Hemerocallis citrina Baroni is rich in nutritional value,with a clear trend of increasing market demand,and it is a pillar industry for rural economic development.Hemerocallis citrina Baroni exhibits rapid growth,a sh...Hemerocallis citrina Baroni is rich in nutritional value,with a clear trend of increasing market demand,and it is a pillar industry for rural economic development.Hemerocallis citrina Baroni exhibits rapid growth,a shortened harvest cycle,lacks a consistent maturity identification standard,and relies heavily on manual labor.To address these issues,a new method for detecting the maturity of Hemerocallis citrina Baroni,called LTCB YOLOv7,has been introduced.To begin with,the layer aggregation network and transition module are made more efficient through the incorporation of Ghost convolution,a lightweight technique that streamlines the model architecture.This results in a reduction of model parameters and computational workload.Second,a coordinate attention mechanism is enhanced between the feature extraction and feature fusion networks,which enhances the model precision and compensates for the performance decline caused by lightweight design.Ultimately,a bi-directional feature pyramid network with weighted connections replaces the Concatenate function in the feature fusion network.This modification enables the integration of information across different stages,resulting in a gradual improvement in the overall model performance.The experimental results show that the improved LTCB YOLOv7 algorithm for Hemerocallis citrina Baroni maturity detection reduces the number of model parameters and floating point operations by about 1.7 million and 7.3G,respectively,and the model volume is compressed by about 3.5M.This refinement leads to enhancements in precision and recall by approximately 0.58%and 0.18%respectively,while the average precision metrics mAP@0.5 and mAP@0.5:0.95 show improvements of about 1.61%and 0.82%respectively.Furthermore,the algorithm achieves a real-time detection performance of 96.15 FPS.The proposed LTCB YOLOv7 algorithm exhibits strong performance in detecting maturity in Hemerocallis citrina Baroni,effectively addressing the challenge of balancing model complexity and performance.It also establishes a standardized approach for maturity detection in Hemerocallis citrina Baroni for identification and harvesting purposes.展开更多
In greenhouse environments,using automated machines for tomato harvesting to reduce labor consumption is a future development trend.Accurate and effective visual recognition is essential to accomplish harvesting tasks...In greenhouse environments,using automated machines for tomato harvesting to reduce labor consumption is a future development trend.Accurate and effective visual recognition is essential to accomplish harvesting tasks.However,most current studies use various models to gain harvesting information in multiple steps,resulting in heavy calculation costs,poor real-time availability,and weak recognition precision.In this study,an improved YOLOv8np-RCW end-to-end model based on YOLOv8n pose is proposed to simultaneously detect tomato bunches,maturity,and keypoints using a decoupled-head structure.The model integrates a ResNet-enhanced RepVGG architecture for a balance of accuracy and speed,employs the CARAFE upsampling algorithm for a larger receptive field with lightweight design,and optimizes the loss function with WIoU loss to enhance bounding box prediction,maturity detection,and keypoint extraction.Experimental results indicate that mAP50 of YOLOv8np-RCW model for bounding box and keypoints is 87.3%and 86.8%respectively,which is 6.2%and 5.5%higher than YOLOv8n pose model.Completing the tasks of bunch detection,maturity assessment,and keypoint localization requires only 9.8 ms.Euclidean distance error is less than 20 pixels in detecting keypoints.Based on this model,a method is proposed to quickly determine the orientation of tomato bunches using geometric cross-product and cross-multiplication calculations from keypoint 2D information,providing guidance for the motion planning of the end-effector.In field experiments,the robot achieved a harvesting success rate of 68%,with an average time of 10.8366 seconds per tomato bunch.展开更多
基金funded by the Shanxi Provincial Science and Technology Department Surface Project(Grant No.202303021211330)Innovation Platform Project of Science and Technology Innovation Program of Higher Education Institutions in Shanxi Province(Grant No.2022P009)+2 种基金Shanxi Province Basic Research Program Projects(Grant No.202303021212244)the Datong City Shanxi Province Key Research&Development(Agriculture)Program Projects(Grants No.2023006,2023015)the 2024 Basic Research Program of Shanxi Province(Free Exploration Category)Program Projects(Grant No.202403021221181).
文摘Hemerocallis citrina Baroni is rich in nutritional value,with a clear trend of increasing market demand,and it is a pillar industry for rural economic development.Hemerocallis citrina Baroni exhibits rapid growth,a shortened harvest cycle,lacks a consistent maturity identification standard,and relies heavily on manual labor.To address these issues,a new method for detecting the maturity of Hemerocallis citrina Baroni,called LTCB YOLOv7,has been introduced.To begin with,the layer aggregation network and transition module are made more efficient through the incorporation of Ghost convolution,a lightweight technique that streamlines the model architecture.This results in a reduction of model parameters and computational workload.Second,a coordinate attention mechanism is enhanced between the feature extraction and feature fusion networks,which enhances the model precision and compensates for the performance decline caused by lightweight design.Ultimately,a bi-directional feature pyramid network with weighted connections replaces the Concatenate function in the feature fusion network.This modification enables the integration of information across different stages,resulting in a gradual improvement in the overall model performance.The experimental results show that the improved LTCB YOLOv7 algorithm for Hemerocallis citrina Baroni maturity detection reduces the number of model parameters and floating point operations by about 1.7 million and 7.3G,respectively,and the model volume is compressed by about 3.5M.This refinement leads to enhancements in precision and recall by approximately 0.58%and 0.18%respectively,while the average precision metrics mAP@0.5 and mAP@0.5:0.95 show improvements of about 1.61%and 0.82%respectively.Furthermore,the algorithm achieves a real-time detection performance of 96.15 FPS.The proposed LTCB YOLOv7 algorithm exhibits strong performance in detecting maturity in Hemerocallis citrina Baroni,effectively addressing the challenge of balancing model complexity and performance.It also establishes a standardized approach for maturity detection in Hemerocallis citrina Baroni for identification and harvesting purposes.
基金support from the National Key Research and Development Program of China(Grant No.2022YFD2000500).
文摘In greenhouse environments,using automated machines for tomato harvesting to reduce labor consumption is a future development trend.Accurate and effective visual recognition is essential to accomplish harvesting tasks.However,most current studies use various models to gain harvesting information in multiple steps,resulting in heavy calculation costs,poor real-time availability,and weak recognition precision.In this study,an improved YOLOv8np-RCW end-to-end model based on YOLOv8n pose is proposed to simultaneously detect tomato bunches,maturity,and keypoints using a decoupled-head structure.The model integrates a ResNet-enhanced RepVGG architecture for a balance of accuracy and speed,employs the CARAFE upsampling algorithm for a larger receptive field with lightweight design,and optimizes the loss function with WIoU loss to enhance bounding box prediction,maturity detection,and keypoint extraction.Experimental results indicate that mAP50 of YOLOv8np-RCW model for bounding box and keypoints is 87.3%and 86.8%respectively,which is 6.2%and 5.5%higher than YOLOv8n pose model.Completing the tasks of bunch detection,maturity assessment,and keypoint localization requires only 9.8 ms.Euclidean distance error is less than 20 pixels in detecting keypoints.Based on this model,a method is proposed to quickly determine the orientation of tomato bunches using geometric cross-product and cross-multiplication calculations from keypoint 2D information,providing guidance for the motion planning of the end-effector.In field experiments,the robot achieved a harvesting success rate of 68%,with an average time of 10.8366 seconds per tomato bunch.
