and other stakeholders can minimize losses due to spoilage, damage, or other forms of waste. This can lead to increased profitability and reduced environmental impact.
- Improved traceability: With an optimized supply chain, it is easier to track the movement of agricultural products from farm to fork. This can help to ensure food safety, enhance transparency, and meet regulatory requirements.
- Increased efficiency: By reducing bottlenecks, optimizing inventory management, and streamlining logistics, supply chain optimization can improve overall efficiency and reduce costs.
- Better decision-making: By leveraging data and analytics, farmers and other stakeholders can make more informed decisions about when and where to sell their products, how to allocate resources, and how to respond to changing market conditions.
Some examples of supply chain optimization technologies in agriculture include:
- Internet of Things (IoT) sensors and devices that can monitor temperature, humidity, and other environmental factors throughout the supply chain.
- Blockchain-based systems that can provide a secure and transparent record of the movement of agricultural products.
- Artificial intelligence (AI) algorithms that can analyse data from various sources to optimize logistics, reduce waste, and improve decision-making.
Overall, supply chain optimization is becoming increasingly important in agriculture as stakeholders seek to improve efficiency, reduce waste, and meet the growing demand for sustainable and traceable food products.
In conclusion, AI is transforming agriculture by helping farmers to optimize crop production, reduce costs, and improve yields. The technology is also helping to improve the overall health of crops and animals and reduce the environmental impact of farming. As AI continues to evolve, it is expected to play an increasingly important role in the agricultural sector.
