ArticleFrontiers in plant science2022
Seedling maize counting method in complex backgrounds based on YOLOV5 and Kalman filter tracking algorithm.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Field-deployable lightweight YOLOv8n for real-time detection and counting of Maize seedlings using UAV RGB imagery.Frontiers in plant science · 2025Article
- Large-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application Strategy.Plants (Basel, Switzerland) · 2024Article
- Estimation of sorghum seedling number from drone image based on support vector machine and YOLO algorithms.Frontiers in plant science · 2024Article
- Detection and Identification of Tassel States at Different Maize Tasseling Stages Using UAV Imagery and Deep Learning.Plant phenomics (Washington, D.C.) · 2024Article
- Crop detection technologies, mechanical weeding executive parts and working performance of intelligent mechanical weeding: a review.Frontiers in plant science · 2024Review
- Detection of Pig Movement and Aggression Using Deep Learning Approaches.Animals : an open access journal from MDPI · 2023Article
- Maize plant detection using UAV-based RGB imaging and YOLOv5.Frontiers in plant science · 2023Article
- Recognition of soybean pods and yield prediction based on improved deep learning model.Frontiers in plant science · 2022Article
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3 authors.
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Abstract
Maize population density is one of the most essential factors in agricultural production systems and has a significant impact on maize yield and quality. Therefore, it is essential to estimate maize population density timely and accurately. In order to address the problems of the low efficiency of the manual counting method and the stability problem of traditional image processing methods in the field complex background environment, a deep-learning-based method for counting maize plants was proposed. Image datasets of the maize field were collected by a low-altitude UAV with a camera onboard firstly. Then a real-time detection model of maize plants was trained based on the object detection model YOLOV5. Finally, the tracking and counting method of maize plants was realized through Hungarian matching and Kalman filtering algorithms. The detection model developed in this study had an average precision mAP@0.5 of 90.66% on the test dataset, demonstrating the effectiveness of the SE-YOLOV5m model for maize plant detection. Application of the model to maize plant count trials showed that maize plant count results from test videos collected at multiple locations were highly correlated with manual count results (
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