Classifying and Detecting Live Insects with Computationally Effective Deep Learning Object Detection Models
Abstract A crucial part of agriculture is detecting insects that increase yield productivity. Insects in agricultural land are both helpful and harmful. The harmful insects are detected and controlled as early as possible, but these control measures should not affect the beneficial insects that help...
Saved in:
| Main Authors: | Arumuga Arun Rajeswaran, Karthik Katara, Yoganand Selvaraj, Ranjithkumar Sundarasamy |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-06-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-025-00885-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
High-Precision Stored-Grain Insect Pest Detection Method Based on PDA-YOLO
by: Fuyan Sun, et al.
Published: (2025-06-01) -
Two-stage object detection in low-light environments using deep learning image enhancement
by: Ghaith Al-refai, et al.
Published: (2025-04-01) -
Few-shot object detection for pest insects via features aggregation and contrastive learning
by: Shuqian He, et al.
Published: (2025-06-01) -
A lightweight mechanism for vision-transformer-based object detection
by: Yanming Ye, et al.
Published: (2025-05-01) -
DAMI-YOLOv8l: A multi-scale detection framework for light-trapping insect pest monitoring
by: Xiao Chen, et al.
Published: (2025-05-01)