1. Introduction
The cacao pod borer (CPB),
Conopomorpha cramerella (Snellen) (Lepidoptera: Gracillariidae), is a major insect pest of cacao (
Theobroma cacao L.) in the Philippines and other Southeast Asian countries [
1,
2]. Females lay eggs on the surface of pods. Newly hatched larvae then bore through the pod to feed on the pulp and the placenta surrounding the beans. Feeding is characterized by tunnels and scars on tissues, which result in premature fruit ripening and clumping of beans making them difficult or even impossible to extract [
3]. Economic thresholds have been established based on the percentage of pod infestations that were related to yield loss [
1]. Almost no loss was observed with infestations up to 60%, but losses increase rapidly with higher infestation. Monitoring adult populations using pheromone traps may also provide some basis for setting thresholds; however, data must be based on entire cropping periods. Previous studies [
1,
2] indicated that application of insecticides during the low crop period kept populations of CPB below economic damage levels during the subsequent peak season. On the other hand, spraying during peak crop season had little effect on infestations. This suggests that the best return from pod spraying is likely to come from applications during the early stage of a rising crop, soon after the low-crop period. In the Philippines, the low-crop period is around May–July and peak season starts in August. Based on our previous data on seasonal abundance of CPB in the Philippines [
3], the average male CPB trap catch during the early stages of a rising crop period was around 30, which was the basis of the density thresholds used in this study. At low density (<10 CPB), no control is needed. At medium density (10–30 CPB) and high density (>30 CPB), appropriate control measures need to be employed before the peak population density.
In severe infestations, pods become completely unusable and yield losses can range from 30 to 50% [
4]. Due to the detrimental impact of CPB on cacao production, farmers resort to various management strategies. Its management has relied heavily on the use of synthetic insecticides [
5]; however, concerns of its adverse effects to human health and the environment directed efforts to develop alternative methods as part of an integrated pest management (IPM) system [
6]. An alternative solution is the use of sex pheromones to trap adult male CPBs. The sex pheromone components of CPB were identified in 1986 [
7] and have been successfully used in monitoring the population of CPB in Southeast Asian countries, including the Philippines [
1]. Monitoring, one of the pillars of IPM, ensures a guided decision-making process [
8]. Currently, monitoring by trapping involves the use of delta traps with removable sticky paper traps and sex pheromone-impregnated lures [
9]. After the trapping period, sticky paper traps are collected to manually count the trapped CPBs. This may lead to inaccuracies since manual counting is often error-prone. More errors arise when a large number of insects are collected or when the farm manager lacks the technical expertise to recognize and differentiate CPBs from other insect species. An automated method that can accurately identify and count CPB in a short amount of time is therefore necessary for routine pest monitoring.
There are two digital approaches in monitoring insect pests: automated and semi-automated. A fully automated approach entails the use of wireless sensor nodes, which are equipped with cameras that capture sticky paper trap images [
10]. The sticky paper trap images are analyzed locally or through cloud computing. This allows close to real-time monitoring of insect pest presence. However, its main bottleneck is its cost, which makes it difficult for low to middle income farmers to afford. On the other hand, semi-automated insect pest monitoring systems collect sticky paper trap images using mobile phones in controlled or uncontrolled environments [
11]. The main advantage of this method is reduced equipment cost, which makes it more viable for use of most farmers. But currently, there is still limited work on developing mobile applications for insect pest counting.
Most mobile applications related to insect pest detection perform computations on the cloud, which requires a user to upload an image to a cloud server to receive image analysis results. Unfortunately, this approach necessitates a user’s mobile phone to have an internet connection, which is not always feasible, especially for remote farming areas such as cacao plantations. Ref. [
12] developed a mobile application that can detect insect pests from close up images of insects on leaves. They trained a Faster-RCNN object detection model then processed images in a cloud server with GPU support. Similarly, ref. [
13] detected scale pests by employing multiple object detector models then used a cloud platform for computing. Ref. [
14] tested model-centric, data-centric, and deployment-centric strategies to train object detector models for insect pest detection in viticulture. They presented optimization approaches so that each model will work on different mobile device models. The above-mentioned works reveal that mobile computing is a niche topic and several approaches have been taken to develop a convenient and reliable mobile application for insect pest detection. However, it also shows that more work must be carried out to achieve better performance in terms of speed and accuracy.
This work presents novel approaches for enabling mobile computing on low-end mobile devices. This work has three objectives: (1) Develop an insect pest detection algorithm that can locally run on low-end mobile devices without cloud server support; (2) Propose novel approaches for optimizing an edge-based insect pest detection algorithm; and (3) Design and develop a mobile application that can rapidly count cacao pod borers on sticky paper traps. This work demonstrates the potential of mobile computing in integrated pest management, especially for managing remote farming environments.