Classification of Wheat Grains Using Machine Algorithms
Journal: International Journal of Science and Research (IJSR) (Vol.2, No. 8)Publication Date: 2013-08-05
Authors : Meesha Punn; Nidhi Bhalla;
Page : 363-366
Keywords : Classification; Computer Vision System; Image Processing; Grading; Quality; SVM; Wheat;
Abstract
India is the second largest producer of wheat in the world after China. Specifying the quality of wheat manually requires an expert judgment and is time consuming. To overcome this problem, machine algorithms can be used to classify wheat according to its quality. In this paper we have done wheat classification by using two machine learning algorithms, that is, Support Vector Machine (OVR) and Neural Network (LM). For classification, images of wheat grain are captured using digital camera and thresholding is performed. Following this step, features of wheat are extracted from these images and machine learning algorithms are implemented. The accuracy of Support Vector Machine is 86.8 % and of Neural network is 94.5 %. Results show that Neural Network (LM) is better than Support Vector Machine (OVR).
Other Latest Articles
- Aspect Level Information Retrieval System for Micro Blogging Site
- Design of Bagasse Dryer to Recover Energy of Water Tube Boiler in a Sugar Factory
- Study and Analysis of Multiwavelet Transform with Threshold in Image Denoising: A Survey
- The Role of Tonga Language and Culture Committee (TOLACCO), Roman Catholic and Community Leadership in the Promotion of Literacy in Binga Community in Zimbabwe
- Efficacy of Dual Task Training to Improve Functional Gait Performance in Idiopathic Parkinsons disease Subjects
Last modified: 2021-06-30 20:21:07