Automatic Tassel Detection to Estimate Flowering Date in the UAV Images using Deep Learning Techniques

Document Type : Research Paper


1 Image Processing Lab, Dep. of Physics, Shahid Bahonar University, Kerman, Iran

2 Dep. of Physics, Shahid Bahonar University, Kerman, Iran


Estimating crop yields and examining growth trends in different species of a crop in precision agriculture is very important for researchers and agricultural experts. In this article, a new technique based on one-stage objection detection called GP-YOLOv5 for automatic tassel detection in the UAV images of a large maize field at different growing stages and flowering date estimation is presented. Because of the existing small number of tassels in the early stages of growth, GP-GAN is used to augment the training data. After that, the hyperparameters of the YOLOv5 are optimized to increase the tassel detection accuracy. Plant counting using CenterNet in the early stage of growth is calculated to determine the flowering date. Finally, well-known interpolation and prediction algorithms are used to estimate the flowering date. The proposed method is compared with two state-of-the-art methods based on detection “CenterNet” and regression “TasselNetv2+” technique for tassel counting. The average accuracy of GP-YOLOv5 for tassel detection is around 96.81 % and for the CenterNet method, it is around 81.78 %, which indicates that the accuracy of the proposed method is higher than the CenterNet technique.