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

翻译：番石榴果实常受多种病害影响，这会损害果实品质与作物产量。早期识别对于减少损失和保障果实健康至关重要。本研究聚焦于三类病害分类：炭疽病、果蝇侵害及健康果实。所用数据集采集自Mendeley Data平台，包含473张原始番石榴图像，其尺寸与格式各异。为提升一致性，原始数据集被统一调整为256×256像素的RGB色彩模式。随后通过数据增强技术对原始图像生成变体以优化数据集，经先进预处理方法得到的增强数据集共包含3784张图像。本研究采用两种深度学习模型进行图像分类：以其先进架构闻名的InceptionV3模型能有效运用多重卷积滤波器提取差异化特征；而ResNet50模型则通过残差学习实现更深层网络的训练。InceptionV3模型取得了98.15%的优异准确率，ResNet50模型准确率为94.46%。研究应用CutMix与MixUp等数据混合方法以增强模型鲁棒性，并采用混淆矩阵评估两种模型的整体性能。此外，通过SHAP分析提升模型可解释性，有助于识别影响模型预测的关键图像区域。本研究旨在揭示先进模型如何提升农业病害诊断效能。