Tobacco Plant Recognizing and Counting Based on SVM

摘要

Tobacco plants recognizing and counting accurately is very important in the tobacco plant management. In this paper, we propose an algorithm for tobacco plants recognizing and counting which consists of four main steps: first, we apply the unmanned aircraft to acquire the tobacco images. Second, the tobacco image is converted into the Lab color space, and then b channel in Lab space is processed based on morphological reconstruction. Third, the candidate regions which might contain tobacco plants are extracted based on the processed b channel. Finally, SVM (Support Vector Machine) is employed to classify the candidate regions as tobacco plants or not. The proposed method has been evaluated on a tobacco image data set. Experimental results (96.1% of accuracy and 94.3% of sensitivity) showed that the proposed method is feasible.