ArticleInsects2023
A New Pest Detection Method Based on Improved YOLOv5m.
Article in Insects, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Improving RGB image recognition in the YOLO11n algorithm for accurate detection of tea plant diseases.Journal of Zhejiang University. Science. B · 2026Article
- Integrating morphological and deep learning approaches for the identification of economically important nematode genera in vineyards:Helminthologia · 2026Article
- OCDBMamba: A Robust and Efficient Road Pothole Detection Framework with Omnidirectional Context and Consensus-Based Boundary Modeling.Sensors (Basel, Switzerland) · 2026Article
- Pest Detection in Edible Crops at the Edge: An Implementation-Focused Review of Vision, Spectroscopy, and Sensors.Sensors (Basel, Switzerland) · 2025Review
- Audio signal analysis using a modified forward-forward algorithm with enhanced segmentation for soil pest detection.Scientific reports · 2025Article
- Detection techniques for tomato diseases under non-stationary climatic conditions.Frontiers in plant science · 2025Article
- Article
- International trade market forecasting and decision-making system: multimodal data fusion under meta-learning.PeerJ. Computer science · 2025Article
- Deep learning-based rice pest detection research.PloS one · 2024Article
- TP-Transfiner: high-quality segmentation network for tea pest.Frontiers in plant science · 2024Article
- Determining the Presence and Size of Shoulder Lesions in Sows Using Computer Vision.Animals : an open access journal from MDPI · 2023Article
- Aphid Recognition and Counting Based on an Improved YOLOv5 Algorithm in a Climate Chamber Environment.Insects · 2023Article
- Article
- A Novel Deep Learning Model for Accurate Pest Detection and Edge Computing Deployment.Insects · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Pest detection in plants is essential for ensuring high productivity. Convolutional neural networks (CNN)-based deep learning advancements recently have made it possible for researchers to increase object detection accuracy. In this study, pest detection in plants with higher accuracy is proposed by an improved YOLOv5m-based method. First, the SWin Transformer (SWinTR) and Transformer (C3TR) mechanisms are introduced into the YOLOv5m network so that they can capture more global features and can increase the receptive field. Then, in the backbone, ResSPP is considered to make the network extract more features. Furthermore, the global features of the feature map are extracted in the feature fusion phase and forwarded to the detection phase via a modification of the three output necks C3 into SWinTR. Finally, WConcat is added to the fusion feature, which increases the feature fusion capability of the network. Experimental results demonstrate that the improved YOLOv5m achieved 95.7% precision rate, 93.1% recall rate, 94.38%
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.