AI in Agriculture: Transforming Agriculture and Food Production

In the 19thcentury in the times of industrial revolution machines were deployed as a substitution or reduction for human labour. This in course of time, with the advancements and in information technology in the 20th century, post the arrival of the computers, initiated the vision for artificial intelligence (AI) powered machines. In the preset day it’s a reality that AI is tardily taking over the human labour. Agriculture has been the backbone of human civilization for thousands of years, providing sustenance and livelihoods to billions of people around the globe. With the world's population projected to reach 9.7 billion by 2050, the demand for food is expected to increase significantly. At the same time, the agriculture sector faces various challenges such as climate change, diminishing resources, and labor shortages. To meet the growing demand and tackle these challenges, Digital transformation in future smart agriculture and forestry requires a human-centered AI approach that incorporates sociological, ethical, and legal issues. Natural intelligence should be augmented—not replaced—by artificial intelligence, like “power steering for the brain”. This is where the human-in-the-loop approach comes in, because this approach incorporates the human experience, prior knowledge, and conceptual understanding of human experts to augment, enhance, and strengthen human capabilities with AI—rather than replacing humans.
Following are some of the ways in which use of AI can help in us in development of agriculture.

How AI Can Help in Precision Farming

Precision farming, also known as smart farming or precision agriculture, involves helping AI, sensors, and other technologies to optimize various aspects of farming. By collecting real-time data, farmers can make informed decisions to enhance crop yields, reduce waste, and minimize environmental impacts. Here we'll see how to use artificial intelligence;

1. Soil and Crop Monitoring: AI-powered sensors and drones enable farmers to assess soil conditions, moisture levels, and nutrient content. These insights help farmers apply precise amounts of water, fertilizers, and pesticides, reducing resource wastage and improving crop health. By understanding the variability in soil conditions across their fields, farmers can make informed decisions about the appropriate amount and type of fertilizers to apply, leading to improved crop yields and reduced environmental impact.
2. Disease Detection in crop: By using AI technology-powered image recognition technology can identify signs of disease, pests, or nutrient deficiencies in crops. Cameras installed on drones or at various points in the fields capture images, which are then analyzed by AI algorithms. Early detection of diseases allows farmers to take swift action, preventing the spread and minimizing crop losses.
3. Automated Machinery: AI-driven robots and autonomous vehicles are revolutionizing labor-intensive tasks like planting, harvesting, and weeding. These machines can work 24/7, offering increased efficiency and reduced operational costs.
4. Predictive Analytics: AI algorithms process vast amounts of historical and real-time data to forecast crop growth patterns, disease outbreaks, and pest infestations. Armed with this information, farmers can take proactive measures to protect their crops and optimize yields.
5. Climate and Weather Forecasting: By using AI historical weather data and real-time information from weather stations to predict future climate patterns can be processed. By understanding weather forecasts and adapting their farming practices accordingly, farmers can optimize planting and harvesting schedules, manage water resources more effectively, and mitigate the risks associated with extreme weather events.
6. Water Management: AI-powered irrigation systems optimize water usage by adjusting the irrigation schedule based on real-time data. This not only conserves water but also improves crop water-use efficiency.
7. Crop Breeding: Traditional crop breeding methods are time-consuming and expensive. AI-driven genomic analysis accelerates the development of climate-resistant and high-yielding crop varieties, contributing to global food security.

Goals for precision farming

1. Profitability: Recognize crops and market strategically as well as prefiguring ROI (Return on Investment) based on cost and gross profit.
2. Efficiency: By putting in precision algorithm, improved, rapid and low cost farming opportunities can be utilized. This lets the overall use of resource efficiently.
3. Sustainability: Better socio-economic and environmental operation assures additive improvements in each season for all the performance indicators. 

Challenges in AI adoption in agriculture 

Although AI presents immense opportunities in agriculture application, there still prevails a deficiency in familiarity with advanced high tech machine learning solutions in farms around the world. Exposing farming to external factors like weather conditions, soil conditions and vulnerability to the attack of pests is high. A crop raising plan scheduled at the start of the season might not seem to be good at the start of harvesting as it gets influenced by external parameters.AI systems too require a lot of data for training machines, to take precise forecasting or predictions. Just in case of a very large area of agricultural land, spatial data could be collected easily while getting temporal data is a challenge. The various crop specific data could be obtained only once in a year when the crop are grown. As the database takes time to mature, it involves a substantial amount of time to construct a robust AI machine learning model. This is a major reason for the utilization of AI in agronomic products like seeds, fertilizer and pesticides than that of on field precision solutions.

The integration of AI in agriculture represents a paradigm shift in the way we produce and consume food. From precision farming and climate resilience to supply chain optimization and disease management, AI technologies are revolutionizing every aspect of the agriculture sector. By harnessing the power of AI, farmers can achieve higher yields, reduce resource wastage, and contribute to global food security. However, it is essential to address potential challenges such as data privacy, access to technology, and ethical considerations surrounding AI adoption. As the agricultural landscape continues to evolve, policymakers, researchers, and stakeholders must collaborate to ensure the responsible and inclusive implementation of AI in agriculture. By striking the right balance between technological advancement and sustainable practices, we can create a future where agriculture thrives, and food production meets the demands of a growing world population.