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Article
Computer Science, Artificial Intelligence
Yunchao Tang et al.
Summary: In this study, a fruit detection model method called YOLO-Oleifera was developed based on the YOLOv4-tiny model. The YOLO-Oleifera model improved upon the YOLOv4-tiny model by adding convolutional kernels and using the k-means++ clustering algorithm. It innovatively used bounding boxes for stereo matching to determine the picking position of the fruit. Experimental results showed that the model achieved stable detection performance and low computational complexity under different illumination conditions.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Agriculture, Multidisciplinary
Tian-Hu Liu et al.
Summary: This paper proposes a method for detecting and localizing pineapples in natural environments using binocular stereo vision and an improved YOLOv3 model. Experimental results show that the improved model outperforms other models in terms of detection accuracy and localization precision.
PRECISION AGRICULTURE
(2023)
Article
Agriculture, Multidisciplinary
Shang Chen et al.
Summary: This study proposes a YOLO-COF lightweight multiclass occlusion target detection method based on the YOLOv5s framework, which automatically filters and selects target datasets using the K-means++ clustering algorithm. It incorporates Coordinate Attention to enhance the feature extraction ability of occluded targets and improve the generalizability and robustness of the model. Experimental results show that YOLO-COF achieves AP values of 0.97, 0.94, 0.93, and 0.92 for the detection of NO, OL, OB, and OF, respectively. Compared to other neural network models, YOLO-COF achieves a good balance between precision, speed, and size. Its mAP value is 94.10%, frame rate is 74.8FPS, and model size is 27.1 MB. The proposed model has the potential to facilitate the operational decision-making of vision robot during the process of Camellia oleifera fruit harvesting.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Automation & Control Systems
Tao Li et al.
Summary: This research presents a new image-based uncalibrated visual servoing control approach called hybrid-IBUVS, which combines a hybrid visual configuration and an adaptive tracking controller. The approach utilizes eye-in-hand and fixed red-green-blue-depth cameras and incorporates multiobject detection and edge-computing technologies. Adaptive laws are also proposed to estimate the uncalibrated parameters of the cameras and robot dynamics. The effectiveness of the proposed scheme is demonstrated through experimental results, and the convergence of the control scheme is rigorously proven using Lyapunov stability theory.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Agronomy
Renzhi Li et al.
Summary: In this study, a tomato maturity recognition model, YOLOv5s-tomato, is proposed based on improved YOLOv5 to recognize different tomato maturity stages. The model was enhanced using methods such as Mosaic data enhancement and CSPNet. The experiment results show that YOLOv5s-tomato effectively solves the problem of low recognition accuracy for occluded and small-target tomatoes, and meets the accuracy and speed requirements of tomato maturity recognition in greenhouses.
Article
Agronomy
Jialiang Zhou et al.
Summary: There is a high demand for dragon fruit in China and Southeast Asia, and manual picking requires a lot of labor. This paper proposes a dragon fruit detection method based on RDE-YOLOv7 to accurately identify and locate dragon fruits. Experimental results show that RDE-YOLOv7 improves precision, recall, and mean average precision, and has high accuracy for fruit detection under different lighting and blur conditions. The development of a dragon fruit picking system using RDE-YOLOv7 is supported by the accurate detection of dragon fruits in picking experiments, with minimal spatial positioning error.
Article
Agriculture, Multidisciplinary
Xing Wang et al.
Summary: Field robotic harvesting is a promising technique in agricultural industry. This study proposes a geometry-aware network, A3N, and a global-to-local scanning strategy to enable robots to accurately recognize and retrieve fruits in complex field environments.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Agriculture, Multidisciplinary
Shenglian Lu et al.
Summary: In this study, a near real-time method using a low-cost smartphone was proposed to accurately detect immature and mature apples in orchard environments. By adding an attention mechanism module and modifying the network structure, the proposed CA-YOLOv4 detector outperformed other algorithms and showed great potential for efficient real-world applications.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Forestry
Xueyan Zhu et al.
Summary: This study proposes a new deep learning model called EfficientNet-B4-CBAM for identifying oil tea cultivars. The experiment results show that the model achieves high accuracy and kappa coefficient in cultivar identification compared to other methods. This research provides new strategies and a theoretical basis for the application of deep learning technology in oil tea cultivar identification and supports the automatic identification and non-destructive testing of oil tea cultivars.
Article
Agronomy
Yajun Li et al.
Summary: This study aims to develop a method for locating laser strike points accurately in order to achieve pest elimination on leaves using laser power. By using specific camera equipment and algorithms to identify and locate the pests, the research achieved high precision and accuracy. This study provides important technical support for robotic pest control in vegetables.
Article
Agriculture, Multidisciplinary
Chunlin Chen et al.
Summary: This study developed a computer vision system for intelligent picking of tea buds using robotics and deep learning technologies. The system successfully recognizes and extracts the picking points of tea buds, achieving a picking rate of 80%. The results have the potential to contribute to the development of a machine-based tea picking system and precision agriculture.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Chemistry, Multidisciplinary
Delin Wu et al.
Summary: A Camellia oleifera fruit detection method based on YOLOv7 network and multiple data augmentation was proposed to detect Camellia oleifera fruit in complex field scenes. The method achieved high detection performance and strong generalisation ability.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Multidisciplinary
Yunhe Zhou et al.
Summary: This paper proposes a fusion method based on deep learning and image processing for target recognition and localization of Camellia oleifera fruits. The YOLOv7 model is optimized and selected to detect and locate the fruits, and image processing is used to extract the contour information. Experimental results show that the proposed method achieves accurate recognition and localization of Camellia oleifera fruits in complex environments.
APPLIED SCIENCES-BASEL
(2022)
Article
Agronomy
Anwen Liu et al.
Summary: In this paper, a machine vision and automatic control system for pineapple eye recognition and positioning is developed. Advanced target detection and 3D localization algorithms are utilized to accurately detect and locate pineapple eyes. The experimental results demonstrate high accuracy and fast detection, meeting the requirements of actual pineapple eye removal operations.
Article
Plant Sciences
Meili Sun et al.
Summary: This article presents an improved density peak cluster segmentation algorithm using the gradient field of depth images to locate and recognize green apples. With the analysis of the gradient field, the algorithm achieves accurate center location and segmentation using optimized density peak clustering. The contour of the target fruit is then obtained.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Agriculture, Multidisciplinary
Tao Yu et al.
Summary: Fruit detection and localization are crucial for horticulture and robotic harvesting in orchards. Existing studies have achieved good results in fruit detection based on 2D image analysis, but accurately detecting fruits on trees is still challenging due to various factors. This paper proposes a novel method for detecting and locating ripe pomegranates using an improved F-PointNet and 3D clustering method. Experimental results demonstrate the effectiveness of the proposed method.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Agricultural Engineering
Lianyi Yu et al.
Summary: This paper proposes a method for litchi recognition using an RGB-D camera, training a model with color and texture features, and improving fruit detection precision with multi-scale detection and non-maximum suppression algorithm, achieving high recognition accuracy in experiments.
BIOSYSTEMS ENGINEERING
(2021)
Article
Environmental Sciences
Bin Yan et al.
Summary: This study proposed an improved YOLOv5s algorithm for apple target recognition in apple picking robots. Experimental results show that the algorithm successfully differentiates between occluded graspable and ungraspable apples, with improved performance and speed.
Article
Agriculture, Multidisciplinary
Yatao Li et al.
Summary: The study developed a reliable algorithm using RGB-D camera images for detecting and locating tea shoots in a tea garden. The YOLO network was used to detect tea shoot regions on RGB images, and 3D point clouds were acquired to determine the plucking position, achieving successful robotic tea plucking.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Food Science & Technology
Kuang-Wen Hsieh et al.
Summary: This study aims to identify the maturity and position of greenhouse tomatoes through image capturing and object detection, achieving a precision and recall rate of over 95%. The research successfully compares the calculated 3D position of mature fruits with the actual measured position, as well as the size of the bounding box with the actual size of the fruits, with an R-squared value over 0.9.
JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION
(2021)
Article
Agriculture, Multidisciplinary
Mingyou Chen et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2020)
Review
Agriculture, Multidisciplinary
Longsheng Fu et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2020)
Article
Computer Science, Artificial Intelligence
Mahmood Sotoodeh et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2019)
Article
Environmental Sciences
Runzhi Wang et al.
Article
Chemistry, Analytical
Guichao Lin et al.
Article
Environmental Sciences
Dawei Li et al.
