2022
DOI: 10.3390/agriculture13010078
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Yolov5s-CA: An Improved Yolov5 Based on the Attention Mechanism for Mummy Berry Disease Detection

Abstract: Early detection and accurately rating the level of plant diseases plays an important role in protecting crop quality and yield. The traditional method of mummy berry disease (causal agent: Monilinia vaccinii-corymbosi) identification is mainly based on field surveys by crop protection experts and experienced blueberry growers. Deep learning models could be a more effective approach, but their performance is highly dependent on the volume and quality of labeled data used for training so that the variance in vis… Show more

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Cited by 18 publications
(9 citation statements)
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“…This method can avoid the loss of feature information during downsampling, decrease the load of calculations, and enhance the speed of analysis of the algorithm. The SPP module, also known as spatial pyramid pooling, can convert input feature maps of different specifications into uniform size feature vectors;Neck uses the Feature Pyramid Networks(FPN)+Path Aggregation Network(PAN) feature pyramid structure [13,14], which combines feature information both from above to below and from below to above, so that features at multiple scales contain rich semantic information [15]; Head is a prediction network with prediction boxes of 20 20  , 40 40  , 80 80  and output sizes for predicting large, medium and small objects.…”
Section: Yolov5 Modelmentioning
confidence: 99%
“…This method can avoid the loss of feature information during downsampling, decrease the load of calculations, and enhance the speed of analysis of the algorithm. The SPP module, also known as spatial pyramid pooling, can convert input feature maps of different specifications into uniform size feature vectors;Neck uses the Feature Pyramid Networks(FPN)+Path Aggregation Network(PAN) feature pyramid structure [13,14], which combines feature information both from above to below and from below to above, so that features at multiple scales contain rich semantic information [15]; Head is a prediction network with prediction boxes of 20 20  , 40 40  , 80 80  and output sizes for predicting large, medium and small objects.…”
Section: Yolov5 Modelmentioning
confidence: 99%
“…The first method is used in many publications as it is a simple method for artificially expanding a small existing data set, with the aim to get better training results. Obsie, E. et al 7 use this method, for example, to identify plant diseases more robustly. An application of the first approach in a medical context can be found in the article by Badilla-Solorzano, J. et al 8 .…”
Section: Synthetic Data Generationmentioning
confidence: 99%
“…It can fuse both channel and spatial information to enhance the localization accuracy of object detection. Recently, the CA-based YOLOv5s model was used for mummy berry disease detection, which showed that the overall performance of the improved YOLOv5s-CA network model was superior to that of the original YOLOv5s model [56]. b) Split Attention (SA) SA, as a simple and unified computation block (Fig.…”
Section: ) Uav Remote Sensing Image Capture and Preprocessingmentioning
confidence: 99%