Detection of insect-induced crop damages using satellites, drones, sensors, and machine learning

Insect feeding induces plant responses that can manifest as physiological and biochemical changes, resulting in signatures that can be detectable. These responses include changes in leaf color, texture, moisture content, and chemical composition, often not visible to the naked eye but can be identified through advanced sensing technologies. Modern technologies such as satellites, drones, sensors, and machine learning can automate detecting and measuring these insect-induced signatures on crops. By leveraging high-resolution imagery from airborne sensors and sophisticated machine learning algorithms, it is possible to monitor crop health and identify areas affected by insect pests with high accuracy and efficiency. The detection of such signatures can be accomplished at various spatial scales, depending on the specific goals and objectives of the monitoring efforts. For instance, satellite imagery can provide a broad overview of large agricultural areas, while drones can offer more detailed views of specific fields or plots. Machine learning algorithms play a crucial role in analyzing the vast amounts of data collected from these sources, identifying patterns and anomalies that indicate insect-induced damage. This presentation will demonstrate how these modern technologies can be integrated to create a comprehensive system for the automated detection and measurement of crop damage caused by insect pests. By combining the strengths of satellites, drones, sensors, and machine learning, this approach promises to enhance agricultural management practices, improve pest control strategies, and ultimately increase crop yields and sustainability.