How AI Can Help Our Farmers
It helps us from managing important national security to simple daily functions like playing a game. AI has now developed into another vital sector – Agriculture.
Artificial intelligence (AI), which until recently only seemed to be the subject of science fiction, is currently being studied by a vast number of businesses throughout the world. A large amount of data is processed by AI, which then analyzes the patterns in the data to perform actions that are similar to those of a human. Scientists have used AI to create chess-playing computers and self-driving cars, but the technology has also been applied to agriculture. If the development of artificial intelligence is invested in, it could lead to more effective farming techniques that can help stop global warming.
Reduced food production has had disastrous effects on underdeveloped nations in particular. The annual loss of 35 trillion consumable food calories caused by climate change affects less developed countries that lack the resources to import food. This results in increased food insecurity. Furthermore, rising sea levels exacerbate the problem.
This is where AI steps into the picture. Using AI, farmers can monitor crop moisture, soil composition, and temperature in farming areas. This enables farmers to enhance yields by learning how to care for their crops and figuring out the best quantity of fertilizer or water to use.
Why adopting AI is such a challenge for farmers
The majority of the procedures and steps involved in agriculture are manual. AI can simplify the most difficult and common activities by enhancing already-adopted technology. When used in conjunction with other technology, it can collect and analyze large amounts of data on a digital platform, determining or even executing the best course of action.
Farmers frequently believe that AI exclusively pertains to the digital realm. They fail to see how it can facilitate activities like clearing an area for farming. This is not a result of their conservatism or fear of the uncharted. Their reluctance results from a lack of knowledge about how AI tools are applied in real-world situations.
Because AgriTech companies fail to adequately explain why their solutions are valuable and how precisely they should be implemented, new technologies can appear complex and excessively expensive. Despite the potential benefits of AI, there is still much that needs to be done by technology companies to assist farmers in properly using it.
Let's look at some of the uses and advantages of AI in a farming context.
IoT-powered data analytics
Humans are unable to process the amount of data generated by technologies like farm equipment, drone footage, and crop analytics. AI is being used by farmers and those working in agricultural technology to analyze data points, increasing the value that can be gained from various data sources.
With the use of agricultural AI, farmers can examine data acquired from their farms on weather, temperature, water use, and soil conditions to make educated decisions about their businesses, such as choosing the most practical crops to grow that year or which hybrid seeds reduce waste. Big data analysis also pinpoints the precise soil, light, food, and water requirements needed for propagation and determines optimum irrigation, and aids in lowering greenhouse gas emissions.
Deep learning and computer vision algorithms also process data from drones and unmanned aircraft systems in addition to data from the ground. When AI, drones, and unmanned aircraft systems are used together, it is possible to acquire images of the entire farm and analyze them almost instantly. This allows for the monitoring and analysis of crop health and soil health across the entire farm as well as the identification of problem regions.
Precision farming and predictive analytics
Precision agriculture (PA) is a management approach that makes use of AI technologies to enhance harvest quality and accuracy. PA uses AI technology to assist in the identification of pests, plant diseases, and inadequate plant nutrition on farms. Artificial intelligence (AI) sensors may identify and target weeds while selecting the appropriate herbicides to use within the appropriate buffer, preventing herbicide overuse and herbicide resistance.
Through the development of probabilistic models for seasonal forecasting, farmers are adopting PA to increase agricultural precision. The most suited crop kinds for the season, best planting timings, and ideal planting locations can all be predicted by these models, which can look at the yield months in advance and use data gathered to do so. Agricultural AI technology can then optimize farm management by using forecasted weather patterns for the coming season as a basis for decisions.
Risk management, another agricultural AI technology, is built on precision forecasting. While machine learning and AI on their own are fantastic tools for decreasing errors in business processes, farmers are utilizing forecasting and predictive analytics to lower the risk of crop failures.
A farmer must assume significant financial risks in order to produce a viable crop in large enough quantities to satisfy supply chain demands. Numerous more factors may be managed on farms, even though climate and weather can only be somewhat forecast. By utilizing IoT data to fill prediction algorithms that guide decisions for the upcoming harvest, it is possible to manage plant population, plant strain, irrigation, soil preparation, and pest management.
Given that the majority of cutting-edge technologies are only utilized on sizable, well-connected farms, the future of AI in agriculture will need to place a strong emphasis on universal access. The future of machine learning, automated agricultural goods and data science in farming will be secured by expanding connectivity and outreach to even tiny farms in distant regions throughout the world.