Multi-Fruit Classification and Grading Using a Same-Domain Transfer Learning Approach

Aim:

       To develop an advanced fruit classification and grading system using deep learning models (EfficientNetV2-B3, ResNet152V2, and ResNet50V2) for comparative analysis and to implement an alert mechanism for detecting bad-quality fruits.

Abstract:

         Fruit classification and grading play a crucial role in agricultural automation, ensuring quality control and efficient sorting. The existing system primarily uses EfficientNetV2, which achieves high accuracy but lacks a comparative study with other deep learning models. In this study, we propose a novel approach evaluating EfficientNetV2-B3, ResNet152V2, and ResNet50V2 to analyze their performance in fruit classification and grading. Additionally, we introduce an alert system that notifies users when bad-quality fruits are detected. The proposed method leverages transfer learning and data augmentation techniques to improve performance. Experimental results show that our approach provides valuable insights into model performance, making it a promising solution for real-world applications.

Introduction:

      Fruits are essential for human nutrition, and their quality significantly impacts consumer health and market value. Traditional manual grading methods are subjective, time-consuming, and prone to errors. Automated fruit classification and grading using deep learning provide a scalable solution. However, existing methods often focus on a single model, limiting the scope of comparative analysis. Our study aims to compare multiple models and integrate an alert system for detecting substandard fruits.

 Problem Definition:

        The existing fruit classification systems are primarily reliant on a single deep learning model, which limits their ability to generalize across diverse datasets. This often leads to inconsistencies in quality grading, as there is no comparative analysis to benchmark performance across different architectures. Furthermore, the absence of an automated alert system for identifying and notifying users about bad-quality fruits creates a significant gap in agricultural automation. To ensure improved accuracy and robustness, a more sophisticated approach integrating multiple models must be adopted to enable better classification and grading mechanisms.

Existing System:

    Currently, the fruit classification and grading system predominantly utilizes EfficientNetV2, a deep learning model that has demonstrated high classification accuracy. Transfer learning is applied to enhance its performance, enabling faster convergence and better feature extraction. However, the system does not compare its performance with other state-of-the-art models, making it difficult to determine its true effectiveness. Additionally, the current framework lacks a real-time alert mechanism to notify users when bad-quality fruits are detected. This limitation reduces the system’s applicability in practical scenarios where instant decision-making is crucial..

Disadvantages:

       One of the major drawbacks of the existing system is its dependency on a single model, which restricts adaptability and generalization when dealing with diverse fruit datasets. Furthermore, dataset imbalance can lead to biased model predictions, reducing classification accuracy for certain fruit categories. Without comparative evaluation against other models, it is challenging to assess the robustness of the current approach. Additionally, the lack of an alert system for poor-quality fruit classification prevents immediate corrective actions, leading to inefficiencies in the fruit sorting and grading process.

 Proposed System:

     To overcome these limitations, we propose a comparative analysis framework that evaluates multiple deep learning models, including EfficientNetV2-B3, ResNet152V2, and ResNet50V2, to determine the most effective approach for fruit classification and grading. This system integrates an automated alert mechanism to notify users when bad-quality fruits are detected, improving real-time decision-making. By leveraging advanced data augmentation techniques such as CutMix, MixUp, and AugMix, the proposed approach enhances model generalization and robustness. Furthermore, the comparative analysis will provide insights into the strengths and weaknesses of each model, enabling better selection for real-world applications.

Advantages:

       The proposed system offers a comprehensive evaluation of multiple deep learning models, leading to better accuracy and reliability in fruit classification and grading. By incorporating transfer learning, the models achieve faster convergence and improved feature extraction, optimizing classification performance. The introduction of an automated alert system enhances real-time monitoring, enabling timely decision-making and reducing the chances of distributing low-quality fruits. Additionally, the use of data augmentation techniques ensures model robustness by mitigating dataset imbalance issues. This system presents a scalable and practical solution that can be integrated into real-world agricultural automation applications.