Automatic pest identification system in the greenhouse based on deep learning and machine vision

Abstract

Monitoring and understanding pest population dynamics is essential to greenhouse management for effectively preventing infestations and crop diseases. Image-based pest recognition approaches demonstrate the potential for real-time pest monitoring. However, the pest detection models are challenged by the tiny pest scale and complex image background. Therefore, high-quality image datasets and reliable pest detection models are required. In this study, we developed a trapping system with yellow sticky paper and LED light for automatic pest image collection, and proposed an improved YOLOv5 model with copy-pasting data augmentation for pest recognition. We evaluated the system in cherry tomato and strawberry greenhouses during 40 days of continuous monitoring. Six diverse pests, including tobacco whiteflies, leaf miners, aphids, fruit flies, thrips, and houseflies, are observed in the experiment. The results indicated that the proposed improved YOLOv5 model obtained an average recognition accuracy of 96% and demonstrated superiority in identification of nearby pests over the original YOLOv5 model. Furthermore, the two greenhouses show different pest numbers and populations dynamics, where the number of pests in the cherry tomato greenhouse was approximately 1.7 times that in the strawberry greenhouse. The developed time-series pest-monitoring system could provide insights for pest control and further applied to other greenhouses.

IoT-based system of prevention and control for crop diseases and insect pests

“…First, accurate and rapid classification of diseases is an important basis for early disease monitoring, diagnostics, and prevention (Abdullah et al, 2023;Liu and Wang, 2023). In recent years, deep convolutional neural networks have achieved remarkable success in disease image classification (Ning et al, 2023;Zhang et al, 2023). In our research team's previous work, several image classification methods have been proposed (Wang et al, 2022b;Wei et al, 2022).…”