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

Abstract

The codling moth (Cydia pomonella) is probably the most harmful pest in apple and pear orchards. The crop loss due to the high harmfulness of the insect can be extremely expensive; therefore, sophisticated pest management is necessary to protect the crop. The conventional monitoring approach for insect swarming has been based on traps that are periodically checked by human operators. However, this workflow can be automatized. To achieve this goal, a dedicated image capture device and an accurate insect counter algorithm are necessary which make online insect swarm prediction possible. From the hardware side, more camera-equipped embedded systems have been designed to remotely capture and upload pest trap images. From the software side, with the aid of machine vision and machine learning methods, traditional (manual) identification and counting can be solved by algorithm. With the appropriate combination of the hardware and software components, spraying can be accurately scheduled, and the crop-defending cost will be significantly reduced. Although automatic traps have been developed for more pest species and there are a large number of papers which investigate insect detection, a limited number of articles focus on the C. pomonella. The aim of this paper is to review the state of the art of C. pomonella monitoring with camera-equipped traps. The paper presents the advantages and disadvantages of automated traps’ hardware and software components and examines their practical applicability.

Increasing catches of adult moth pests (Lepidoptera: Tortricidae) in pome fruit with low‐intensity LED lights added to sex pheromone/kairomone lure‐baited traps

“…The unintended catch of nontargets in light traps has been a major concern impacting the sorting and counting of the targeted species (Kim et al, 2019;Madsen & Sanborn, 1962). Ecological impacts from the broad-spectrum effect of lights on human health as well as insect (Frewin et al, 2022;Preti, Favaro, et al, 2020;Suto, 2022).…”

“…Further studies should compare the effectiveness of lure baited traps with LED lights to monitor more than one tortricid pest in orchards, and consider different wavelength emission ranges. Adoption of “smart traps” with remote visual identification software could benefit from simultaneous accurate counts of multiple pest populations (Frewin et al, 2022; Preti, Favaro, et al, 2020; Suto, 2022).…”

Image-Based Insect Counting Embedded in E-traps that Learn Without Manual Image Annotation and Self-Dispose Captured Insects

“…For these reasons, in recent years we have witnessed a significant advancement in the field of automated vision-based insect traps (a.k.a e-traps see [11][12][13] for thorough reviews). In [14][15][16][17][18] the authors use cameras attached to various platforms for biodiversity assessment in the field, while in this study we are particularly interested in agricultural moth pests [19][20][21][22][23][24].…”

EfficientDet-4 Deep Neural Network-Based Remote Monitoring of Codling Moth Population for Early Damage Detection in Apple Orchard

“…Therefore, DNN-based detection methods have become increasingly important for automatic pest monitoring in agriculture [47][48][49]. Due of the diverse positions, sizes, and textures of the insects found on the adhesive pads, however, it is difficult to develop these solutions [50]. Suto [50] emphasised that there is no universally acceptable automatic method for monitoring codling moth.…”

“…Due of the diverse positions, sizes, and textures of the insects found on the adhesive pads, however, it is difficult to develop these solutions [50]. Suto [50] emphasised that there is no universally acceptable automatic method for monitoring codling moth. Considering that codling moth control is mostly based on chemical measures, it is very important to apply monitoring methods as accurately as possible to limit the use of chemical insecticides to targeted, necessary, and effective applications.…”