Wheat Seed Classification: Utilizing Ensemble Machine Learning Approach

Ajay Khatri,Jyotir M Chatterjee,Shweta Agrawal,Punit Gupta

doi:10.1155/2022/2626868

Abstract

Recognizing and authenticating wheat varieties is critical for quality evaluation in the grain supply chain, particularly for methods for seed inspection. Recognition and verification of grains are carried out manually through direct visual examination. Automatic categorization techniques based on machine learning and computer vision offered fast and high-throughput solutions. Even yet, categorization remains a complicated process at the varietal level. The paper utilized machine learning approaches for classifying wheat seeds. The seed classification is performed based on 7 physical features: area of wheat, perimeter of wheat, compactness, length of the kernel, width of the kernel, asymmetry coefficient, and kernel groove length. The dataset is collected from the UCI library and has 210 occurrences of wheat kernels. The dataset contains kernels from three wheat varieties Kama, Rosa, and Canadian, with 70 components chosen at random for the experiment. In the first phase, K-nearest neighbor, classification and regression tree, and Gaussian Naïve Bayes algorithms are implemented for classification. The results of these algorithms are compared with the ensemble approach of machine learning. The results reveal that accuracies calculated for KNN, decision, and Naïve Bayes classifiers are 92%, 94%, and 92%, respectively. The highest accuracy of 95% is achieved through the ensemble classifier in which decision is made based on hard voting.

Highlights

In many developing nations, farming is the most significant economic sector
(2) e model is compared with three machine learning classifiers: K-nearest neighbors (KNN) classifier, decision tree classifier (CART), and Gaussian Naive Bayes (NB) (NB)
F1-score: the F1-score is calculated by combining both precision and recall. at is, a high F1 score indicates a low number of false positives and false negatives, which infers that the model is accurately detecting actual threats and are not bothered by false alarms. e formula for calculating the F1 score is precision ∗ recall

Summary

Introduction

In many developing nations, farming is the most significant economic sector. Most of the tasks are carried out without the use of modern technology. E varietal level may be challenging due to the great degree of resemblance in the characteristics of different kinds of wheat seeds. E use of a single classifier for the objects which has a very minute difference in physical characteristics such as color, texture, and morphology does not give better accuracy [11]. To address this problem, ensemble approach is used in the present work. (1) An optimized classifier is designed for wheat seed classification by utilizing an ensemble machine learning approach with bagging (2) e model is compared with three machine learning classifiers: K-nearest neighbors (KNN) classifier, decision tree classifier (CART), and Gaussian NB (NB)

Related Work

Machine Learning Models

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Conclusion