Seedling maize counting method in complex backgrounds based on YOLOV5 and Kalman filter tracking algorithm

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 (R2 = 0.92), illustrating the accuracy and validity of the counting method. Therefore, the maize plant identification and counting method proposed in this study can better achieve the detection and counting of maize plants in complex backgrounds and provides a research basis and theoretical basis for the rapid acquisition of maize plant population density.