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

2026-02-11

AI Summary

This study addresses the significant losses in quality and yield of guava caused by anthracnose and fruit fly infestations by proposing a deep learning–based method for early and precise disease identification. A three-class disease classification dataset comprising 3,784 images was constructed from the Mendeley dataset. The approach leverages InceptionV3 and ResNet50 architectures, enhanced with CutMix and MixUp data augmentation strategies to improve model robustness. SHAP (SHapley Additive exPlanations) interpretability analysis was employed to identify the image regions most influential to model predictions. Experimental results demonstrate that the InceptionV3 model achieves an accuracy of 98.15%, substantially outperforming ResNet50 (94.46%). The high performance and reliability of the model are further corroborated through confusion matrix analysis and SHAP-based visual explanations.

Technology Category

Machine Learning: Multi-class/Multi-label Learning & Extreme ClassificationComputer Vision: Generative Adversarial Networks (GANs) for VisionNatural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasetsWeb Mining and Content Analysis: Large pretrained models with web dataGraph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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

Problem

• Guava disease
• disease classification
• early identification
• Anthracnose
• Fruit flies

Innovation

• InceptionV3
• Data Augmentation
• SHAP Interpretability
• CutMix
• Guava Disease Classification