Smart Pest Monitoring Technology
A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Remote Sensing in Agriculture".
Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 3569
Special Issue Editors
Interests: computer vision; image and signal processing; machine learning; embedded system; pest recognition and detection; precision agriculture
Interests: image analysis; data mining and visualization; decision-making analysis and auxiliary diagnosis in agricultural life sciences
Special Issue Information
Dear Colleagues,
It is well known that insect pests are one of the main causes of crop damage all over the world. The prevention and control of insect pests could reduce the loss of crops in agriculture. The first step to implement this task is the ability to accurately monitor pests, with the aim of discriminating between various species and estimating their population for precision control. Since this task requires continuous and expensive monitoring, there has been a growing interest in automatic insect pest monitoring in recent years.
Traditional insect pest monitoring depends on insect experts or technicians to manually recognize insect pests, which is subjective, labor intensive, and prohibits large-scale, low-cost applications. As embedded devices with cameras and Internet connections become ubiquitous, the rapid development of computer vision technology has provided a new way of automatic pest monitoring in modern agriculture, which can greatly improve the monitoring efficiency.
Smart pest monitoring (SPM) refers to a new scientific area in integrated pest management (IPM), which has resulted from the rapid breakthrough of theories and technologies related to artificial intelligence (AI). The goal of SPM is to improve the automatic and intelligent collection of major crop insect pests, and promote the ability to monitor and give early warning for insect pests through the integration of the Internet of Things (IoT), big data, AI, and other modern information technologies and equipment. Specifically, automatic data collection, remote wireless transmission, intelligent online data processing, and accurate decision making can be achieved, eventually forming a new insect pest monitoring architecture. In the phase of data collection, there are many IoT devices for collecting image data related to insect pests, including sex-pheromone traps, yellow sticky traps, light traps and mobile phones. Images from these devices will be uploaded and processed on the remote servers using computer vision and machine learning algorithms during the data processing stage. Ultimately, the results of insect recognition and detection will be analyzed to estimate pest density based on related theories, thus helping to make decisions on control actions and precision pesticide spraying, which can help improve food quantity and reduce the economic losses.
This Special Issue will focus on recent developments in smart pest monitoring technology and practices, helping researchers and practitioners to clarify the methods, applications and challenges of information and digital technologies applied to pest monitoring and management. These developments will contribute to clarifying some current questions and point out feasible solutions for specific real problems, particularly in accurate pest detection and forecasting.
Submissions on the following topics are encouraged: (1) smart devices for pest monitoring, including bioinformatic-based and electronic-based devices; (2) data processing methods, including acoustic signal processing, image processing, hyperspectral or multispectral sensing, and behavior analysis and multimodal information processing; and (3) application solutions, including large-scale or small-scale solutions, for the monitoring of single or multiple pests.
Dr. Wenyong Li
Dr. Dongmei Chen
Dr. Jianming Du
Guest Editors
Manuscript Submission Information
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Keywords
- insect traps
- embedded system
- acoustic and image signals
- hyperspectral or multispectral sensing
- computer vision
- machine learning
- insect behavior
- data mining and visualization
- decision-making analysis and auxiliary diagnosis in agricultural life sciences
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