An improved technique of plant leaf classificaion using hybrid feature modeling

Various applications have been developed during recent years which are based on the computer vision system. In this field, plant species recognition is a challenging task for researchers due to environmental and image acquisition condition of image. Leaf classification application can be used for various purpose such as remote sensing imaging, botanical characteristically analysis etc. Now a day, amount of dataset is increasing rapidly in this field. Thus it motivates us to develop an efficient model for leaf classification in terms of computation time and accuracy. To address this issue, here in this work we propose a hybrid approach for feature extraction to improve the classification accuracy. Proposed model follows twofold working process whereas in first stage image pre-processing is applied to enhance the image quality of image by applying image enhancement with the help of auto-regressive model, second stage performs feature computation by combining morphological, shape and SIFT feature and finally Deep Neural Network is applied for classification performance evaluation. Proposed work is a combination of image enhancement, morphological feature, SIFT feature and classification technique. For efficient image enhancement, auto-regressive model is adopted here. A comparative study of classification performance is presented which shows the robustness of proposed model.