Abstract
Classification of crops is one of the important processes in precision agriculture. Classification of crops based on their verity, enhances the quality. In this paper, we presented a study of three main supervised classifiers, KNN, SVM and ANN for classifying the raw arecanut using color histogram and color moments as features. Experiments conducted over arecanut image dataset of 800 images across 4 classes. Among these classifiers K-NN gave a good result of 98.16% of with color histogram as feature.
Publisher
Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP
Subject
Computer Science Applications,General Engineering,Environmental Engineering
Cited by
3 articles.
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1. Enhancing Arecanut Farming Profits through Technological Advancements: A CNN-based Approach for Efficient Grading and Sorting;2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI);2024-01-18
2. A Review of the Literature on Arecanut Sorting and Grading Using Computer Vision and Image Processing;International Journal of Applied Engineering and Management Letters;2023-04-29
3. Grading of Arecanut Using Machine Learning Techniques;International Journal of Scientific Research in Computer Science, Engineering and Information Technology;2022-07-03