Automated Cocoa Pod Borer Detection using an Edge Computing-based Deep Learning Algorithm

Abstract

<b><sc>Abstract.</sc></b> The cocoa pod borer (CPB) (Conopomorpha cramerella) is a very small insect pest native to Asia and Oceania. CPBs cause extensive damage by boring holes into cocoa pod husks and cause premature ripening. Due to its resemblance to other insect pest species, most farm managers fail to recognize it; this makes farm managers unable to avert crop damage. This shows that an automated method for counting CPBs is necessary to allow farm managers to perform integrated pest management (IPM) more effectively. This research proposes a lightweight deep learning algorithm for the on-site counting of CPBs on scanned sticky paper trap images. Sticky paper traps were placed on cocoa plantations to monitor the presence of CPBs. Each sticky paper trap image was obtained using a flatbed scanner to form a dataset called CPB1722. A deep learning model was trained and used to detect the CPBs on sticky paper trap images while bounding box analysis was applied as a lightweight approach to improve overall algorithm performance. The proposed algorithm can detect CPBs on the sticky paper trap images with an F₁-score of 0.89 and a R² of 0.98, relative to the number of manually counted CPBs. The algorithm was tested using an edge device with an average computation time of 24 seconds per image, which was fast enough for the on-site detection of CPBs. The developed algorithm can be used to build a portable imaging device for seamless counting of insects on sticky paper traps.