An Open-Source Low-Cost Imaging System Plug-In for Pheromone Traps Aiding Remote Insect Pest Population Monitoring in Fruit Crops

Abstract

This note describes the development of a plug-in imaging system for pheromone delta traps used in pest population monitoring. The plug-in comprises an RGB imaging sensor integrated with a microcontroller unit and associated hardware for optimized power usage and data capture. The plug-in can be attached to the top of a modified delta trap to realize periodic image capture of the trap liner (17.8 cm × 17.8 cm). As configured, the captured images are stored on a microSD card with ~0.01 cm2 pixel−1 spatial resolution. The plug-in hardware is configured to conserve power, as it enters in sleep mode during idle operation. Twenty traps with plug-in units were constructed and evaluated in the 2020 field season for codling moth (Cydia pomonella) population monitoring in a research study. The units reliably captured images at daily interval over the course of two weeks with a 350 mAh DC power source. The captured images provided the temporal population dynamics of codling moths, which would otherwise be achieved through daily manual trap monitoring. The system’s build cost is about $33 per unit, and it has potential for scaling to commercial applications through Internet of Things-enabled technologies integration.

Codling Moth Monitoring with Camera-Equipped Automated Traps: A Review

“…However, many growers are not satisfied by the cost and scalability of commercial traps. Schrader et al [21] mentioned that commercial remote monitoring traps are adopted hesitantly by growers because they are expensive (approximately USD 1375/ha) and are used at a low trap density (1 trap every 4 ha while the recommended would be 1 trap/ha).…”

Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities

“…In recent years, we are witnessing an upsurge of interest in technologically advanced devices as applied to automatic insect detection, counting, and identification [1]. There are mainly three major approaches: (a) optical counters attached to the entrance of traps that target specific pests using lures (pheromones in the case of lepidoptera [2] and palm pests, soil arthropods [3,4] or scents and CO 2 in the case of mosquitoes [5]), (b) camera-based traps that take a picture of their internal space [6][7][8][9][10][11][12][13][14], and (c) near infrared sensors [15] and lidars that emit light covering a volume of space of the open field and registering the backscattered wingbeat signal of flying insects [16][17][18]. All approaches have advantages and disadvantages depending on the application scenario.…”

“…This work adopts the third approach as it is best suited for crawling/walking Arthropoda encountered in urban environments. There is a large corpus of previous approaches on cameras embedded in insect traps (see [6][7][8][9][10][11][12][13][14] and the references therein). Their role is to either report a picture to a server and let a human observer discern the number and the species of the captured insects [14], or proceed into processing the captured image to detect [10,12] and automatically count and/or identify insects [7][8][9][10][11]13,19].…”

“…There is a large corpus of previous approaches on cameras embedded in insect traps (see [6][7][8][9][10][11][12][13][14] and the references therein). Their role is to either report a picture to a server and let a human observer discern the number and the species of the captured insects [14], or proceed into processing the captured image to detect [10,12] and automatically count and/or identify insects [7][8][9][10][11]13,19]. In the former case, the gain is the reduction of cost and manpower required to visit the network of traps and deliver the photos.…”

A Multimodal Sensing Platform for Interdisciplinary Research in Agrarian Environments

“…Quantifying moths in these systems is time-consuming and requires specific identification skills. Proposed solutions primarily involve machine- or computer-vision methods [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. While they have a range of advantages, the main drawbacks for their field deployment are the increased system cost and high bandwidth requirements in locations where high-speed data are generally unavailable.…”