Tomato ripening detection in natural environment based on improved YOLOv5s
[Objective]In the tomato recognition task,the existing target recognition algorithms are slow and have low accuracy in recognizing occluded tomatoes and small tomatoes,which affects their deployment and application on embedded devices.In order to realize the fast and accurate recognition of tomato fruits by agricultural robots in complex environments,this study proposes a tomato ripeness recognition method based on the improved YOLOv5s model.[Method]Combined with the distribution characteristics of tomato growing environment,MobileNetv3 network and ECANet(Efficient Channel Attention Network)mechanism were introduced to improve the YOLOv5s target detection algorithm.[Result]Compared with YOLOv5,the results of the improved YOLOv5s(Im-YOLOv5s)showed that the accuracy,recall,and average accuracy were improved by 3.4%,2.4%,and 2.3%,the weight size was reduced by 48.6%,and the detection speed was increased by 52.9%,which improved the detection performance and shortened the model inference time.[Conclusion]Compared with a variety of mainstream target detection models,the improved YOLOv5s algorithm greatly reduces the omission and false detection of tomato ripeness.With better recognition effect,good robustness and real-time performance,it meets the demand for accurate real-time identification of tomatoes with different ripeness,and it is suitable for the deployment and application on embedded devices,thus providing technical support for automated tomato picking.
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