Enhancing Mango Fruit Disease Severity Assessment with CNN and SVM-Based Classification

D. Banerjee, V. Kukreja, S. Hariharan, Vishal Jain
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引用次数: 1

Abstract

The mango leaf powdery mildew disease poses a serious threat to mango production society globally by significantly lowering yield and quality. For timely intervention and efficient management, early disease detection and classification are important. In this research and education area, a novel hybrid approach utilizes Convolutional Neural Networks (CNN) and Support Vector Machines to identify the mango leaf powdery mildew disease based on four severity levels (SVM). Three phases make up the proposed approach: data structure, CNN-selected attributes, and SVM classification. We collect and preprocess images of mango leaves during the data organization step, and in the CNN - attributes selection phase, we apply a CNN model for feature extraction and selection. For the mango leaf powdery mildew dataset, we improve the CNN model to find the most relevant features for the classification task. The SVM - classification step includes training an SVM model on the obtained features and refining the hyperparameters via k-fold cross-validation. The proposed CNN and SVM hybrid multi-classification model for mango leaf powdery mildew disease achieved an overall accuracy of 89.29%. A dataset of 2559 images with 4 severity levels was utilized. The model works well overall, as a macro-average F1-score of 90.10, the weighted average F1-score's minimal value of 53.85%. The model is less successful in predicting instances for classes with smaller support proportions, as shown by the micro-average F1-score, which is 89.29% and is lower overall than the macro-average F1-score.
基于CNN和svm分类增强芒果果实病害严重程度评估
芒果叶白粉病严重影响芒果产量和品质,对全球芒果生产社会造成严重威胁。为了及时干预和有效管理,疾病的早期发现和分类是重要的。在这个研究和教育领域,一种新的混合方法利用卷积神经网络(CNN)和支持向量机(SVM)来识别芒果叶白粉病,基于四个严重程度(SVM)。该方法由三个阶段组成:数据结构、cnn选择属性和SVM分类。在数据组织阶段,我们采集芒果叶图像并进行预处理,在CNN -属性选择阶段,我们采用CNN模型进行特征提取和选择。对于芒果叶白粉病数据集,我们改进CNN模型,为分类任务找到最相关的特征。支持向量机分类步骤包括在得到的特征上训练支持向量机模型,并通过k-fold交叉验证来细化超参数。提出的芒果叶片白粉病CNN和SVM混合多分类模型总体准确率达到89.29%。使用了一个包含2559张图像的数据集,其中包含4个严重级别。模型总体效果良好,宏观平均f1得分为90.10,加权平均f1得分最小值为53.85%。该模型在预测支持比例较小的类的实例时不太成功,微观平均f1得分为89.29%,总体上低于宏观平均f1得分。
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