A Method of Green Citrus Detection in Natural Environments Using a Deep Convolutional Neural Network

Xiong, Juntao; Lin, Huan; Han, Yonglin; Bao-xia, Sun; Xie, Zhiming; Yang, Zhengang; Chen, Zhong

doi:10.3389/fpls.2021.705737

Cited by 22 publications

(10 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with the second-order target detection algorithm, the first-order algorithm has higher detection accuracy and detection efficiency. Some scholars have provided technical support for robotic intelligent citrus picking by pruning the backbone network of YOLOv4 algorithm and proposing a two-way pyramidal network (Bi-PANet) ( Zheng et al., 2021 ). An efficient network for detecting grape leaf pests was constructed by combining Inception structure, depth-separable convolution and dense connectivity structure, the accuracy of the model could reach 97.22% ( Liu et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Precision detection of crop diseases based on improved YOLOv5 model

Zhao

Yang

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

Accurate identification of crop diseases can effectively improve crop yield. Most current crop diseases present small targets, dense numbers, occlusions and similar appearance of different diseases, and the current target detection algorithms are not effective in identifying similar crop diseases. Therefore, in this paper, an improved model based on YOLOv5s was proposed to improve the detection of crop diseases. First, the CSP structure of the original model in the feature fusion stage was improved, and a lightweight structure was used in the improved CSP structure to reduce the model parameters, while the feature information of different layers was extracted in the form of multiple branches. A structure named CAM was proposed, which can extract global and local features of each network layer separately, and the CAM structure can better fuse semantic and scale inconsistent features to enhance the extraction of global information of the network. In order to increase the number of positive samples in the model training process, one more grid was added to the original model with three grids to predict the target, and the formula for the prediction frame centroid offset was modified to obtain the better prediction frame centroid offset when the target centroid falled on the special point of the grid. To solve the problem of the prediction frame being scaled incorrectly during model training, an improved DIoU loss function was used to replace the GIoU loss function used in the original YOLOv5s. Finally, the improved model was trained using transfer learning, the results showed that the improved model had the best mean average precision (mAP) performance compared to the Faster R-CNN, SSD, YOLOv3, YOLOv4, YOLOv4-tiny, and YOLOv5s models, and the mAP, F1 score, and recall of the improved model were 95.92%, 0.91, and 87.89%, respectively. Compared with YOLOv5s, they improved by 4.58%, 5%, and 4.78%, respectively. The detection speed of the improved model was 40.01 FPS, which can meet the requirement of real-time detection. The results showed that the improved model outperformed the original model in several aspects, had stronger robustness and higher accuracy, and can provide better detection for crop diseases.

show abstract

Section: Introductionmentioning

confidence: 99%

Precision detection of crop diseases based on improved YOLOv5 model

Zhao

Yang

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…The colors of green fruits are similar to those of fruit tree leaves in tropical and subtropical evergreen orchards. Thus, Zheng et al proposed the YOLO BP network to detect green citrus in the natural environment, and the results showed that the accuracy, recall, mean average precision (mAP), and detection speed of YOLO BP were 86%, 91% and 91.55% and 18 frames per second (FPS), respectively [11]. Kuznetsova et al used YOLOv3 and YOLOv5 in general and close-up images.…”

Section: Introductionmentioning

confidence: 99%

Green Citrus Detection and Counting in Orchards Based on YOLOv5-CS and AI Edge System

Lyu

Zhao

et al. 2022

Sensors

View full text Add to dashboard Cite

Green citrus detection in citrus orchards provides reliable support for production management chains, such as fruit thinning, sunburn prevention and yield estimation. In this paper, we proposed a lightweight object detection YOLOv5-CS (Citrus Sort) model to realize object detection and the accurate counting of green citrus in the natural environment. First, we employ image rotation codes to improve the generalization ability of the model. Second, in the backbone, a convolutional layer is replaced by a convolutional block attention module, and a detection layer is embedded to improve the detection accuracy of the little citrus. Third, both the loss function CIoU (Complete Intersection over Union) and cosine annealing algorithm are used to get the better training effect of the model. Finally, our model is migrated and deployed to the AI (Artificial Intelligence) edge system. Furthermore, we apply the scene segmentation method using the “virtual region” to achieve accurate counting of the green citrus, thereby forming an embedded system of green citrus counting by edge computing. The results show that the mAP@.5 of the YOLOv5-CS model for green citrus was 98.23%, and the recall is 97.66%. The inference speed of YOLOv5-CS detecting a picture on the server is 0.017 s, and the inference speed on Nvidia Jetson Xavier NX is 0.037 s. The detection and counting frame rate of the AI edge system-side counting system is 28 FPS, which meets the counting requirements of green citrus.

show abstract

“…Darknet has achieved state-of-the-art results with its recent YOLOv4 model (Bochkovskiy et al, 2020) and is known for its training and prediction speed on a single graphics processing unit (GPU). Darknet and versions of the YOLO models have been used in multiple fields, including medicine and diagnosis (Elgendi et al, 2020;Yao et al, 2022), agriculture (Zheng et al, 2021), construction and industry (Nath and Behzadan, 2020;Kohtala and Steinert, 2021), and autonomous vehicles (Cai et al, 2021) to name a few.…”

Section: Introductionmentioning

confidence: 99%

Automated Quantification of Human Osteoclasts Using Object Detection

Kohtala

Nedal²,

Kriesi³

et al. 2022

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

A balanced skeletal remodeling process is paramount to staying healthy. The remodeling process can be studied by analyzing osteoclasts differentiated in vitro from mononuclear cells isolated from peripheral blood or from buffy coats. Osteoclasts are highly specialized, multinucleated cells that break down bone tissue. Identifying and correctly quantifying osteoclasts in culture are usually done by trained personnel using light microscopy, which is time-consuming and susceptible to operator biases. Using machine learning with 307 different well images from seven human PBMC donors containing a total of 94,974 marked osteoclasts, we present an efficient and reliable method to quantify human osteoclasts from microscopic images. An open-source, deep learning-based object detection framework called Darknet (YOLOv4) was used to train and test several models to analyze the applicability and generalizability of the proposed method. The trained model achieved a mean average precision of 85.26% with a correlation coefficient of 0.99 with human annotators on an independent test set and counted on average 2.1% more osteoclasts per culture than the humans. Additionally, the trained models agreed more than two independent human annotators, supporting a more reliable and less biased approach to quantifying osteoclasts while saving time and resources. We invite interested researchers to test their datasets on our models to further strengthen and validate the results.

show abstract

A Method of Green Citrus Detection in Natural Environments Using a Deep Convolutional Neural Network

Cited by 22 publications

References 31 publications

Precision detection of crop diseases based on improved YOLOv5 model

Precision detection of crop diseases based on improved YOLOv5 model

Green Citrus Detection and Counting in Orchards Based on YOLOv5-CS and AI Edge System

Automated Quantification of Human Osteoclasts Using Object Detection

Contact Info

Product

Resources

About