Research Article Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction Bhamidipati Kishore, 1 Ali Yasar , 2 Yavuz Selim Taspinar , 3 Ramazan Kursun , 4 Ilkay Cinar , 5 Venkatesh Gauri Shankar , 6 Murat Koklu , 5 and Isaac Ofori 7 1 Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India 2 Department of Mechatronic Engineering, Selcuk University, Konya, Turkey 3 Doganhisar Vocational School, Selcuk University, Konya, Turkey 4 Guneysinir Vocational School, Selcuk University, Konya, Turkey 5 Department of Computer Engineering, Selcuk University, Konya, Turkey 6 Manipal University Jaipur, Jaipur, Rajasthan, India 7 Department of Environmental and Safety Engineering, University of Mines and Technology, Tarkwa, Ghana Correspondence should be addressed to Isaac Ofori; iofori@umat.edu.gh Received 27 April 2022; Revised 16 June 2022; Accepted 28 June 2022; Published 10 August 2022 Academic Editor: Vijay Kumar Copyright © 2022 Bhamidipati Kishore et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Corn has great importance in terms of production in the field of agriculture and animal feed. Obtaining pure corn seeds in corn production is quite significant for seed quality. For this reason, the distinction of corn seeds that have numerous varieties plays an essential role in marketing. is study was conducted with 14,469 images of BT6470, Calipso, Es_Armandi, and Hiva types of corn licensed by BIOTEK. e classification of images was carried out in three stages. At the first stage, deep feature extraction of the four types of corn images was performed with the pretrained CNN model SqueezeNet 1000 deep features were obtained for each image. In the second stage, in order to reduce these features obtained from deep feature extraction with SqueezeNet, separate feature selection processes were performed with the Bat Optimization (BA), Whale Optimization (WOA), and Gray Wolf Optimization (GWO) algorithms among optimization algorithms. Finally, in the last stage, the features obtained from the first and second stages were classified by using the machine learning methods Decision Tree (DT), Naive Bayes (NB), multi-class Support Vector Machine (mSVM), k-Nearest Neighbor (KNN), and Neural Network (NN). In the classification processes of the features obtained in the first stage, the mSVM model has achieved the highest classification success with 89.40%. In the second stage, as a result of the classifications performed through the active features selected by using three types of feature selection algorithms (BA, WOA, GWO), the classification success obtained with the mSVM model was 88.82%, 88.72%, and 88.95%, respectively. e classification accuracies of the tested methods and the classification accuracies obtained in the first stage are close to each other in terms of classification success. However, with the algorithms used in feature selection, successful classification processes have been carried out with fewer features and in a shorter time. e results of the study, in which classification was carried out in the inexpensive, the objective, and the shorter time of processing for the corn types, present a different perspective in terms of classification performance. 1. Introduction Corn, one of the basic grain products, is a staple food for millions of people all over the world, particularly in Latin America, Asia, and Africa. Corn is used by being processed in various food products directly as human food such as corn flour, semolina, starch, snacks, breakfast cereals as well as it is used in the production of animal feed [1]. Corn, or maize, which can be harvested once a year, is an agricultural product that ranks third after wheat and rice in terms of cultivation area throughout the world [2]. As a multipurpose grain widely cultivated in many parts of the world, corn has Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 2062944, 10 pages https://doi.org/10.1155/2022/2062944