AccScience Publishing / DP / Online First / DOI: 10.36922/DP025230028
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ARTICLE

A novel approach of quality assessment of potatoes using artificial neural network

Zeeshan Ali Haq1 ,  Zainul Abdin Jaffery2* ,  Shabana Mehfuz2
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1 Department of Computer Science & Engineering, Jamia Hamdard, New Delhi, India
2 Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, India
DP, 025230028 https://doi.org/10.36922/DP025230028
Received: 3 June 2025 | Revised: 16 September 2025 | Accepted: 28 October 2025 | Published online: 17 November 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

A novel approach for quality assessment of potatoes using an artificial neural network (ANN) technique is developed and integrated with thermal imaging system to analyze the decay of tissues in potatoes. The merger of ANN with acquired thermal images leads to increased system efficiency, accuracy, reliability, and speed. A sample of 480 thermal images of potatoes was generated for training and testing the dataset. Categorization of potatoes as healthy or defective is performed based on seven critical parameters, such as standard deviation, energy, entropy, skewness, kurtosis, means, and variance. A classifier based on ANN is employed, incorporating two hidden layers with 200 neurons in the first layer and 50 neurons in the second layer. The input layer size is feature-dependent, while the output layer employs two neurons for binary classification. Among the seven features evaluated individually, entropy and energy achieved the best results, with classification accuracies of 79.81% and 81.27%, respectively. The highest overall performance, 94.89% accuracy with 455 correctly classified samples, was obtained by combining all features except variance. It is observed that integrating thermal imaging with ANNs is a promising approach for quality assessment. Selecting appropriate features is crucial for optimizing the classification model.

Keywords
Quality assessment of agricultural products
Artificial neural network
Smart assessment technique
Thermal imaging
Feature extraction
Image processing
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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