Context: Sticky trap catches of agricultural pests can be employed for early hotspot detection, identification, and estimation of pest presence in greenhouses or in the field. However, manual procedures to produce and analyze catch results require substantial time and effort. As a result, much research has gone into creating efficient techniques for remotely monitoring possible infestations. A considerable number of these studies use Artificial Intelligence (AI) to analyze the acquired data and focus on performance metrics for various model architectures. Less emphasis, however, was devoted to the testing of the trained models to investigate how well they would perform under practical, in-field conditions.
Objective: In this study, we showcase an automatic and reliable computational method for monitoring insects in witloof chicory fields, while shifting the focus to the challenges of compiling and using a realistic insect image dataset that contains insects with common taxonomy levels.
Methods: To achieve this, we collected, imaged, and annotated 731 sticky plates - containing 74,616 bounding boxes - to train a YOLOv5 object detection model, concentrating on two pest insects (chicory leaf-miners and wooly aphids) and their two predatory counterparts (ichneumon wasps and grass flies). To better understand the object detection model's actual field performance, it was validated in a practical manner by splitting our image data on the sticky plate level.
Results and conclusions: According to experimental findings, the average mAP score for all dataset classes was 0.76. For both pest species and their corresponding predators, high mAP values of 0.73 and 0.86 were obtained. Additionally, the model accurately forecasted the presence of pests when presented with unseen sticky plate images from the test set.
Significance: The findings of this research clarify the feasibility of AI-powered pest monitoring in the field for real-world applications and provide opportunities for implementing pest monitoring in witloof chicory fields with minimal human intervention.
Keywords: Automatic monitoring; Convolutional neural networks; Insect recognition; Pest management.
© 2023 The Authors.