Disease detection of apple leaf with combination of color segmentation and modified DWT

Abayomi-Alli, 2021, Cassava disease recognition from low-quality images using enhanced data augmentation model and deep learning, Expert Syst., 38, e12746, 10.1111/exsy.12746 Abisha, 2019, Application of Image Processing Techniques and Artificial Neural Network for Detection of Diseases on Brinjal Leaf, IETE J. Res., 1 Adeel, 2019, Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion, Sustain. Comput. Informatics Syst., 24 Al Bashish, 2011, Detection and classification of leaf diseases using K-means-based segmentation and, Inf. Technol. J., 10, 267, 10.3923/itj.2011.267.275 Ali, 2017, Symptom based automated detection of citrus diseases using color histogram and textural descriptors, Comput. Electron. Agric., 138, 92, 10.1016/j.compag.2017.04.008 Almadhor, 2021, Ai-driven framework for recognition of guava plant diseases through machine learning from dslr camera sensor based high resolution imagery, Sensors, 21, 3830, 10.3390/s21113830 Bansal, 2021, Disease Detection in Apple Leaves Using Deep Convolutional Neural Network, Agriculture, 11, 617, 10.3390/agriculture11070617 Basavaiah, 2020, Tomato Leaf Disease Classification using Multiple Feature Extraction Techniques, Wirel. Pers. Commun., 115, 633, 10.1007/s11277-020-07590-x Bi, 2020, MobileNet based apple leaf diseases identification, Mob. Networks Appl., 1 Bracino, A.A., Concepcion, R.S., Bedruz, R.A.R., Dadios, E.P., Vicerra, R.R.P., 2020. Development of a Hybrid Machine Learning Model for Apple (Malus domestica) Health Detection and Disease Classification, in: 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). IEEE, pp. 1–6. G. Capizzi LO SCIUTO, G., Napoli, C., Tramontana, E., WOŹNIAK, M., A Novel Neural Networks-Based Texture Image Processing Algorithm for Orange Defects Classification 2016 Int. J. Comput. Sci Appl 13. Chuanlei, 2017, Apple leaf disease identification using genetic algorithm and correlation based feature selection method, Int. J. Agric. Biol. Eng., 10, 74 Cruz, 2017, X-FIDO: An effective application for detecting olive quick decline syndrome with deep learning and data fusion, Front. Plant Sci., 8, 1741, 10.3389/fpls.2017.01741 Dubey, 2016, Apple disease classification using color, texture and shape features from images, Signal, Image Video Process., 10, 819, 10.1007/s11760-015-0821-1 Habib, 2020, Machine vision based papaya disease recognition, J. King Saud Univ. Inf. Sci., 32, 300 Hamdani, 2021, Detection of oil palm leaf disease based on color histogram and supervised classifier, Optik (Stuttg)., 245 D. Hughes M. Salathé An open access repository of images on plant health to enable the development of mobile disease diagnostics 2015 arXiv Prepr arXiv1511.08060. Jiang, 2020, Image recognition of four rice leaf diseases based on deep learning and support vector machine, Comput. Electron. Agric., 179, 10.1016/j.compag.2020.105824 Khan, 2019, An optimized method for segmentation and classification of apple diseases based on strong correlation and genetic algorithm based feature selection, IEEE Access, 7, 46261, 10.1109/ACCESS.2019.2908040 Khan, 2020 Kumari, 2019, Leaf disease detection: feature extraction with K-means clustering and classification with ANN, 1095 Li, D., Wang, R., Xie, C., Liu, L., Zhang, J., Li, R., Wang, F., Zhou, M., Liu, W., 2020. A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network. Sensors 2020, Vol. 20, Page 578 20, 578. https://doi.org/10.3390/S20030578. Luo, 2021, Apple Leaf Disease Recognition and Sub-Class Categorization Based on Improved Multi-Scale Feature Fusion Network, IEEE Access, 9, 95517, 10.1109/ACCESS.2021.3094802 Padol, 2016, SVM classifier based grape leaf disease detection, 175 Pravin Kumar, 2021, Particle Swarm Optimization (PSO) with fuzzy c means (PSO-FCM)–based segmentation and machine learning classifier for leaf diseases prediction, Concurr. Comput. Pract. Exp., 33, e5312, 10.1002/cpe.5312 L. Ramírez Alberto E.C. Ardila C., Augusto Prieto Ortiz, F., A computer vision system for early detection of anthracnose in sugar mango (Mangifera indica) based on UV-A illumination 2022 Process. Agric Inf 10.1016/J.INPA.2022.02.001. Z. Rehman ur, Khan, M.A., Ahmed, F., Damaševičius, R., Naqvi, S.R., Nisar, W., Javed, K., Recognizing apple leaf diseases using a novel parallel real-time processing framework based on MASK RCNN and transfer learning: An application for smart agriculture IET Image Process. 15 2021 2157 2168. Sharif, 2018, Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection, Comput. Electron. Agric., 150, 220, 10.1016/j.compag.2018.04.023 Singh, 2019, Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease, IEEE Access, 7, 43721, 10.1109/ACCESS.2019.2907383 Sinha, 2020, Olive spot disease detection and classification using analysis of leaf image textures, Procedia Comput. Sci., 167, 2328, 10.1016/j.procs.2020.03.285 Vishnu, 2015, Plant disease detection using leaf pattern: A review, Int. J. Innov. Sci. Eng. Technol., 2, 774 Wang, 2009, Plant leaf disease recognition based on kernel K-means clustering algorithm. Nongye Jixie Xuebao= Trans, Chinese Soc. Agric. Mach., 40, 152 Wang, 2017 Wiesner-Hanks, 2018, Image set for deep learning: field images of maize annotated with disease symptoms, BMC Res. Notes, 11, 1, 10.1186/s13104-018-3548-6 Yadav, R., Kumar Rana, Y., Nagpal, S., 2019. Plant leaf disease detection and classification using particle swarm optimization. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 11407 LNCS, 294–306. https://doi.org/10.1007/978-3-030-19945-6_21/COVER/. Yu, 2020, Leaf spot attention network for apple leaf disease identification, in, 52 Zhang, 2018, Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG. Optik (Stuttg), 157, 866, 10.1016/j.ijleo.2017.11.190 Zhong, 2020, Research on deep learning in apple leaf disease recognition, Comput. Electron. Agric., 168, 10.1016/j.compag.2019.105146