Abstract
Introduction: Effective monitoring of insect-pests is vital for safeguarding agricultural yields and ensuring food security. Recent advances in computer vision and machine learning have opened up significant possibilities of automated persistent monitoring of insect-pests through reliable detection and counting of insects in setups such as yellow sticky traps. However, this task is fraught with complexities, encompassing challenges such as, laborious dataset annotation, recognizing small insect-pests in low-resolution or distant images, and the intricate variations across insect-pests life stages and species classes. Methods: to tackle these obstacles, this work investigates combining two solutions, Hierarchical Transfer Learning (HTL) and Slicing-Aided Hyper Inference (SAHI), along with applying a detection model. HTL pioneers a multi-step knowledge transfer paradigm, harnessing intermediary in-domain datasets to facilitate model adaptation. Moreover, slicing-aided hyper inference subdivides images into overlapping patches, conducting independent object detection on each patch before merging outcomes for precise, comprehensive results. Results: The outcomes underscore the substantial improvement achievable in detection results by integrating a diverse and expansive in-domain dataset within the HTL method, complemented by the utilization of SAHI. Discussion: We also present a hardware and software infrastructure for deploying such models for real-life applications. Our results can assist researchers and practitioners looking for solutions for insect-pest detection and quantification on yellow sticky traps.
Recommended Citation
F. Fotouhi and K. Menke and A. Prestholt and A. Gupta and M. E. Carroll and H. J. Yang and E. J. Skidmore and M. O'Neal and N. Merchant and S. K. Das and P. Kyveryga and B. Ganapathysubramanian and A. K. Singh and A. Singh and S. Sarkar, "Persistent Monitoring of Insect-Pests on Sticky Traps through Hierarchical Transfer Learning and Slicing-Aided Hyper Inference," Frontiers in Plant Science, vol. 15, article no. 1484587, Frontiers Media, Jan 2024.
The definitive version is available at https://doi.org/10.3389/fpls.2024.1484587
Department(s)
Computer Science
Keywords and Phrases
deep learning; Edge-IoT cyberinfrastructure; insect-pest monitoring; transfer learning; yellow sticky traps
International Standard Serial Number (ISSN)
1664-462X
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2025 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jan 2024
Comments
Iowa Soybean Association, Grant 1952045