Deep Learning Approach for the Morphological Differentiation of Corn Seed Types
Abstract
Corn is one of Indonesia's main food ingredients that contains the second largest source of carbohydrates after rice. Classification of the type and quality of corn seeds is still conducted manually by farmers. This procedure is time-consuming and can result in inaccuracies in sorting. Morphology has important characteristics to determine varieties such as size, color, area and seed shape. Some of these attributes, if measured manually, will take a long time and complexity that requires special expertise. The right way to describe these characteristics is to utilize machine learning. The machine learning used is CNN (Convolutional Neural Network). The CNN models used are ResNet101, Resnet50, VGG-19 and MobileNetV2. An analysis of the performance of the model was carried out using a confusion matrix. The results of the CNN model performance parameters for the classification of corn seed varieties with the ResNet101 model showed an accuracy of 89.8%, a precision of 86.9%, a recall of 88.3% and an F1-score of 86.4%. The ResNet50 model showed an accuracy of 86.27%, a precision of 83.2%, a recall of 84.1% and an F1-score of 83.4%. While the VGG-19 model showed an accuracy of 76.47%, a precision of 66.8%, a recall of 78.% and an F1-score of 71.1%. Meanwhile, the MobileNetV2 model showed an accuracy of 73.34%, a precision of 69%, a recall of 69.8% and an F1-score of 69.8%.
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Copyright (c) 2025 Doni Rizqi Setiawan, Esa Prakasa, Dimas Firmanda Al Riza, Sumardi Hadi Sumarlan, Muhammad Aqil
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