Artificial intelligence-based systems can identify and detect pests by analysing photos or sensor data gathered from fields. Using photos or videos taken by drones, cameras, or sensors, computer vision algorithms can identify pest species, damage signs, or pest activity patterns. This makes it possible to identify pests early and supports farmers in acting quickly to stop the spread of such pests.
In order to forecast and predict pest outbreaks, AI systems can process data from a variety of sources, including weather patterns, crop phenology, and insect population dynamics. AI algorithms can produce precise pest risk assessments by examining historical data and real-time inputs, assisting farmers in foreseeing pest outbreaks and organising preventative measures. Through focused therapies made possible by this proactive strategy, there is less need for
Artificial intelligence-powered decision support systems give farmers access to real-time information and pest management advice. These systems combine information from several sources, such as historical records, crop status, pest monitoring data, and weather forecasts. AI algorithms can use this data to analyse pest management procedures that are most effective, such as when and how much to apply pesticides, cultural norms, or biological control techniques. This helps farmers make wise judgements, use less pesticides, and have a smaller negative environmental impact.
