Artificial intelligence in agriculture is divided into three categories: robotics, soil and crop management, and livestock farming. Its goal is to make farming simpler, more precise, lucrative, and fruitful for farmers. The global AI in the agriculture market was worth US$ 240 million in 2017 and is predicted to grow to US$ 1.1 billion by 2025. In addition, challenges like population expansion, climate change, and food security necessitate novel approaches to increasing crop output. As a result, comprehending AI’s application in agriculture becomes essential. By 2050, the globe will have to grow 50% more food. To accommodate this goal, unfortunately, only 4% of extra land will be put under production. At a time where the global needs to produce more food with fewer resources, AI has the potential to drive an agricultural revolution. Essential Artificial intelligence in agriculture at various phases of the agricultural process can pay off for farmers in terms of increased profitability and revenue.
AI in Indian agriculture has the potential to improve farm production, alleviate supply chain restrictions, and expand market access. It has the potential to benefit the entire agriculture value chain. By 2026, AI in global agriculture is expected to be a $4 billion opportunity.
Farmers tend to think of AI as something that only applies in the digital world. They may be unable to see how it can assist them in working the real land. This isn’t because they’re fearful of the unknown or conservative. Their resistance stems from a lack of awareness of how AI tools can be applied in the real world.
For the time being, technology providers must consider a few things: how to enhance their tools, how to assist farmers in addressing their concerns, and how to communicate how machine learning can help solve real-world problems, such as lowering manual labour. AI’s future in agriculture is certain to be beneficial.
The three most popular applications of AI in agriculture are:
Precision agriculture:using AI to analyze data from sensors, drones, and other sources to optimize crop production, reduce waste, and improve resource efficiency.
Agricultural robotics:using AI to develop autonomous robots and machines for tasks such as planting, harvesting, and crop monitoring.
Crop and livestock monitoring:using AI to analyze data from satellite imagery, cameras, and sensors to monitor crop growth and health, detect pests and diseases, and monitor the health and well-being of livestock.
The impact of AI on agriculture can be significant, including:
Increased productivity:AI can help farmers optimize their operations, improve decision-making, and increase efficiency, resulting in higher yields and greater profitability.
Improved sustainability:AI can help farmers reduce their environmental impact by improving resource management, reducing waste, and promoting sustainable practices.
Better crop quality:AI can help farmers monitor crops more effectively, detect issues early, and take corrective action, resulting in better crop quality and higher yields.
Improved animal welfare:AI can help farmers monitor animal health and behavior, detect issues early, and take preventive action, resulting in improved animal welfare.
Enhanced food safety:AI can help identify potential risks in the food supply chain, such as contamination or spoilage, and help prevent outbreaks of foodborne illness.
Reduced labor costs:AI can automate repetitive or time-consuming tasks, such as monitoring crops or livestock, reducing the need for manual labor and potentially lowering labor costs.Overall, the use of AI in agriculture has the potential to improve efficiency, productivity, and sustainability, leading to greater food security and economic growth in rural communities.
In India, AI is being used in agriculture in several ways, including:
Crop monitoring:AI-powered drones and satellite imagery are being used to monitor crop health and identify areas that require attention, such as irrigation or pest control.
Weather forecasting:AI is being used to provide farmers with accurate weather forecasts, enabling them to plan their operations and reduce the risk of crop failure.
Pest and disease detection:AI is being used to detect pests and diseases early, allowing farmers to take timely action and minimize crop damage.
Soil analysis:AI-powered sensors are being used to analyze soil health and provide farmers with insights into fertilization and irrigation.
Market analysis:AI is being used to provide farmers with real-time information on market prices, helping them make informed decisions about crop selection and pricing.
Farm automation:AI-powered robots and machines are being used to automate repetitive tasks such as seeding, weeding, and harvesting, reducing the need for manual labor and potentially lowering labor costs.Overall, the use of AI in Indian agriculture has the potential to improve productivity, reduce costs, and promote sustainable farming practices, helping to address the country's food security challenges and supporting rural economic development.
AI is used in agriculture in various ways, such as crop monitoring, weather forecasting, pest and disease detection, soil analysis, market analysis, and farm automation. The use of AI in agriculture benefits several stakeholders, including:
Farmers:AI helps farmers improve productivity, reduce costs, and increase efficiency, resulting in higher yields and greater profitability.
Consumers:AI helps ensure food safety and quality by detecting contamination, spoilage, and other potential risks in the food supply chain.
Agribusinesses:AI helps agribusinesses make data-driven decisions and reduce risk, improving operational efficiency and profitability.
Governments:AI helps governments monitor and regulate agricultural practices, ensuring compliance with environmental and food safety standards.
The environment:AI helps promote sustainable farming practices by reducing resource waste and minimizing environmental impact.Overall, the use of AI in agriculture benefits various stakeholders and contributes to the goal of achieving sustainable, productive, and profitable agriculture.