Detecting Fruit Spoilage through Convolutional Neural Networks and Machine Learning Methods

Abstract

The detection of fruit spoilage is a critical aspect of quality control in the agricultural and food supply chain industries. Traditional methods for identifying spoiled fruits are often labor-intensive and subjective, leading to inconsistencies and inefficiencies. In recent years, the application of Convolutional Neural Networks (CNNs) and machine learning techniques has shown significant promise in automating and enhancing the accuracy of fruit spoilage detection. This paper presents a comprehensive study on the utilization of CNNs and machine learning algorithms to identify and classify spoiled fruits from fresh ones. Our proposed approach involves the collection and preprocessing of a diverse dataset of fruit images, including various stages of spoilage. The CNN model is trained to extract features and patterns associated with spoilage, while machine learning classifiers such as Support Vector Machines (SVM) and Random Forest are employed to further refine the detection process. Experimental results demonstrate high accuracy and robustness of the proposed method, outperforming traditional techniques. This study highlights the potential of integrating advanced computational methods into the agricultural sector, paving the way for more efficient and reliable quality control processes.