Plant Recognition based on Deep Belief Network Classifier and Combination of Local Features

Abstract

During the past decades, recognition of plant types has attracted the attention of numerous researchers due to its outstanding applications including precision agriculture. Applying to the video frames, this paper proposes a hybrid method which combines the features extracted from the images using the SIFT, HOG and GIST descriptors and classifies the plants by means of the deep belief network. First, in order to avoid ineffective features, a pre-processing course is performed on the image. Then, the mentioned descriptors extract several features from the image. Due to the problems of working with a large number of the features, a small and distinguishing feature set is produced using the bag of words technique. Finally, these reduced features are given to a deep belief network in order to recognize the plants. Comparing the results of the proposed method with some other existing methods demonstrates an improvement in the accuracy, precision and recall measures for the approach of this work in the plant recognition.