健康收获：使用InceptionV3对番石榴疾病分类的比较分析
Healthy Harvests: A Comparative Look at Guava Disease Classification Using InceptionV3
AI总结：
本研究使用InceptionV3和ResNet50模型对番石榴疾病进行分类，通过数据增强和混合技术提升模型性能，最终实现98.15%的准确率。
摘要：
番石榴果实常常受到多种疾病的侵害。这会损害果实质量和产量。早期识别对于减少损害并确保果实健康至关重要。本研究专注于三种分类：炭疽病、果蝇和健康果实。所用数据集来自Mendeley Data。该数据集包含473张原始图像，尺寸和格式各异。原始数据集被调整为256x256像素，使用RGB颜色模式以提高一致性。之后，应用数据增强过程来改进数据集，通过生成原始图像的变体来提高数据集质量。增强后的数据集包含3784张图像，使用先进的预处理技术。实现了两种深度学习模型来分类图像。InceptionV3模型因其先进的框架而闻名，它通过使用多个卷积滤波器有效地获取不同特征。另一方面，ResNet50模型通过使用残差学习来训练更深层的网络。InceptionV3模型实现了98.15%的准确率，ResNet50模型实现了94.46%的准确率。数据混合方法如CutMix和MixUp被应用于增强模型的鲁棒性。混淆矩阵被用来评估InceptionV3和ResNet50两个模型的整体性能。此外，SHAP分析用于提高可解释性，帮助找到对模型预测具有重要意义的图像部分。本研究旨在突出先进模型如何增强分类效果。
英文摘要：
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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