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

1. National Engineering Research Center for Information Technology in Agriculture, Beijing, China
2. National Engineering Laboratory for Agri-Product Quality Traceability, Beijing, China
Interests: computer vision; image and signal processing; machine learning; embedded system; pest recognition and detection; precision agriculture

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Guest Editor
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: image analysis; data mining and visualization; decision-making analysis and auxiliary diagnosis in agricultural life sciences

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Guest Editor
Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China
Interests: image processing; computer vision; voice recognition; artificial neural network; intelligent agricultural; pest and disease recognition

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

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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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Published Papers (2 papers)

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Research

34 pages, 36990 KiB  
Article
Integrating Low-Altitude Remote Sensing and Variable-Rate Sprayer Systems for Enhanced Cassava Crop Management
by Pongpith Tuenpusa, Grianggai Samseemoung, Peeyush Soni, Thirapong Kuankhamnuan, Waraphan Sarasureeporn, Warinthon Poonsri and Apirat Pinthong
AgriEngineering 2025, 7(6), 195; https://doi.org/10.3390/agriengineering7060195 - 17 Jun 2025
Viewed by 366
Abstract
Integrating remote-controlled (RC) helicopters and drones equipped with variable-rate sprayer systems represents a significant advancement in agricultural practices, particularly for the precise management of crop diseases. This study utilizes low-altitude remote sensing platforms to monitor crop growth and disease infestation, proposing advanced technology [...] Read more.
Integrating remote-controlled (RC) helicopters and drones equipped with variable-rate sprayer systems represents a significant advancement in agricultural practices, particularly for the precise management of crop diseases. This study utilizes low-altitude remote sensing platforms to monitor crop growth and disease infestation, proposing advanced technology for managing and monitoring disease outbreaks in cassava fields. The performance of these systems was evaluated using statistical analysis and Geographic Information System (GIS) applications for mapping, with a particular emphasis on the relationship between vegetation indices (NDVI and GNDVI) and the growth stages of cassava. The results indicated that NDVI values obtained from both the RC helicopter and drone systems decreased with increasing altitude. The RC helicopter system exhibited NDVI values ranging from 0.709 to 0.352, while the drone system showed values from 0.726 to 0.361. Based on the relationship between NDVI and GNDVI of cassava plants at different growth stages, the study recommends a variable-rate spray system that utilizes standard instruments to measure chlorophyll levels. Furthermore, the study found that the RC helicopter system effectively measured chlorophyll levels, while the drone system demonstrated superior overall quality. Both systems showed strong correlations between NDVI/GNDVI values and cassava health, which has significant implications for disease management. The image processing algorithms and calibration methods used were deemed acceptable, with drones equipped with variable-rate sprayer systems outperforming RC helicopters in overall quality. These findings support the adoption of advanced remote sensing and spraying technologies in precision agriculture, particularly to enhance the management of cassava crops. Full article
(This article belongs to the Special Issue Smart Pest Monitoring Technology)
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19 pages, 3913 KiB  
Article
Morning Glory Flower Detection in Aerial Images Using Semi-Supervised Segmentation with Gaussian Mixture Models
by Sruthi Keerthi Valicharla, Jinge Wang, Xin Li, Srikanth Gururajan, Roghaiyeh Karimzadeh and Yong-Lak Park
AgriEngineering 2024, 6(1), 555-573; https://doi.org/10.3390/agriengineering6010034 - 1 Mar 2024
Cited by 1 | Viewed by 1952
Abstract
The invasive morning glory, Ipomoea purpurea (Convolvulaceae), poses a mounting challenge in vineyards by hindering grape harvest and as a secondary host of disease pathogens, necessitating advanced detection and control strategies. This study introduces a novel automated image analysis framework using aerial images [...] Read more.
The invasive morning glory, Ipomoea purpurea (Convolvulaceae), poses a mounting challenge in vineyards by hindering grape harvest and as a secondary host of disease pathogens, necessitating advanced detection and control strategies. This study introduces a novel automated image analysis framework using aerial images obtained from a small fixed-wing unmanned aircraft system (UAS) and an RGB camera for the large-scale detection of I. purpurea flowers. This study aimed to assess the sampling fidelity of aerial detection in comparison with the actual infestation measured by ground validation surveys. The UAS was systematically operated over 16 vineyard plots infested with I. purpurea and another 16 plots without I. purpurea infestation. We used a semi-supervised segmentation model incorporating a Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to detect and count I. purpurea flowers. The flower detectability of the GMM was compared with that of conventional K-means methods. The results of this study showed that the GMM detected the presence of I. purpurea flowers in all 16 infested plots with 0% for both type I and type II errors, while the K-means method had 0% and 6.3% for type I and type II errors, respectively. The GMM and K-means methods detected 76% and 65% of the flowers, respectively. These results underscore the effectiveness of the GMM-based segmentation model in accurately detecting and quantifying I. purpurea flowers compared with a conventional approach. This study demonstrated the efficiency of a fixed-wing UAS coupled with automated image analysis for I. purpurea flower detection in vineyards, achieving success without relying on data-driven deep-learning models. Full article
(This article belongs to the Special Issue Smart Pest Monitoring Technology)
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