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apple leaf disease identification using genetic algorithm and correlation based feature selection method

International Journal of Agricultural and Biological Engineering 2017年第2期10卷 74-83页

作者： Zhang Chuanlei Zhang Shanwen Yang Jucheng Shi Yancui Chen Jia College of Computer Science and Information Engineering Tianjin University of Science and TechnologyTianjin 300222China Xijing University Xi’an710123China

apple leaf disease is one of the main factors to constrain the apple production and *** takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best time to prevent and treat the *** leaf disease recognition based on leaf image is an essential research topic in the field of computer vision,where the key task is to find an effective way to represent the diseased leaf *** this research,based on image processing techniques and pattern recognition methods,an apple leaf disease recognition method was proposed.A color transformation structure for the input RGB(Red,Green and Blue)image was designed firstly and then RGB model was converted to HSI(Hue,Saturation and Intensity),YUV and gray *** background was removed based on a specific threshold value,and then the disease spot image was segmented with region growing algorithm(RGA).Thirty-eight classifying features of color,texture and shape were extracted from each spot *** reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification,the most valuable features were selected by combining genetic algorithm(GA)and correlation based feature selection(CFS).Finally,the diseases were recognized by SVM *** the proposed method,the selected feature subset was globally *** experimental results of more than 90%correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases,powdery mildew,mosaic and rust,demonstrate that the proposed method is feasible and effective.

关键词： apple leaf disease diseased leaf recognition region growing algorithm(RGA) genetic algorithm and correlation based feature selection(GA-CFS)

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Improved multi-scale inverse bottleneck residual network based on triplet parallel attention for apple leaf disease identification

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Journal of Integrative Agriculture 2024年第3期23卷 901-922页

作者： Lei Tang Jizheng Yi Xiaoyao Li College of Computer&Information Engineering Central South University of Forestry and TechnologyChangsha 410004China Yuelushan Laboratory Carbon Sinks Forests Variety Innovation Center Changsha 410000China

Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf(including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recogniti

关键词： multi-scale module inverse bottleneck structure triplet parallel attention apple leaf disease

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