ArticleScientific reports2024
A lightweight rice pest detection algorithm based on improved YOLOv8.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- SLR-YOLO: An Improved YOLO-Based Method for Accurate Detection of Potato Leaf Diseases in Complex Field Images.Plants (Basel, Switzerland) · 2026Article
- EP-YOLO: An Enhanced Lightweight Model for Micro-Pest Detection in Agricultural Light-Trap Environments.Sensors (Basel, Switzerland) · 2026Article
- Proposal for Computationally Efficient Fog Computing System for Coffee Berry Borer Detection via Optimized YOLOv26.Sensors (Basel, Switzerland) · 2026Article
- Crop Pest Identification and Real-Time Monitoring System Design Based on Improved YOLOv8s.Sensors (Basel, Switzerland) · 2026Article
- Rice pest detection via multi-scale edge network and wavelet attention enhancement.Frontiers in plant science · 2026Article
- HDA-YOLO: a hierarchical and densely-fused attention network for rice pest detection in complex agricultural environments.Frontiers in plant science · 2026Article
- Attention-PestNet: hierarchical scaled dot-product attention for insect pest detection.Plant methods · 2025Article
- Improving Rice Pest Management Through RP11: A Scientifically Annotated Dataset for Adult Insect Recognition.Life (Basel, Switzerland) · 2025Article
- A non-invasive diagnostic approach for neuroblastoma utilizing preoperative enhanced computed tomography and deep learning techniques.Scientific reports · 2025Article
- Exploration and future prospects of intelligent supervision technology and system development in biological laboratories.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Timely and accurate detection of rice pests is highly important for pest control, as well as for improving rice yield and quality. However, owing to the high interclass similarity, significant intraclass age differences, and complex backgrounds among different pests, accurately and rapidly identifying a variety of rice pests via deep neural network models poses a significant challenge. To address this issue, this paper presents a fast and accurate method for rice pest detection and identification named Rice-YOLO (You Only Look Once). This model is based on YOLOv8-N and incorporates an efficient detection head designed for the complex characteristics of pests. Additionally, deep supervision layers were introduced into the network, along with the incorporation and improvement of the dynamic upsampling module. The experimental data included the large-scale pest public dataset IP102 and the sixteen-class rice pest dataset R2000. The experimental results demonstrated that Rice-YOLO outperformed previous object detection algorithms, with 78.1% mAP@0.5, 62.9% mAP@0.5:0.95, and 74.3% F1 scores.
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.