基于改進(jìn)YOLOv8n的草莓葉片病害檢測(cè)方法
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Strawberry leaf disease detection method based on improved YOLOv8n
XIA Shunxing， NI Ming， LUO Youlu， HE Yinghao， ZHAO Taotao（Collegeof InformationEngineering，SichuanAgricultural University，Ya'an625O14，China）
Abstract：Inordertoimprovethedetectionabilityof targetdetectionmodelsforstrawberryleaf diseasesinorchard environment，thisstudyusedtheYOLOv8nmodelasthebaseline model，introducedthedynamicconvolution module toreplacethethirdconvolution layerof the backbone network and partoftheC2fmoduleof theneck network，introduced the GSConv and Slim-neck module toreplacetheconvolution layerand partof the C2f moduleof the neck network，and introduced the content-aware reassemblyof features （CARAFE）operator to replacethe nearestneighbor interpolation method in upsampling.An improved YOLOv8n model named YOLOv8n-DGC was proposed to improve thedetection accuracy of strawberyleaf diseases while maintaining the lightweightofthe model.Theresults showed that the meanaverage precisionwhen the intersection over union（ IoU ）threshold was 0.50 （ mAP50 ），the mean average precision when the IoU was between （20 0.50 and 0.95 （ mAP50：95 ），precision and recall rate of the improved model YOLOv8n-DGC for strawberry leaf disease detection were 2.5percentage points，1.5percentage points，.6percentage pointsand1.6percentagepoints higher thanthose of the baseline model，respectively. The model size and parameter quantity increased by 3.2% and 3.3% ，respectively， while the number of floating point of operations decreased by 8.6% . Compared with models such as Faster R-CNN，SSD， YOLOv5s，and YOLOv7-tiny，the YOLOv8n-DGC model beter achieved a balance between detectionaccuracyand ffi
Keywords：strawberry；leaf diseases；object detection；YOLOv8n；dynamic convolution;GSConv；CARAFE
草莓屬薔薇科漿果類(lèi)多年生草本植物，素有“水果皇后”的美稱(chēng)。（剩余15206字）