Integrated Pest Management (IPM) programmes are designed to reduce agricultural pest pressure through management, biological and chemical approaches in order to reduce the use of synthetic pesticides and enhance biodiversity. A key aspect of pest management is accurate and high-throughput monitoring of insect populations. The development of pheromone baits and insect traps has allowed better estimation of population densities. Timely data on species captured, and number of captures per times, from these traps can assist in making better pest management decisions. However, insect traps are often inspected manually, which involves labour costs and is dependent on personnel availability. This can lead to delayed diagnosis and significant economic loss to farmers.
Schrader et al. (2022) thus developed an open hardware, plug-in imaging system for pheromone delta traps used in pest population monitoring (see image above). In brief, the plug-in consists of an RGB imaging sensor integrated with a microcontroller unit and associated hardware to optimise power consumption and data acquisition. The plug-in can be attached to the top of a modified delta trap or similar to provide periodic image capture of the trap liner. As configured, the captured images are stored on a microSD card. The hardware is configured to save power by entering a sleep mode when idle. The open hardware traps were evaluated on a codling moth (Cydia pomonella) population. The units reliably captured images at daily intervals over a two-week period using a 350 mAh DC power source. The captured images provided the temporal population dynamics of codling moths that would otherwise be achieved by daily manual trap monitoring. The system hardware is very affordable and has the potential to be scaled to commercial applications through the integration of Internet of Things-enabled technologies.
Citation: Schrader, M. J., Smytheman, P., Beers, E. H., & Khot, L. R. (2022). An open-source low-cost imaging system plug-in for pheromone traps aiding remote insect pest population monitoring in fruit crops. Machines, 10(1), 52. https://doi.org/10.3390/machines10010052
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