Abstract
The prevention of the loss of agricultural resources caused by pests is an important issue. Advances are being made in technologies, but current farm management methods and equipment have not yet met the level required for precise pest control, and most rely on manual management by professional workers. Hence, a pest detection system based on deep learning was developed for the automatic pest density measurement. In the proposed system, an image capture device for pheromone traps was developed to solve nonuniform shooting distance and the reflection of the outer vinyl of the trap while capturing the images. Since the black pine bast scale pest is small, pheromone traps are captured as several subimages and they are used for training the deep learning model. Finally, they are integrated by an image stitching algorithm to form an entire trap image. These processes are managed with the developed smartphone application. The deep learning model detects the pests in the image. The experimental results indicate that the model achieves an F1 score of 0.90 and mAP of 94.7% and suggest that a deep learning model based on object detection can be used for quick and automatic detection of pests attracted to pheromone traps.
Highlights
The prevention of the loss of agricultural resources caused by pests is an important issue
A vision-based pest detection m ethod[9] was developed to detect the parasites found on strawberry plants based on SVM (Support Vector Machine) classification
We evaluated the pest detection with IoU threshold of 0.5 for all the models
Summary
The prevention of the loss of agricultural resources caused by pests is an important issue. A pest detection system based on deep learning was developed for the automatic pest density measurement. Since the black pine bast scale pest is small, pheromone traps are captured as several subimages and they are used for training the deep learning model. They are integrated by an image stitching algorithm to form an entire trap image. The experimental results indicate that the model achieves an F1 score of 0.90 and mAP of 94.7% and suggest that a deep learning model based on object detection can be used for quick and automatic detection of pests attracted to pheromone traps. There have been multiple approaches to pest detection such as computer vision and machine learning-based approaches, deep learning-based approaches, and real-time application development using these approaches. Liu et al.[14] developed a real-time computer vision based robot automobile monitoring system using inverse histogram mapping and object contour to recognize Pyralidae
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