African Journal of Agriculture and Food Security

African Journal of Agriculture and Food Security ISSN 2375-1177 Vol. 8 (11), pp. 001-007, November, 2020. © International Scholars Journals

Full Length Research Paper

Weed/corn seedling recognition by support vector machine using texture features

Lanlan Wu* and Youxian Wen

College of Engineering and Technology, Huazhong Agricultural University, Wuhan, 430070, P. R. China.

Accepted 23 August, 2020


This study investigated the effect of a new approach, the support vector machine, as a classifier tool to identify the weeds in corn fields at early growth stage. Image segmentation was done by transforming original color images to gray level images according to the statistical values of red, green, blue components. The Gray Level Co-occurrence Matrix (GLCM) and statistical properties of the histogram from the gray level images were further used to obtain the texture features of the weeds and corn seedlings. These texture features were used in the classification procedure. Principle component analysis was used to select the texture features according to their better contributions to reduce space dimensions. A Support Vector Machine (SVM) classifier was employed to recognize the weeds and the corn seedlings. The results indicated that the SVM classifiers with different feature selections could identify successfully weed-corn with a higher accuracy ranged from 92.31 to 100%. A comparison study of the recognition capabilities of SVM and back-propagation (BP) neural-network classifier using the same data set was conducted. It was found that the SVM classifier provided the best recognition performance with an accuracy of 100%, which exceeded the accuracy of 80% given by the BP classifier.

Key words: Corn seedling, weeds, texture, support vector machine, recognize.