OICC Press International Journal of Nano Dimension 2228-5059 16 4 2025 05 17 Feature-driven fruit classification using leveraging machine learning algorithms and nano semiconductor sensors 10.57647/j.ijnd.2025.1604.26 EN Venkateswarlu Gundu Koneru Lakshmaiah Education Foundation Vaddeswaram, Guntur, AP, India Krishna Kumba Vellore Institute of Technology Chennai, Vandalur, Tamil Nadu, India https://orcid.org/0000-0003-1577-2516 Sishaj P Simon National Institute of Technology Tiruchirappalli, Tamil Nadu, India Parusharamulu Buduma Lendi Institute of Engineering and Technology, Vizianagaram, AP, India https://orcid.org/0000-0001-5740-6472 Mithu Sarkar Vellore Institute of Technology Chennai, Vandalur, Tamil Nadu, India Journal Article 2025 05 17 The proposed work compares machine learning algorithms for fruit classification using apples, mandarins, oranges, and lemons. The goal is to identify the most accurate and precision-score algorithm. To assemble a robust dataset, we purchased several dozen oranges, lemons, and apples of various subtypes and meticulously recorded their mass, width, height, and color score using nano semiconductor sensors. Using this recorded data statistical analysis is conducted for identifying the accurate fruit classification machine learning method. The accurate prediction rate is determined by subtracting the actual and anticipated values. K-Nearest Neighbors (KNN) shows superior performance, achieving accuracies of 0.989, 0981 and 0.979 on training, validation and testing sets. The performance of the KNN algorithm combined with the W-H-CS feature combination technique is highly dependent on the choice of k and relevance of the selected features.