Abstract
The bagworm is a vicious leaf eating insect pest that threatens the oil palm plantations in Malaysia. The economic impact from defoliation of approximately 10% to 13% due to bagworm attack might cause about 33% to 40% yield loss over 2 years. Due to this, monitoring and detecting of bagworm populations in oil palm plantations is required as the preliminary steps to ensure proper planning of control actions in these areas. Hence, the development of an image processing algorithm for detection and counting of Metisa plana Walker, a species of Malaysia’s local bagworm, using image segmentation has been researched and completed. The color and shape features from the segmented images for real time object detection showed an average detection accuracy of 40% and 34%, at 30 cm and 50 cm camera distance, respectively. After some improvements on training dataset and marking detected bagworm with bounding box, a deep learning algorithm with Faster Regional Convolutional Neural Network (Faster R-CNN) algorithm was applied leading to the percentage of the detection accuracy increased up to 100% at a camera distance of 30 cm in close conditions. The proposed solution is also designed to distinguish between the living and dead larvae of the bagworms using motion detection which resulted in approximately 73–100% accuracy at a camera distance of 30 cm in the close conditions. Through false color analysis, distinct differences in the pixel count based on the slope was observed for dead and live pupae at 630 nm and 940 nm, with the slopes recorded at 0.38 and 0.28, respectively. The higher pixel count and slope correlated with the dead pupae while the lower pixel count and slope, represented the living pupae.
Highlights
In Malaysia, the palm oil sector is described as one of the key contributors to the national economy and currently, the palm oil industry has contributed a GNI of RM 79.9 billion in 2017 [1]
The proposed image processing algorithm comprises of four stages which involve image segmentation, shape extraction using morphological operators, object detection using deep learning with Faster R-CNN, image classification to distinguish between the stages of the bagworms, and counting of the bagworms
In the color space processing method, which involved morphological image processing, the results showed that the accuracy of the detection was low; the averages were 40% and 34%, at the 30 cm and 50 cm camera distances, respectively (Table 2)
Summary
In Malaysia, the palm oil sector is described as one of the key contributors to the national economy and currently, the palm oil industry has contributed a GNI of RM 79.9 billion in 2017 [1]. To realize the robot function, image processing algorithms were designed and implemented by Shapiro et al [6] to be used in a spraying guidance system that was based on a proportional controller, evaluating the system dynamics and examining its behaviour under different parameters. The image processing algorithm, through image segmentation, was used by Amatya et al [8] to segment out branch and cherry regions from leaves and background for sweet cherry harvesting They obtained 89.6% branch pixel classification exactness by applying RGB color structures on each pixel together with a Bayesian classifier for separating the branch pixels. Realizing the importance of precise data collection, efforts to develop a ground-based device using a deep learning image processing algorithm targeted to detect bagworms has been developed in this work. Planters can be assisted using an automated bagworm counter device to carry out census prior to control actions activity and can save the pesticides usage due to accurate timing of bagworms control
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