用InceptionV3识别木瓜病害，准确率达98.15%
Healthy Harvests: A Comparative Look at Guava Disease Classification Using InceptionV3
- 基于InceptionV3和ResNet50模型，结合数据增强提升分类性能
- 模型在473张原始图像上训练，经扩增后达3784张，准确率最高98.15%
- 使用SHAP分析解释模型决策，适合农业病害检测研究者参考
木瓜果实常受多种病害影响，损害果实品质与产量。早期识别对减少损失、保障果实健康至关重要。本研究聚焦三种病害类别：炭疽病、果蝇侵害及健康果实。实验数据来自Mendeley Data，包含473张原始木瓜图像，尺寸与格式各异。图像统一处理为256x256像素的RGB格式，并通过数据增强生成3784张新图像以提升数据多样性。采用InceptionV3与ResNet50两个深度学习模型进行分类。InceptionV3在特征提取方面表现优异，实现98.15%的准确率；ResNet50则达到94.46%。引入CutMix与MixUp等数据混合策略以增强模型鲁棒性。通过混淆矩阵评估整体性能，并使用SHAP分析提升模型可解释性，识别出影响预测的关键图像区域。
原文摘要 · Abstract (English)
Guava fruits often suffer from many diseases. This can harm fruit quality and fruit crop yield. Early identification is important for minimizing damage and ensuring fruit health. This study focuses on 3 different categories for classifying diseases. These are Anthracnose, Fruit flies, and Healthy fruit. The data set used in this study is collected from Mendeley Data. This dataset contains 473 original images of Guava. These images vary in size and format. The original dataset was resized to 256x256 pixels with RGB color mode for better consistency. After this, the Data augmentation process is applied to improve the dataset by generating variations of the original images. The augmented dataset consists of 3784 images using advanced preprocessing techniques. Two deep learning models were implemented to classify the images. The InceptionV3 model is well known for its advanced framework. These apply multiple convolutional filters for obtaining different features effectively. On the other hand, the ResNet50 model helps to train deeper networks by using residual learning. The InceptionV3 model achieved the impressive accuracy of 98.15%, and ResNet50got 94.46% accuracy. Data mixing methods such as CutMix and MixUp were applied to enhance the model's robustness. The confusion matrix was used to evaluate the overall model performance of both InceptionV3 and Resnet50. Additionally, SHAP analysis is used to improve interpretability, which helps to find the significant parts of the image for the model prediction. This study purposes to highlight how advanced models enhan
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