Convolutional Neural Network Based Quality Analysis of Fruits and Vegetables

Abstract:

In the recent era- very frequently people come across health issues due to consumption of poor-quality food items- which leads to issues such as food poisoning, vomiting, diarrhea, etc., For a full development of fruits and vegetables, all the nutrients are necessary during its growth. But due to circumstances like soil defects, infections, water scarcity, waterlogging, etc., the vegetables & fruits gets infected with some diseases. So there arises a necessity of a system which inspects for any presence of disease in fruits & vegetables, with reduced manual intervention. This paper provides a detailed overview of a system developed using the Python programming language. Its aim is to recognize and classify various fruits and vegetables, while also identifying any diseases affecting them and determining the specific type of infection. In order to recognize the details accurately, the system is designed to use convolutional neural networks (CNN) and the results are displayed using computer vision techniques. The analysis, implementation, and future improvements of the proposed system are briefed in this paper. For this, we have used Anaconda navigator software (Jupyter notebook, IDLE).

• Periodical: Applied Mechanics and Materials (Volume 926)
• Pages: 151-162
• DOI: https://doi.org/10.4028/p-rT5gTD
• Online since: April 2025
• Authors: G. Sekar*, Benson Mansingh, Joghee Prasad, R. Nallakumar
• Keywords: Computer Vision, Convolution Neural Network, Deep Learning, Disease Identification, Feature Extraction, Food Quality Assessment, Food Recognition, Python, Segmentation

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