Plants are the backbone of all life on Earth and an essential resource for human well-being. Plant recognition is very important in agriculture for the management of plant species whereas botanists can use this application for medicinal purposes. Leaf of different plants have different characteristics which can be used to classify them. This paper presents a simple and computationally efficient method for plant identification using digital image processing and machine vision technology. The proposed approach consists of three phases: pre-processing, feature extraction and classification. Pre- processing is the technique of enhancing data images prior to computational processing. The feature extraction phase derives features based on color and shape of the leaf image. These features are used as inputs to the classifier for efficient classification and the results were tested and compared using Artificial Neural Network (ANN) and Euclidean (KNN) classifier. The network was trained with 1907 sample leaves of 33 different plant species taken form Flavia dataset. The proposed approach is 93.3 percent accurate using ANN classifier and the comparison of classifiers shows that ANN takes less average time for execution than Euclidean distance method.
展开▼
