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764 results on '"Lan, Yubin"'

157. Inversion of Leaf Area Index in Citrus Trees Based on Multi-Modal Data Fusion from UAV Platform.

Author

Lu, Xiaoyang, Li, Wanjian, Xiao, Junqi, Zhu, Hongyun, Yang, Dacheng, Yang, Jing, Xu, Xidan, Lan, Yubin, and Zhang, Yali

Subjects
LEAF area index, DRONE aircraft, ARTIFICIAL neural networks, MULTISENSOR data fusion, FOOD crops, CITRUS, TREE growth
Abstract

The leaf area index (LAI) is an important growth indicator used to assess the health status and growth of citrus trees. Although LAI estimation based on unmanned aerial vehicle (UAV) platforms has been widely used for field crops, mainly focusing on food crops, less research has been reported on the application to fruit trees, especially citrus trees. In addition, most studies have used single-modal data for modeling, but some studies have shown that multi-modal data can be effective in improving experimental results. This study utilizes data collected from a UAV platform, including RGB images and point cloud data, to construct single-modal regression models named VoVNet (using RGB data) and PCNet (using point cloud data), as well as a multi-modal regression model called VPNet (using both RGB data and point cloud data). The LAI of citrus trees was estimated using deep neural networks, and the results of two experimental hyperparameters (loss function and learning rate) were compared under different parameters. The results of the study showed that VoVNet had Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-Squared (R2) of 0.129, 0.028, and 0.647, respectively. In comparison, PCNet decreased by 0.051 and 0.014 to 0.078 and 0.014 for MAE and MSE, respectively, while R2 increased by 0.168 to 0.815. VPNet decreased by 0% and 42.9% relative to PCNet in terms of MAE and MSE to 0.078 and 0.008, respectively, while R2 increased by 5.6% to 0.861. In addition, the use of loss function L1 gave better results than L2, while a lower learning rate gave better results. It is concluded that the fusion of RGB data and point cloud data collected by the UAV platform for LAI estimation is capable of monitoring citrus trees' growth process, which can help farmers to track the growth condition of citrus trees and improve the efficiency and quality of orchard management. [ABSTRACT FROM AUTHOR]

Published
2023
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158. Detection of Power Poles in Orchards Based on Improved Yolov5s Model.

Author

Zhang, Yali, Lu, Xiaoyang, Li, Wanjian, Yan, Kangting, Mo, Zhenjie, Lan, Yubin, and Wang, Linlin

Subjects
DEEP learning, OBJECT recognition (Computer vision), AGRICULTURAL technology, ORCHARDS, DRONE aircraft, DATA augmentation, POLISH people
Abstract

During the operation of agricultural unmanned aerial vehicles (UAVs) in orchards, the presence of power poles and wires pose a serious threat to flight safety, and can even lead to crashes. Due to the difficulty of directly detecting wires, this research aimed to quickly and accurately detect wire poles, and proposed an improved Yolov5s deep learning object detection algorithm named Yolov5s-Pole. The algorithm enhances the model's generalization ability and robustness by applying Mixup data augmentation technique, replaces the C3 module with the GhostBottleneck module to reduce the model's parameters and computational complexity, and incorporates the Shuffle Attention (SA) module to improve its focus on small targets. The results show that when the improved Yolov5s-Pole model was used for detecting poles in orchards, its accuracy, recall, and mAP@50 were 0.803, 0.831, and 0.838 respectively, which increased by 0.5%, 10%, and 9.2% compared to the original Yolov5s model. Additionally, the weights, parameters, and GFLOPs of the Yolov5s-Pole model were 7.86 MB, 3,974,310, and 9, respectively. Compared to the original Yolov5s model, these represent compression rates of 42.2%, 43.4%, and 43.3%, respectively. The detection time for a single image using this model was 4.2 ms, and good robustness under different lighting conditions (dark, normal, and bright) was demonstrated. The model is suitable for deployment on agricultural UAVs' onboard equipment, and is of great practical significance for ensuring the efficiency and flight safety of agricultural UAVs. [ABSTRACT FROM AUTHOR]

Published
2023
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167. Back Cover Image, Volume 78, Issue 8

Author

Guo, Shuang, primary, Yao, Weixiang, additional, Xu, Tongyu, additional, Ma, Hui, additional, Sun, Mujun, additional, Chen, Chunling, additional, and Lan, Yubin, additional

Published
2022
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175. Evaluating the Influence of Deficit Irrigation on Fruit Yield and Quality Indices of Tomatoes Grown in Sandy Loam and Silty Loam Soils

Author

Alordzinu, Kelvin Edom, primary, Appiah, Sadick Amoakohene, additional, AL Aasmi, Alaa, additional, Darko, Ransford Opoku, additional, Li, Jiuhao, additional, Lan, Yubin, additional, Adjibolosoo, Daniel, additional, Lian, Chenguo, additional, Wang, Hao, additional, Qiao, Songyang, additional, and Liao, Juan, additional

Published
2022
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176. Real-Time Assessment of Mandarin Crop Water Stress Index

Author

Appiah, Sadick Amoakohene, primary, Li, Jiuhao, additional, Lan, Yubin, additional, Darko, Ransford Opoku, additional, Alordzinu, Kelvin Edom, additional, Al Aasmi, Alaa, additional, Asenso, Evans, additional, Issaka, Fuseini, additional, Afful, Ebenezer Acheampong, additional, Wang, Hao, additional, and Qiao, Songyang, additional

Published
2022
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185. Detection and Location of Dead Trees with Pine Wilt Disease Based on Deep Learning and UAV Remote Sensing

Author

Lan Yubin, Huang Zixiao, Xiaoling Deng, and Tong Zejing

Subjects
0106 biological sciences, UAV remote sensing, Computer science, Pharmaceutical Science, 02 engineering and technology, geographical information, 01 natural sciences, Convolutional neural network, 0202 electrical engineering, electronic engineering, information engineering, Pharmacology (medical), Dead tree, lcsh:Agriculture (General), Remote sensing, Wilt disease, pine wilt disease, business.industry, Deep learning, deep learning, lcsh:S1-972, Complementary and alternative medicine, Remote sensing (archaeology), Pine wood, lcsh:TA1-2040, Optimization methods, 020201 artificial intelligence & image processing, Artificial intelligence, Faster-RCNN, business, Scale (map), lcsh:Engineering (General). Civil engineering (General), 010606 plant biology & botany
Abstract

Pine wilt disease causes huge economic losses to pine wood forestry because of its destructiveness and rapid spread. This paper proposes a detection and location method of pine wood nematode disease at a large scale adopting UAV (Unmanned Aerial Vehicle) remote sensing and artificial intelligence technology. The UAV remote sensing images were enhanced by computer vision tools. A Faster-RCNN (Faster Region Convolutional Neural Networks) deep learning framework based on a RPN (Region Proposal Network) network and the ResNet residual neural network were used to train the pine wilt diseased dead tree detection model. The loss function and the anchors in the RPN of the convolutional neural network were optimized. Finally, the location of pine wood nematode dead tree was conducted, which generated the geographic information on the detection results. The results show that ResNet101 performed better than VGG16 (Visual Geometry Group 16) convolutional neural network. The detection accuracy was improved and reached to about 90% after a series of optimizations to the network, meaning that the optimization methods proposed in this paper are feasible to pine wood nematode dead tree detection.

Published
2020

191. Impacts of Slow-Release Nitrogen Fertilizer Rates on the Morpho-Physiological Traits, Yield, and Nitrogen Use Efficiency of Rice under Different Water Regimes

Author

AL Aasmi, Alaa, primary, Li, Jiuhao, additional, Hamoud, Yousef Alhaj, additional, Lan, Yubin, additional, Alordzinu, Kelvin Edom, additional, Appiah, Sadick Amoakohene, additional, Shaghaleh, Hiba, additional, Sheteiwy, Mohamed, additional, Wang, Hao, additional, Qiao, Songyang, additional, and Yu, Chaoran, additional

Published
2022
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199. An improved YOLO model for detecting trees suffering from pine wilt disease at different stages of infection.

Author

Wu, Kunjie, Zhang, Jiantao, Yin, Xuanchun, Wen, Sheng, and Lan, Yubin

Subjects
DISEASE progression, CONIFER wilt, DRONE aircraft, DEEP learning, TREES
Abstract

Pine wilt disease (PWD) is one of the most destructive forest diseases in the world. Therefore, timely monitoring of PWD is essential to the preservation of the ecological environment. However, the complex topography of PWD-outbreak locations affords many limitations in the manual detection of the disease. The use of unmanned aerial vehicles (UAVs) and deep learning technology to detect PWD-infected trees has gained popularity in recent years. In this study, we configured the You Only Look Once version 3 (YOLOv3) model according to the characteristics of the three disease stages of PWD and proposed an improved model called Effi_YOLO_v3. The results revealed that the improved model achieved good detection performance. The proposed model yielded a mean average precision (mAP) of 94.39%, and the classification of the different infection stages was relatively accurate. The recall values for the classifications of trees in the early-infection, late-infection, and death stages were 89.15%, 86.13%, and 86.77%, respectively. This indicates that the model offers good applicability in detecting different stages of PWD in trees. [ABSTRACT FROM AUTHOR]

Published
2023
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