基于MECB-DeepLabV3+的梨树叶片病害分级研究

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中图分类号：S436.612：S126 文献标识号：A 文章编号：1001-4942（2025）06-0138-11

AbstractThe accurate grading of pear leaf diseases is of great significance to effective disease control and improvement of pear fruit yield and quality. Based on pear black spot disease，brown spot disease，gray spot disease and healthy leaves，this study proposed a lightweight image segmentation model for pear leaf diseases called MECB-DeepLabV3+. Firstly，the transfer learning method was used to compare and analyze the U-Net，PSPNet and DeepLabV3+ networks，and the DeepLabV3+ with the best comprehensive performance was selected as the basic network model.Secondly，to addressthe issues of large parameter size and high computational complexity in DeepLabV3+，MobileNetV3 was chosen as the backbone network to achieve model lightweighting.Finally，to overcome the shortcomings of DeepLabV3+ in recognizing fine lesions and boundary segmentation，efficient chanel attention （ECA），coordinate attention（CA），and bottleneck attention module （BAM） were introduced. Additionally，the Ranger21 optimizer and a composite loss function were used to optimize model training.The experimental results showed that the mean intersection over union and mean pixel accuracy of the proposed model reached 91.22% and 95.01% ，respectively，representing an improvement of 2.91 and 2.14 percentage points compared to the basic network. The parameter size of the proposed model was only 4.221 M. By using the proportion of disease spot area for disease grading，the overall average accuracy reached 95.02% ，enabling effective disease grading，which could provide the scientific reference for controlling pear leaf diseases.

KeywordsPear leaf diseases ; Disease grading； Deep learning; Semantic segmentation; Transfer learning

梨树是我国重要的经济果树之一，栽培历史悠久，河北、新疆、河南、辽宁、安徽和等省份广泛种植。（剩余12598字）