A modern approach for plant leaf disease classification which depends on leaf image processing

Agrarian production is that trait on which our nation's economy immensely depends. This is the motivation that recognition of leaves unhealthiness is the solution for saving the reduction of crops and productivity. It requisite enormous amount of work, mastery in the leaf diseases, and additionally need the extreme amount of time. Thus, image processing techniques are applied for the discovering and recognition of plant leaf unhealthiness. Recognition of plant leaf diseases along some automatic method is useful as it decrease a huge effort of observing in large farms, and at initial phase itself it identify the signs of diseases. Plant leaf disease detection and identification includes the stages like image acquisition, image pre-processing, image segmentation, feature extraction and classification. This paper discusses techniques for image pre-processing, image segmentation algorithm used for automatic recognition and research on various plant leaf disease classification algorithms that may be used for leaves disease classification.

[1]  J. Sil,et al.  Rice disease identification using pattern recognition techniques , 2008, 2008 11th International Conference on Computer and Information Technology.

[2]  Lin Zhang,et al.  Study on the Methods of Detecting Cucumber Downy Mildew Using Hyperspectral Imaging Technology , 2012 .

[3]  Murali Krishnan,et al.  A novel algorithm for detecting bacterial leaf scorch (BLS) of shade trees using image processing , 2013, 2013 IEEE 11th Malaysia International Conference on Communications (MICC).

[4]  R. V. Kshirsagar,et al.  Cotton leaf disease identification using pattern recognition techniques , 2015, 2015 International Conference on Pervasive Computing (ICPC).

[5]  William E. Fry,et al.  Development and implementation of the BlightPro decision support system for potato and tomato late blight management , 2015, Comput. Electron. Agric..

[6]  Malik Braik,et al.  Fast and Accurate Detection and Classification of Plant Diseases , 2011 .

[7]  L. Plümer,et al.  Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with Support Vector Machines , 2011 .

[8]  Jayme Garcia Arnal Barbedo,et al.  Digital image processing techniques for detecting, quantifying and classifying plant diseases. , 2013 .

[9]  Yogesh H. Dandawate,et al.  An automated approach for classification of plant diseases towards development of futuristic Decision Support System in Indian perspective , 2015, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[10]  Laure Tougne,et al.  Tree Leaves Extraction in Natural Images: Comparative Study of Preprocessing Tools and Segmentation Methods , 2015, IEEE Transactions on Image Processing.

[11]  Jeremy S. Smith,et al.  An image-processing based algorithm to automatically identify plant disease visual symptoms. , 2009 .

[12]  H. Gholipourkanani,et al.  Use of propofol as an anesthetic and its efficacy on some hematological values of ornamental fish Carassius auratus , 2013, SpringerPlus.

[13]  Di Cui,et al.  Image processing methods for quantitatively detecting soybean rust from multispectral images , 2010 .

[14]  Avinash Shrivas,et al.  Color Image Segmentation Using K-Means Clustering and Otsu ’ s Adaptive Thresholding , 2014 .

[15]  Dilip Kumar Chakrabarti,et al.  A brief survey of computerized expert systems for crop protection being used in India , 2008 .

[16]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[17]  Malik Braik,et al.  Detection and Classification of Leaf Diseases using K-means-based Segmentation and Neural-networks-based Classification , 2011 .

[18]  Reza Ehsani,et al.  Review: A review of advanced techniques for detecting plant diseases , 2010 .

[19]  R. Jailani,et al.  Orchid leaf disease detection using border segmentation techniques , 2014, 2014 IEEE Conference on Systems, Process and Control (ICSPC 2014).

[20]  Shun'ichi Kaneko,et al.  Early Detection and Continuous Quantization of Plant Disease Using Template Matching and Support Vector Machine Algorithms , 2013, 2013 First International Symposium on Computing and Networking.

[21]  Vidya Kumbhar,et al.  A Comprehensive Study of Application of Decision Support System in Agriculture in Indian Context , 2013 .

[22]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[23]  Sanjay B. Patil,et al.  Grading of Soybean Leaf Disease Based on Segmented Image Using K-means Clustering , 2016 .