A Development of Deep Learning-Based Lemon Quality Detection through Peel Analysis

Abstract

This paper addresses the critical challenge of lemon quality detection by introducing a novel deep learning system that analyzes lemon peel characteristics for precise quality assessment. With the rising global demand for high-quality produce, the agricultural industry requires more reliable and efficient methods for evaluating fruit quality. This study presents a convolutional neural network (CNN) trained on a comprehensive dataset of lemon images sourced from the Kaggle platform, enabling the classification of lemons into high or low quality based on specific peel attributes. Visualization tools have been integrated to enhance the interpretability and practical application of the system, allowing users to understand better the model's output and the factors influencing quality classification. Utilizing the waterfall model within the software development life cycle (SDLC), the research encompasses essential phases such as data preprocessing, model development, graphical user interface (GUI) implementation, system functionality testing, and a thorough evaluation of the system's performance. The results indicate that the proposed system significantly enhances the accuracy and reliability of lemon quality detection, addressing the limitations of existing methods, which often lack precision and consistency. This innovation represents a significant advancement in agricultural technology, with potential applications extending beyond lemons to other crops requiring stringent quality control. This research provides valuable insights by integrating deep learning methodologies and visualization tools into the farming sector. It lays the groundwork for future developments to refine and expand these techniques. The paper concludes by discussing the system's advantages and limitations, offering a roadmap for future research that could lead to even more sophisticated approaches to agricultural quality assessment.