Developments in crop insect pest detection techniques

Authors: Mankin, Richard

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 7/26/2022
Publication Date: 4/26/2023
Citation: Mankin, R.W. 2023. Developments in crop insect pest detection techniques. Book Chapter. 117-146. https://doi.org/10.19103/AS.2022.0113.03.
DOI: https://doi.org/10.19103/AS.2022.0113.03

Interpretive Summary: The effectiveness of integrated pest management efforts is strongly affected by the accuracy and timeliness of insect pest detection. Researchers at USDA-ARS Center for Medical, Agricultural, and Veterinary Entomology, Gainesville, Florida, have developed technology that improves capabilities to sense insect presence and the timeliness, accuracy and interpretability of the sensor-collected data. Modern sensors cover broad ranges of the electromagnetic spectrum, and also detect, sound, vibration,and the presence of volatile organic chemicals, augmenting the farmers' traditional use of vision, audition, and olfaction to help detect and control pests. The sensors transmit their information electronically to computers that help identify which insects have been detected. This information about the types and locations of pests helps reduce pest populations inexpensively and reduce crop losses.

Technical Abstract: Early detection of crop insect pests has become an important component of integrated pest management efforts, and researchers are devoting increased attention to technology that improves capabilities to sense insects and maximizes the timeliness, accuracy, and interpretability of the sensor-collected data. Traditional senses of vision, audition, olfaction, and taste are now augmented with technology that senses broad ranges of the electromagnetic spectrum, as well as sound, vibration, and volatile organic chemicals. Such technologies obtain information from pheromone and other traps quickly by use of sensors that monitor wing-beat signals of incoming pests and transmit signals to data processing sites that use machine learning algorithms to interpret the signals in near real-time and determine separate counts of each species collected. These technologies become increasingly important to the success of integrated pest management as their costs decrease while labor and agricultural input costs increase.