Artificial Intelligence (AI) technologies are increasingly being used in agriculture to produce healthier crops, for soil monitoring, to control pests, analyze growing conditions and provide data for farmers to help with their practices and workload.
Following are some examples AI used in agriculture.
• Weather Forecasting
With changing climatic condition and increases in pollution farmers can find it difficult to decide the right time to plant their crops. By using AI, farmers can use sophisticated data mining techniques to forecast weather conditions and decide which types of crop are best to grow and the optimal time for planting them.
• Soil and Crop Monitoring Systems
The type of soil in which a crop is planted and the nutrition the soil requires is an important factor in the crop’s yield and quality. Because of deforestation, soil has become degraded making it difficult to determine its quality.
Researchers have developed an AI-based applications that can identify any nutrient deficiencies in soil. They can also check for plant pests and the possibility of diseases which helps farmers decide which fertilizers and chemicals to use to improve harvest quality. These applications use image recognition-based technology that analyze images captured by the farmer using a smartphone.
• Precision Farming Through Predictive Analytics
AI used in agriculture have been developed that help farmers implement accurate and controlled farming techniques by providing information about water management, crop rotation, the best type of crop to be grown, optimum planting periods, nutrition management and the best time to harvest.
Using machine learning algorithms, that are applied to images captured by satellites and drones, these AI-enabled technologies can predict weather conditions, assess crop suitability and sustainability, and check farms for the presence of diseases or pests. They also provide information on areas of the farm that have poor nutrition by analysing data such as temperature, rainfall, wind speed and the amount of sunlight
• Agricultural Robots
Robots are being developed that can perform multiple farming tasks that were previously too difficult for humans to perform. Robots can, for example, be trained to control weeds and harvest crops at a faster rate than a human being could.
Robots are being used to check the quality of crops and detect and remove weeds while picking and packing crops simultaneously; they can potentially meet the challenges farmers face in finding suitable workers.
• Insect Control
Pests in crops are one of the most difficult issues that farmers face. AI systems can use satellite images and historical data to detect when an insect is present in a crop and the type of insect it is. An alert is then sent to the farmer so that they take the necessary action to control the infestation.