Leaf image analysis-based crop diseases classification

Eminently, the countries of developing state have their economy based on agricultural crop yieldings. To retain the economic growth of these countries, the agricultural plants’ disease detection and proper treatment are a leading factor. The work available in the literature basically features pull out to classify the leaf images due to which the classification performance suffers. In the proposed work, we tried to resolve this rough image dataset problem. The proposed technique initially localizes the leaf region by utilizing the color features of the leaf image followed by mixture model-based county expansion for leaf localization. The classification of the leaf images depends on the features of discriminatory properties. The characteristics features of the diseased images show various types of patterns into the leaf region. Here, we utilized the features discriminable property using the Fisher vector in terms of different orders of differentiation of Gaussian distributions. The performance of the proposed system is analyzed using the PlantVillage databases of common pepper, root vegetable as potato, and tomato leaf images using a multi-layer perceptron, and support vector machine. The implementation results confirm the better performance measure of the proposed classification technique than the state of arts and provide an accuracy of 94.35 $$\%$$ % with an area under the curve 94.7 $$\%$$ % .

[1]  Anna Helena Reali Costa,et al.  Comparative assessment of feature selection and classification techniques for visual inspection of pot plant seedlings , 2013 .

[2]  Keith Dobney,et al.  Earliest “Domestic” Cats in China Identified as Leopard Cat (Prionailurus bengalensis) , 2016, PloS one.

[3]  Anil K. Jain,et al.  A modified Hausdorff distance for object matching , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[4]  H. Sabrol,et al.  Tomato plant disease classification in digital images using classification tree , 2016, 2016 International Conference on Communication and Signal Processing (ICCSP).

[5]  Lei Wang,et al.  Compositional Model Based Fisher Vector Coding for Image Classification , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Priyadarshini Patil,et al.  Comparision of Performance of Classifiers - SVM, RF and ANN in Potato Blight Disease Detection Using Leaf Images , 2017, 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC).

[7]  P. Neelamegam,et al.  Image processing based rice plant leaves diseases in Thanjavur, Tamilnadu , 2018, Cluster Computing.

[8]  Shreelekha Pandey,et al.  Semi-automatic leaf disease detection and classification system for soybean culture , 2018, IET Image Process..

[9]  Yashwant Kurmi,et al.  Multifeature-based medical image segmentation , 2018, IET Image Process..

[10]  Anushikha Singh,et al.  Image processing based automated identification of late blight disease from leaf images of potato crops , 2017, 2017 40th International Conference on Telecommunications and Signal Processing (TSP).

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

[12]  Abdelouahab Moussaoui,et al.  Deep Learning for Tomato Diseases: Classification and Symptoms Visualization , 2017, Appl. Artif. Intell..

[13]  Jun Zhang,et al.  Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network , 2019, Sensors.

[14]  Yu Sun,et al.  Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning , 2017, Comput. Intell. Neurosci..

[15]  Weihong Deng,et al.  Compressing Fisher Vector for Robust Face Recognition , 2017, IEEE Access.

[16]  Aditya Khamparia,et al.  Seasonal Crops Disease Prediction and Classification Using Deep Convolutional Encoder Network , 2019, Circuits, Systems, and Signal Processing.

[17]  Khursheed Aurangzeb,et al.  An Optimized Method for Segmentation and Classification of Apple Diseases Based on Strong Correlation and Genetic Algorithm Based Feature Selection , 2019, IEEE Access.

[18]  Berrin A. Yanikoglu,et al.  Automatic plant identification from photographs , 2014, Machine Vision and Applications.

[19]  Bhushan G. Jagyasi,et al.  Severity identification of Potato Late Blight disease from crop images captured under uncontrolled environment , 2014, 2014 IEEE Canada International Humanitarian Technology Conference - (IHTC).

[20]  Erfu Yang,et al.  A Novel Active Semisupervised Convolutional Neural Network Algorithm for SAR Image Recognition , 2017, Comput. Intell. Neurosci..

[21]  Serge Beucher,et al.  The Morphological Approach to Segmentation: The Watershed Transformation , 2018, Mathematical Morphology in Image Processing.

[22]  Yashwant Kurmi,et al.  Tumor Malignancy Detection Using Histopathology Imaging. , 2019, Journal of medical imaging and radiation sciences.

[23]  Aravind Krishnaswamy Rangarajan,et al.  Disease Classification in Eggplant Using Pre-trained VGG16 and MSVM , 2020, Scientific Reports.

[24]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

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

[26]  Zhanhong Ma,et al.  Identification of Alfalfa Leaf Diseases Using Image Recognition Technology , 2016, PloS one.

[27]  Florent Perronnin,et al.  Fisher Kernels on Visual Vocabularies for Image Categorization , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Darko Stefanovic,et al.  Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification , 2016, Comput. Intell. Neurosci..

[29]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[30]  Abhijit Boruah,et al.  Plant Disease Detection by Leaf Image Classification Using Convolutional Neural Network , 2019, Communications in Computer and Information Science.

[31]  A. K. Misra,et al.  Detection of plant leaf diseases using image segmentation and soft computing techniques , 2017 .

[32]  Md. Abu Bakr Siddique,et al.  Digital Image Segmentation in Matlab: A Brief Study on OTSU’s Image Thresholding , 2018, 2018 International Conference on Innovation in Engineering and Technology (ICIET).

[33]  Hongbo Mu,et al.  Tree leaf feature extraction and recognition based on geometric features and Haar wavelet theory , 2019, Engineering in Agriculture, Environment and Food.

[34]  Andrew P French,et al.  Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress , 2017, Plant Methods.

[35]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[36]  Hui-Fuang Ng.  Automatic thresholding for defect detection , 2006, Pattern Recognit. Lett..

[37]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[38]  Xinhua Zhuang,et al.  The digital morphological sampling theorem , 1989, IEEE Trans. Acoust. Speech Signal Process..

[39]  Charles X. Ling,et al.  Using AUC and accuracy in evaluating learning algorithms , 2005, IEEE Transactions on Knowledge and Data Engineering.

[40]  Vijayshri Chaurasia,et al.  Statistical feature extraction based technique for fast fractal image compression , 2016, J. Vis. Commun. Image Represent..

[41]  S. S. Vinod Chandra,et al.  Long-Term Forecasting the Survival in Liver Transplantation Using Multilayer Perceptron Networks , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[42]  Agostinho C. Rosa,et al.  A Statistical Pixel Intensity Model for Segmentation of Confocal Laser Scanning Microscopy Images , 2010, IEEE Transactions on Image Processing.

[43]  Konstantinos P. Ferentinos,et al.  Deep learning models for plant disease detection and diagnosis , 2018, Comput. Electron. Agric..

[44]  Avital Bechar,et al.  Robotic Disease Detection in Greenhouses: Combined Detection of Powdery Mildew and Tomato Spotted Wilt Virus , 2016, IEEE Robotics and Automation Letters.

[45]  T. W. Ridler,et al.  Picture thresholding using an iterative selection method. , 1978 .

[46]  Nicola Vanello,et al.  A Smart Region-Growing Algorithm for Single-Neuron Segmentation From Confocal and 2-Photon Datasets , 2020, Frontiers in Neuroinformatics.

[47]  Yousef Al-Kofahi,et al.  Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images , 2010, IEEE Transactions on Biomedical Engineering.

[48]  Pankaj Bhowmik,et al.  Detection of potato diseases using image segmentation and multiclass support vector machine , 2017, 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE).

[49]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[50]  David W. Jacobs,et al.  Efficient segmentation of leaves in semi-controlled conditions , 2013, Machine Vision and Applications.

[51]  Philip H. S. Torr,et al.  The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix , 1997, International Journal of Computer Vision.