Abstract This research was carried out partly in NIFTEM-K and IIT Gandhinagar in year 2022 and investigates the application of non-destructive Visible/ Near-Infrared spectroscopy for detecting internal defects in potatoes. Conventional methods followed for defect detection are largely destructive, leading to significant material waste and increased operational costs. The study is designed to provide an innovative, efficient, and economical approach of NIR technology combined with advanced data analysis techniques to detect and segregate the quality potatoes. Specifically, it employs machine learning algorithms such as Decision Tree Classifier (DT), Logistic Regression, K Nearest Neighbor (KNN), and a Deep Learning model, the Multi-Layered Perceptron Classifier to categorize potatoes based on internal defects. The NIR sensor (NIRvascan Smart Near Infrared Spectrometer Reflective Model G1) was used for detection. The spectrum was analyzed using specialized software Python 3.7 to be run in Jupyter Notebooks. A total of 600 potatoes were selected for the experiment. After performing 4 scans per potato we had a total of 2400 scans. 400 scans were discarded due to poor signal quality. 1200 were labeled as Black and the remaining 800 as Green. Dataset was divided into Train: Test and Validation sets. A ratio of 70:20:10 was selected for the split. The research identifies the Multi-Layered Perception Classifier as the most effective model, outperforming others in terms of F1 score and accuracy. The accuracy was found to be 81%. This breakthrough offers a scalable solution for enhancing potato quality assessment, with substantial implications for the agricultural industry and food supply chains.
Rafiq et al. (2025) studied this question.
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