Healthy Harvests: A Comparative Look at Guava Disease Classification Using InceptionV3

TL;DR

This study compares InceptionV3 and ResNet50 deep learning models for classifying guava diseases, achieving high accuracy and interpretability using data augmentation, mixing techniques, and SHAP analysis.

Contribution

It demonstrates the effectiveness of InceptionV3 for guava disease classification and integrates interpretability methods like SHAP for better understanding of model decisions.

Findings

InceptionV3 achieved 98.15% accuracy.

ResNet50 achieved 94.46% accuracy.

Data augmentation and mixing improved model robustness.

Abstract

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