Abstract
The detection and counting of wheat ears are very important for crop field management, yield estimation, and phenotypic analysis. Previous studies have shown that most methods for detecting wheat ears were based on shallow features such as color and texture extracted by machine learning methods, which have obtained good results. However, due to the lack of robustness of these features, it was difficult for the above-mentioned methods to meet the detection and counting of wheat ears in natural scenes. Other studies have shown that convolutional neural network (CNN) methods could be used to achieve wheat ear detection and counting. However, the adhesion and occlusion of wheat ears limit the accuracy of detection. Therefore, to improve the accuracy of wheat ear detection and counting in the field, an improved YOLOv4 (you only look once v4) with CBAM (convolutional block attention module) including spatial and channel attention model was proposed that could enhance the feature extraction capabilities of the network by adding receptive field modules. In addition, to improve the generalization ability of the model, not only local wheat data (WD), but also two public data sets (WEDD and GWHDD) were used to construct the training set, the validation set, and the test set. The results showed that the model could effectively overcome the noise in the field environment and realize accurate detection and counting of wheat ears with different density distributions. The average accuracy of wheat ear detection was 94%, 96.04%, and 93.11%. Moreover, the wheat ears were counted on 60 wheat images. The results showed that R2 = 0.8968 for WD, 0.955 for WEDD, and 0.9884 for GWHDD. In short, the CBAM-YOLOv4 model could meet the actual requirements of wheat ear detection and counting, which provided technical support for other high-throughput parameters of the extraction of crops.
Highlights
Wheat is one of the most important food crops and plays an important role in food security
The resolution of the smallest wheat ear was no less than 15 × 15 pixels, and it was ignored if the size was too small
20 images were were randomly selected fromfrom each each test set wheat data (WD), WEDD, and Global Wheat Head Detection Dataset (GWHDD), and ears, 20 images randomly selected testofset of WD, WEDD, and GWHDD, 617, 535, ears were countedcounted on the images
Summary
Wheat is one of the most important food crops and plays an important role in food security. Accurately identifying and counting wheat ears is of great significance for monitoring crop growth, estimating wheat yield, and analyzing plant phenotypic characteristics. Image processing and feature extraction have gradually become key technologies for wheat ear recognition, and have made excellent contributions to improving the accuracy of detection and counting. Previous studies have shown that some features were used to successfully detect wheat ears from the background, including texture, color, and morphology. It was difficult to accurately identify wheat ears by using color features [6]. Some scholars have proposed counting wheat ears based on their texture and color characteristics [7,8]. In the heading stage, wheat ears and leaves have similar texture characteristics that affect detection accuracy. The detection and counting of wheat ears in the natural environment still faces great challenges
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
