This paper presents a methodology to recognize certain crop fields' images using texture, color and combination of both types of features. In this work, we have considered eight varieties of crop images, namely, Brinjal, Cotton, Groundnut, Paddy, Soyabean, Sugarcane and Sunflower. Texture features using GLCM and color features using HSV are deployed. Artificial Neural Network (ANN) is used for recognition. Considering only as feature, classification accuracies of 63. 75%, 66.25% and 84.375% are obtained using texture, color and their combination respectively. The work is helpful in the area of agriculture for early detection and prevention of diseases. Copyright ⓒ 2015 SERSC know the state-of-the-art development in automation activities in the fields of agriculture and horticulture, we have carried out a literature survey. The gist of the literature survey is given in this section 2.0. Hermawan syahputra, et al, (2014) have proposed a plant recognition system of stereo leaf images using gray level co-occurrence matrix. The methodology has used sum of absolute difference (SAD) and 22 features are extracted using GLCM. The accuracy reported is 83.3%. Pallavi P et al, (2014) have proposed leaf recognition based on Zernike moments. This methodology uses shape, vein, color and texture features to identify the leaves. A neural network approach is used to classify the images. Gowri Ariputhiran, et al, (2013) have proposed feature extraction and classification of high resolution satellite images using GLCM and back propagation technique. The performance is analyzed based on its accuracy, error rate and sensitivity. Vishaka Metre et al, (2013) have proposed an overview of the research on texture based plant leaf classification. In this method a leaf is classified based on different morphological features. Manish Maheshwari et al, (2013) have proposed a new feature extraction technique for color image clustering. A color quantization ordering scheme that focuses on color as feature and considers Hue, Saturation and Value space is proposed. To form clusters of images k-means algorithm is applied. A H kulkarni, et al,(2013) have proposed a framework for recognizing and identifying plants using various features, combined with zernike movements. Classifier used is radial basis probabilistic neural network (RBPNN). Dual stage training algorithm is used to train RBPNN to increase performance of the classifier. Flavia leaf is used for the study. The experimental simulation results show an accuracy rate of 93.82%. Smita Naikwad, et al, (2013) have given an account of advances in image processing for detection of plant diseases using color and texture features. The technique has detected and classified the plant diseases with an accuracy of 83%. S.Abhirami, et al,(2012) have proposed plant species identification based on images of flowers. The features extracted are shape, color and texture. Color based segmentation is carried out using k-means algorithm and active contour model. Texture features are extracted using GLCM. Edge detection is done using Sobel, Robert and Prewitts operators. The classification is done using proximal support vector machine. Abdul Kadir, et al, (2012) have proposed foliage plan identification based on leaves using zerkine moments. Zernike moments are combined with other features like geometric features, color moments and gray level co-occurrence matrix. For identification, probabilistic neural network is used. The experiment show that combined features yield good result. Ashutosh Kumar Bhatt et al, (2012) have proposed an artificial neural network based apple classifier. Input is collected from software and web camera, ANN is used to classify apple according to quality. Experimental data show better results compared to predicted values. Dr J Abdul Jakeel et al, (2012) have proposed an artificial neural network based detection of skin cancer. The methodology for feature extraction used is 2D wavelet transform. ANN is used as a classifier. Norasyikin Fadilah et al,(2012) have proposed oil palm fresh fruit bunch ripeness classification using ANN. This method is used to automate the decision of grading oil palm FFBs (fresh fruit bunches), which replaces manual human grading method. Results show that ANN is able to generalize four ripeness categories of oil palm FFB. R Venkta Ramana Chary, et al, (2012) have proposed feature extraction method for color image similarity. Color projection and different mathematical approaches like mean, median, standard deviation are applied to retreive images within a large collection of images. For retreival of images, RGB color combinations are considered. Results are observed to be efficient. Haipeng Yu, et al,(2009) have proposed image retrieval method which includes color, textural and special features of wood species. A total of 9 features are extracted to retrieve Copyright ⓒ 2015 SERSC 289 wood species. The results show that combined effect of these features yield good results. LiyingZheng, et al,(2008) have proposed a method to separate green vegetation in color images using mean shift procedure for segmentation. Multiple color features are extracted like hue, saturation in HSI color space and red, green, blue in RGB space. With mean shift segmentation algorithm, extracted features and BPNN, images are classified as green and non-green. R Pyadipati, et al,(2006) have proposed a color co-occurrence method (CCM) and discriminant analysis method to recognize citrus plant diseases. The method used texture based Hue, Saturation and Intensity (HSI) color features extracted using CCM and statistical classification algorithm to identify diseased and normal citrus leaves. H Fu and Z Chi, (2006) have proposed a combined approach of using artificial neural network and threshold to recognize plants based on images. Leaf veins are extracted to define features for plant species recognition. The results show that combined approach is good. From the literature survey, it is observed that certain work on recognition of plants based on leaf characteristics has already been carried out. Several feature extraction methods like vein, Zernike moments etc. are being deployed. However, no considerable work is carried out on recognition of field images of varieties of crops. Human beings rarely view one or two leaves in a field. Instead, the entire field image is considered for recognition. This is the motivation for the proposed work. The remaining part of the paper is organized into four sections. Section two gives the literature survey, section three deals with methodology, which further explain about image acquisition, preprocessing, feature extraction and classification, section four gives with results and discussion and finally section five deals with conclusion.
REF use color space features and co-occurrence matrices to classify images captured on crop fields containing several plant types.
A Combined Color and Texture Features Based Methodology for Recognition of Crop Field Image
International Journal of Signal Processing, Image Processing and Pattern Recognition
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