ArticleSensors (Basel, Switzerland)2018
A Vision-Based Counting and Recognition System for Flying Insects in Intelligent Agriculture.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Towards sustainable management ofData in brief · 2026Article
- Deep learning-based seed germination prediction using morphological traits and RGB images.BMC plant biology · 2026Article
- Yolo-pest: an optimized YoloV8x for detection of small insect pests using smart traps.Scientific reports · 2025Article
- Energy-environmental evaluation of conventional and variable rate technology sprayer; application of Life Cycle Assessment.PloS one · 2025Article
- A counting method of whiteflies on crop leave images captured by AR glasses based on segmentation and improved YOLOv11 models.Frontiers in plant science · 2025Article
- GIWT-YOLO: an efficient multi-scale framework for real-time Scolytinae pests detection.Frontiers in insect science · 2025Article
- Enhancing mosquito classification through self-supervised learning.Scientific reports · 2024Article
- Artificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographs.Scientific reports · 2024Article
- Lure Monitoring for Mediterranean Fruit Fly Traps Using Air Quality Sensors.Sensors (Basel, Switzerland) · 2024Article
- Evaluation of Rodent Hair Tubes for Activity Indices.Animals : an open access journal from MDPI · 2024Article
- Persistent monitoring of insect-pests on sticky traps through hierarchical transfer learning and slicing-aided hyper inference.Frontiers in plant science · 2024Article
- Automated software for counting and measuring Hyalella genus using artificial intelligence.Environmental science and pollution research international · 2023Article
- Towards automatic insect monitoring on witloof chicory fields using sticky plate image analysis.Ecological informatics · 2023Article
- A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton.Sensors (Basel, Switzerland) · 2023Review
- Article
- From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring.Frontiers in plant science · 2023Review
- New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review.Frontiers in plant science · 2023Review
- Precision agricultural robotic sprayer with real-time Tobacco recognition and spraying system based on deep learning.PloS one · 2023Article
- Leaf Count Aided Novel Framework for Rice (Plants (Basel, Switzerland) · 2022Article
- An image dataset of fruitfly species (Bactrocera Zonata and Bactrocera Dorsalis) and automated species classification through object detection.Data in brief · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapid and accurate counting and recognition of flying insects are of great importance, especially for pest control. Traditional manual identification and counting of flying insects is labor intensive and inefficient. In this study, a vision-based counting and classification system for flying insects is designed and implemented. The system is constructed as follows: firstly, a yellow sticky trap is installed in the surveillance area to trap flying insects and a camera is set up to collect real-time images. Then the detection and coarse counting method based on You Only Look Once (YOLO) object detection, the classification method and fine counting based on Support Vector Machines (SVM) using global features are designed. Finally, the insect counting and recognition system is implemented on Raspberry PI. Six species of flying insects including bee, fly, mosquito, moth, chafer and fruit fly are selected to assess the effectiveness of the system. Compared with the conventional methods, the test results show promising performance. The average counting accuracy is 92.50% and average classifying accuracy is 90.18% on Raspberry PI. The proposed system is easy-to-use and provides efficient and accurate recognition data, therefore, it can be used for intelligent agriculture applications.
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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.