A Low-Cost Remotely Configurable Electronic Trap for Insect Pest Dataset Generation
Source abstract
Abstract
The precise monitoring of insect pest populations is the foundation of Integrated Pest Management (IPM) for pests of plants, humans, and animals. Digital technologies can be employed to address one of the main challenges, such as reducing the IPM workload and enhancing decision-making accuracy. In this study, digital technologies are used to deploy an automated trap for capturing images of insects and generating centralized repositories on a server. Subsequently, advanced computational models can be applied to analyze the collected data. The study provides a detailed description of the prototype, designed with a particular focus on its remote reconfigurability to optimize repository quality; and the server, accessible via an API interface to enhance system interoperability and scalability. Quality metrics are presented through an experimental study conducted on the constructed demonstrator, emphasizing trap reliability, stability, performance, and energy consumption, along with an objective analysis of image quality using metrics such as RMS contrast, Image Entropy, Image sharpness metric, Natural Image Quality Evaluator (NIQE), and Modulation Transfer Function (MFT). This study contributes to the optimization of the current knowledge regarding automated insect pest monitoring techniques and offers advanced solutions for the current systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Plant and animal studies · Greenhouse Technology and Climate Control
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Electronics and Computer Engineering Department, University of Córdoba, 14071 Cordoba, Spain
Agronomy Department, University of Córdoba, 14071 Cordoba, Spain
Funding
MCIN/AEI/10.13039/501100011033 · Spanish State Research Agency · Secretary of State for Digitalization and Artificial Intelligence · Universidad de Córdoba/CBUA
References (42)
Show source-provided references
1. Dent, D., and Binks, R.H. (2020). Insect Pest Management, CABI. [3rd ed.].10.1079/9781789241051.0000 ↗
2. Pedigo, L.P., Rice, M.E., and Krell, R.K. (2021). Entomology and Pest Management, Waveland Press.
3. Azfar, S., Nadeem, A., Ahsan, K., Mehmood, A., Siddiqui, M.S., Saeed, M., and Ashraf, M. (2023). An IoT-Based System for Efficient Detection of Cotton Pest. Appl. Sci., 13.10.3390/app13052921 ↗
4. The Federal Integrated Pest Management (IPM) Coordinating Committee (FIPMCC) (2018). A National Road Map for Integrated Pest Management, Technical Report.
5. Costa, C.M., Camargo, A.P., da Silva, E.A., and Bento, J.M.S. (2022). Automated Barometric Chamber for Entomology Experiments: Arthropods’ Behavior and Insect-Plant Interactions. Appl. Sci., 12.10.3390/app12146971 ↗
6. Stringer, L.D., Soopaya, R., Butler, R.C., Vargas, R.I., Souder, S.K., Jessup, A.J., Woods, B., Cook, P.J., and Suckling, D.M. (2019). Effect of Lure Combination on Fruit Fly Surveillance Sensitivity. Sci. Rep., 9.10.1038/s41598-018-37487-6 ↗
7. Böckmann, E., Pfaff, A., Schirrmann, M., and Pflanz, M. (2021). Rapid and low-cost insect detection for analysing species trapped on yellow sticky traps. Sci. Rep., 11.10.1038/s41598-021-89930-w ↗
8. Development of a Compact, Cost-Effective Photoacoustic Spectral Response Measurement System for Biomedical Applications
9. Raspberry Pi-Based Low-Cost System for Electrical Parameters Gathering and Analysis for Performance Evaluation of Electric Vehicles
10. A Low-Cost Efficient System for Monitoring Microalgae Density Using Gaussian Process
11. A Small Versatile Electrical Robot for Autonomous Spraying in Agriculture10.3390/agriengineering1030029 ↗
12. Development of Automated Devices for the Monitoring of Insect Pests10.12944/CARJ.7.1.03 ↗
13. Cardim Ferreira Lima, M., Damascena de Almeida Leandro, M.E., Valero, C., Pereira Coronel, L.C., and Gonçalves Bazzo, C.O. (2020). Automatic Detection and Monitoring of Insect Pests—A Review. Agriculture, 10.10.3390/agriculture10050161 ↗
14. Automatic identification of insects from digital images: A survey10.1016/j.compag.2020.105784 ↗
15. Gonçalves, J., Silva, E., Faria, P., Nogueira, T., Ferreira, A., Carlos, C., and Rosado, L. (2022). Edge-Compatible Deep Learning Models for Detection of Pest Outbreaks in Viticulture. Agronomy, 12.10.3390/agronomy12123052 ↗
16. Multiple Diseases and Pests Detection Based on Federated Learning and Improved Faster R-CNN10.1109/TIM.2022.3201937 ↗
17. An Effective Data Augmentation Strategy for CNN-Based Pest Localization and Recognition in the Field10.1109/ACCESS.2019.2949852 ↗
18. A Method to Establish a Synthetic Image Dataset of Stored-Product Insects for Insect Detection10.1109/ACCESS.2022.3188282 ↗
19. XFARM (2024, November 06). Protección de Cultivos|Software y App xFarm. Available online: https://xfarm.ag/es/xfarm-analytics.
20. Faria, P., Nogueira, T., Ferreira, A., Carlos, C., and Rosado, L. (2021). AI-Powered Mobile Image Acquisition of Vineyard Insect Traps with Automatic Quality and Adequacy Assessment. Agronomy, 11.10.3390/agronomy11040731 ↗
21. IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework
22. Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Whitefly Detection in IoT Web Application10.3390/agriengineering4020034 ↗
23. Developing a smart trap prototype equipped with camera for tortricid pests remote monitoring
24. Automated Pest Detection with DNN on the Edge for Precision Agriculture10.1109/JETCAS.2021.3101740 ↗
25. Remote monitoring of the Bactrocera oleae (Gmelin) (Diptera: Tephritidae) population using an automated McPhail trap10.1016/j.compag.2017.03.014 ↗
26. Development of an Automated Multidirectional Pest Sampling Detection System Using Motorized Sticky Traps10.1109/ACCESS.2021.3074083 ↗
27. Azfar, S., Nadeem, A., Ahsan, K., Mehmood, A., Almoamari, H., and Alqahtany, S.S. (2023). IoT-Based Cotton Plant Pest Detection and Smart-Response System. Appl. Sci., 13.10.3390/app13031851 ↗
28. A Robust and Rapid Camera Calibration Method by One Captured Image10.1109/TIM.2018.2884583 ↗
29. Suto, J. (2023). Hardware and Software Support for Insect Pest Management. Agriculture, 13.10.3390/agriculture13091818 ↗
30. Suto, J. (2022). Codling Moth Monitoring with Camera-Equipped Automated Traps: A Review. Agriculture, 12.10.3390/agriculture12101721 ↗
31. The Influence of Autofocus Lenses in the Camera Calibration Process
32. Optimization of the ISP Parameters of a Camera through Differential Evolution10.1109/ACCESS.2020.3014558 ↗
33. Contrast in complex images10.1364/JOSAA.7.002032 ↗
34. IIE-SegNet: Deep Semantic Segmentation Network with Enhanced Boundary Based on Image Information Entropy10.1109/ACCESS.2021.3064346 ↗
35. Blind Image Quality Assessment with Joint Entropy Degradation10.1109/ACCESS.2019.2901063 ↗
36. Zhu, M., Yu, L., Wang, Z., Ke, Z., and Zhi, C. (2023). Review: A Survey on Objective Evaluation of Image Sharpness. Appl. Sci., 13.10.3390/app13042652 ↗
37. Making a “Completely Blind” Image Quality Analyzer10.1109/LSP.2012.2227726 ↗
38. Smith, W.J. (2007). Modern Optical Engineering, SPIE Press. [4th ed.].
39. Influence of image quality on the identification of psyllids using convolutional neural networks10.1016/j.biosystemseng.2019.04.007 ↗
40. Insect pest monitoring with camera-equipped traps: Strengths and limitations10.1007/s10340-020-01309-4 ↗
41. Leon-Garcia, F., Palomares, J., and Olivares, J. (2018). D2R-TED: Data—Domain Reduction Model for Threshold-Based Event Detection in Sensor Networks. Sensors, 18.10.3390/s18113806 ↗
42. Data Communication Optimization for the Evaluation of Multivariate Conditions in Distributed Scenarios10.1109/ACCESS.2019.2936918 ↗