AI used in agriculture optimizes farming practice by decreasing workloads, analyzing farming data and improving the accuracy of seasonal forecasting.
Agriculture has constantly evolved as new technologies have been developed, from mechanization to biotechnology. Following current trends, the agricultural industry is turning to AI technologies which have been used to help produce healthier crops, reduce farming workloads and analyze data.
Market research predicts that the number of data points that are gathered on an average farm will rise from an average of 190,000 today to 4.1 million by 2050. The sheer volume of data that will be collected (from farm machinery, drone images and crop analysis) will be impossible for humans to process without help. Farmers and agricultural technology workers will use AI to help analyze data points and identify important patterns and trends automatically.
AI techniques can help farmers can analyze weather conditions, temperature, water usage and soil conditions on their farm and then make informed business decisions such as choosing the most profitable crops for the current market or which hybrid seeds result in the least waste. Big data can also be used to optimize irrigation, reduce greenhouse gas emissions and specify the exact conditions needed for effective seed propagation.
AI systems can also be used to improve harvest quality and accuracy by using a technique known as precision AI used in agriculture. PA uses AI to detect diseases in plants, identify pests and show areas of poor plant nutrition on specific areas on farms. AI sensors can also detect and target weeds by deciding which herbicides to apply and in what concentration thus helping prevent the overuse of herbicides and reducing herbicide resistance in crops.
Farmers are also using PA to improve crop planning by creating probabilistic models for seasonal forecasts. These models look months ahead and use historical and current weather data to provide farmers with predictions for the best crop varieties to plant for the coming season as well as identifying the most favourable planting times and locations.
This type of AI forecasting is also informs another farming practice: risk management. Farmers are using AI forecasting techniques and predictive analytics to reduce the risk of crop failures. Because growing a crop in mass quantities requires a farmer to take financial risks, they must rely on agricultural yield estimates to ensure they will operate profitably.
Weather can only be predicted to a fixed extent but there are many other variables that can be controlled such as plant population, strain, amount of irrigation and pest control to name just a few. Data on these can be used by predictive AI to drive decisions on farms to ensure the risks of farming decisions fall within acceptable parameters.
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