Recognition and Positioning of Strawberries Based on Improved YOLOv7 and RGB-D Sensing

Li, Yuwen; Wang, Wei; Guo, Xiaohuan; Wang, Xiaorong; Liu, Yizhe; Wang, Daren

doi:10.3390/agriculture14040624

Cited by 4 publications

(2 citation statements)

References 37 publications

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“…A number of studies related to positioning of fruit and vegetable picking were referenced [35,38]; this paper uses an Intel D435 camera for identification and localization of picking points. The camera has an RGB image sensor, two infrared receivers, and an infrared emitter, which can acquire RGB information and depth information simultaneously.…”

Section: Depth Camera-based Picking Point Localizationmentioning

confidence: 99%

“…Wang et al [34] introduced a method for pot flower detection and positioning using the ZED2 camera and YOLOv4-Tiny deep learning algorithm, achieving a maximum positioning error of 25.8 mm and an average accuracy of 89.72%. Li et al [35] proposed a strawberry picking-point positioning approach based on the YOLOv7 target detection algorithm and RGB-D perception, yielding an average positioning success rate of 90.8%. Furthermore, Hu et al [36] presented a method for accurate apple recognition and fast positioning utilizing an improved YOLOX and RGB-D image setup, achieving an average accuracy of 94.09% with a maximum positioning error of less than 7 mm.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Feature Fusion Recognition and Localization Method for Unmanned Harvesting of Aquatic Vegetables

Guan,

Shi,

Yang

et al. 2024

Agriculture

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The vision-based recognition and localization system plays a crucial role in the unmanned harvesting of aquatic vegetables. After field investigation, factors such as illumination, shading, and computational cost have become the main difficulties restricting the identification and positioning of Brasenia schreberi. Therefore, this paper proposes a new lightweight detection method, YOLO-GS, which integrates feature information from both RGB and depth images for recognition and localization tasks. YOLO-GS employs the Ghost convolution module as a replacement for traditional convolution and innovatively introduces the C3-GS, a cross-stage module, to effectively reduce parameters and computational costs. With the redesigned detection head structure, its feature extraction capability in complex environments has been significantly enhanced. Moreover, the model utilizes Focal EIoU as the regression loss function to mitigate the adverse effects of low-quality samples on gradients. We have developed a data set of Brasenia schreberi that covers various complex scenarios, comprising a total of 1500 images. The YOLO-GS model, trained on this dataset, achieves an average accuracy of 95.7%. The model size is 7.95 MB, with 3.75 M parameters and a 9.5 GFLOPS computational cost. Compared to the original YOLOv5s model, YOLO-GS improves recognition accuracy by 2.8%, reduces the model size and parameter number by 43.6% and 46.5%, and offers a 39.9% reduction in computational requirements. Furthermore, the positioning errors of picking points are less than 5.01 mm in the X direction, 3.65 mm in the Y direction, and 1.79 mm in the Z direction. As a result, YOLO-GS not only excels with high recognition accuracy but also exhibits low computational demands, enabling precise target identification and localization in complex environments so as to meet the requirements of real-time harvesting tasks.

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Section: Depth Camera-based Picking Point Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Feature Fusion Recognition and Localization Method for Unmanned Harvesting of Aquatic Vegetables

Guan,

Shi,

Yang

et al. 2024

Agriculture

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YOLOv10-pose and YOLOv9-pose: Real-time strawberry stalk pose detection models

Meng,

Du,

Sapkota

et al. 2025

Computers in Industry

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Study on the fusion of improved YOLOv8 and depth camera for bunch tomato stem picking point recognition and localization

Song,

Wang,

et al. 2024

Front. Plant Sci.

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When harvesting bunch tomatoes, accurately identifying certain fruiting stems proves challenging due to their obstruction by branches and leaves, or their similarity in colour to the branches, main vines, and lateral vines. Additionally, irregularities in the growth pattern of the fruiting pedicels further complicate precise picking point localization, thus impacting harvesting efficiency. Moreover, the fruit stalks being too short or slender poses an obstacle, rendering it impossible for the depth camera to accurately obtain depth information during depth value acquisition. To address these challenges, this paper proposes an enhanced YOLOv8 model integrated with a depth camera for string tomato fruit stalk picking point identification and localization research. Initially, the Fasternet bottleneck in YOLOv8 is replaced with the c2f bottleneck, and the MLCA attention mechanism is added after the backbone network to construct the FastMLCA-YOLOv8 model for fruit stalk recognition. Subsequently, the optimized K-means algorithm, utilizing K-means++ for clustering centre initialization and determining the optimal number of clusters via Silhouette coefficients, is employed to segment the fruit stalk region. Following this, the corrosion operation and Zhang refinement algorithm are used to denoise the segmented fruit stalk region and extract the refined skeletal line, thereby determining the coordinate position of the fruit stalk picking point in the binarized image. Finally, the issue of missing depth values of fruit stalks is addressed by the secondary extraction method to obtain the depth values and 3D coordinate information of the picking points in RGB-D camera coordinates. The experimental results demonstrate that the algorithm accurately identifies and locates the picking points of string tomatoes under complex background conditions, with the identification success rate of the picking points reaching 91.3%. Compared with the YOLOv8 model, the accuracy is improved by 2.8%, and the error of the depth value of the picking points is only ±2.5 mm. This research meets the needs of string tomato picking robots in fruit stalk target detection and provides strong support for the development of string tomato picking technology.

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Recognition and Positioning of Strawberries Based on Improved YOLOv7 and RGB-D Sensing

Cited by 4 publications

References 37 publications

Multi-Feature Fusion Recognition and Localization Method for Unmanned Harvesting of Aquatic Vegetables

Multi-Feature Fusion Recognition and Localization Method for Unmanned Harvesting of Aquatic Vegetables

YOLOv10-pose and YOLOv9-pose: Real-time strawberry stalk pose detection models

Study on the fusion of improved YOLOv8 and depth camera for bunch tomato stem picking point recognition and localization

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