A Comparative Study on Rice Grain Classification Using Convolutional Neural Network and Other Machine Learning Techniques
Keywords:
Convolutional Neural Network, Image Processing, Rice grain, Texture FeatureAbstract
Rice is the paramount concern crop in India, and it can be difficult to discern between the many types available. The sort and quality of grain are swiftly ascertained by visual inspection in the present grain-handling system. It takes human abilities to distinguish between different varieties of rice, and this process can be labor-intensive and time-consuming. Furthermore, the classification task may differ from person to person due to the subjectivity of human perception of images. Thus, digital image processing may be used to get around all of these problems. Several convolutional neural networks namely, Googlenet, Resnet50, Alexnet, and EfficientnetB0, as well as other parametric and non-parametric classifiers namely, K-Nearest Neighbours (KNN), Linear Discriminant Analysis (LDA), Naïve Baise (NB), Support Vector Machine (SVM), Decision Trees (DT) and Back Propagation Neural Network (BPNN) are used to classify eight distinct sorts of rice grains. In this work, 800 samples of eight distinct varieties of rice make up the image data set. It is found that CNN models, can achieve classification accuracy up-to 68.20%. However, classification based on other classifiers using texture features provides accuracy as high as 96.75%. It is observed that, other classifiers perform a more accurate classification of rice as compared to that of CNN models.
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