2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 7 , Issue 1 , PP: 30-39, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Abdelaziz A. Abdelhamid 1 * , El-Sayed M. El-Kenawy 2 , Abdelhameed Ibrahim 3 , Marwa M. Eid 4
Doi: https://doi.org/10.54216/JISIoT.070103
When assessing the quality of the grain supply chain's quality, it is essential to identify and authenticate wheat types, as this is where the process begins with the examination of seeds. Manual inspection by eye is used for both grain identification and confirmation. High-speed, low-effort options became available thanks to automatic classification methods based on machine learning and computer vision. To this day, classifying at the varietal level is still challenging. Classification of wheat seeds was performed using machine learning techniques in this work. Wheat area, wheat perimeter, compactness, kernel length, kernel width, asymmetry coefficient, and kernel groove length are the 7 physical parameters used to categorize the seeds. The dataset includes 210 separate instances of wheat kernels, and was compiled from the UCI library. The 70 components of the dataset were selected randomly and included wheat kernels from three different varieties: Kama, Rosa, and Canadian. In the first stage, we use single machine learning models for classification, including multilayer neural networks, decision trees, and support vector machines. Each algorithm's output is measured against that of the machine learning ensemble method, which is optimized using the whale optimization and stochastic fractal search algorithms. In the end, the findings show that the proposed optimized ensemble is achieving promising results when compared to single machine learning models.
Neural network , Support vector machine , Decision tree , Voting ensemble
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