(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 11, No. 2, 2020 160 | Page www.ijacsa.thesai.org Automatic Detection of Plant Disease and Insect Attack using EFFTA Algorithm Kapilya Gangadharan 1 Research Scholar, Dept. of Computer Science and Engineering, SSE, Saveetha Institute of Medical and Technical Sciences Chennai, India G. Rosline Nesa Kumari 2 Professor, Dept. of Computer Science and Engineering Shadan women’s college of Engineering and Technology, Telangana, India D. Dhanasekaran 3 Professor, Dept. of Computer Science and Engineering SSE, Saveetha Institute of Medical and Technical Sciences Chennai, India K. Malathi 4 Associate Professor, Dept. of Computer Science and Engineering, SSE, Saveetha Institute of Medical and Technical Sciences Chennai, India Abstract—The diagnosis of plant disease by computer vision using digital image processing methodology is a key for timely intervention and treatment of healthy agricultural procedure and to increase the yield by natural means. Timely addressal of these ailments can be the difference between the prevention and perishing of an ecosystem. To make the system more efficient and feasible we have proposed an algorithm called Enhanced Fusion Fractal Texture Analysis (EFFTA). The proposed method consists of Feature Fusion technique which combines SIFT- Scale Invariant Feature Transform and DWT- Discrete Wavelet Transform based SFTA- Segment Based Fractal Texture Analysis. Image as a whole can be detected by shape, texture and color. SIFT is used to detect the texture feature, it extracts the set of descriptors that is very useful in local texture recognition and it captures accurate key points for detecting the diseased area. Further extraction of texture is considered and that can be performed by WSFTA method. It adopts intra- class analysis and inter- class analysis. Extracted features trained using Back Propagation Neural Network. It improves and expands the success rate and accuracy of extraction also it provides higher precision and efficiency when compared to the other traditional methods. Keywords—Texture analysis; features; computer vision; inter- class; intra-class I. INTRODUCTION Digital images are typically represented in the form of texture, shape and color features. It indicates the attributes of the image. Dimensions of the raw data or information can be reduced by extracting the features. It is a process that is very much required in Machine Learning and pattern recognition. Where the required texture attributes are extracted from the specified dataset. The characteristic represents the original data property like texture, shape or color based upon the requirement. Texture is based on the feature and shape is based on the template Feature extraction mainly deals with reducing the large number of data into subset that describes the required information. The process makes the classification simpler and accurate. Machine learning methodology is mainly dependent on the appropriate and efficient feature extraction. Extraction begins with an underlying arrangement of already estimated information and determined values or features planned as instructive and non-repetitive, it accelerates the successive learning and speculation steps, also it is interpreted by human as well [16]. And it is identified with dimensionality reduction. SIFT process converts the image into smaller dimensions with the required information. It is mainly used for object matching criteria. Key point extraction through SIFT is more accurate than the other traditional methods, improvement shows 11.12 % more using SIFT [17]. SFTA is a used for image decomposition which utilizes OTSU thresholding in a conventional method. To make the decomposition much easier we follow Discrete Wavelet Transform and it is passed to STFA algorithm for further decomposition and it creates a hybrid method called WSFTA. When shift is combined with WSFTA it produces a best and accurate result and it is relatively easier for the classifier to recognize the diseased portion and the healthy portion of the plants. Our work mainly concentrates on proposing the enhanced texture feature fusion which combines SIFT and WSFTA, once after fusing the algorithms it is very important to perform selection process. Selection is done using PCA method to avoid dense feature vector creation that elaborates the vector length causing high computational cost [19]. The selection process results in the accurate and required features. The selected features are then trained and tested using Back Propagation Neural Network Classifier. II. RELATED WORK The texture parameters are calculated using gray level synchronizing matrix spatial variants and to define the diseased area in the plant leaves [1]. Bark classification on texture and fractal dimension is proposed. It combines texture and the structural features to improve the accuracy [2]. Another method is proposed using the objective values, it is calculated by Kurtosis, variance, Skewness and Entropy, where homogeneity and contrast is calculated for estimating the diseased area [3]. GLCM (Gray level Covariance Matrix) is used to extract the texture features where 12 types of features are extracted which include Entropy, Skewness, Kurtosis, Smoothness, Variance, etc. [4]. A method called