A novel image classification technique for spot and blight diseases in plant leaves

References

• Arnal Barbedo JG. Digital image processing techniques for detecting, quantifying and classifying plant diseases. London: SpringerPlus; 2013; p. 660.
   Google Scholar
• Sinha A, Shekhawat RS. Review of image processing approaches for detecting plant diseases. IET Image Processing. 2020;14(8):1427–1439.
   Web of Science ®Google Scholar
• Hanping M, Yancheng Z, Bo H. Segmentation of crop disease leaf images using fuzzy c-means clustering algorithm. Trans Chinese Soc Agricultural Eng. 2008;9:136–140.
   Google Scholar
• Barbedo JGA. Plant disease identification from individual lesions and spots using deep learning. Biosystems Eng. 2019;180:96–107.
   Web of Science ®Google Scholar
• Bello R, Nowe A, Caballero Y. A model based on ant colony system and rough set theory to feature selection. Proceedings of the 2005 conference on genetic and evolutionary computation – GECCO '05; Washington; 2005. p. 275–276.
   Google Scholar
• Wang X, Yang J, Teng X, et al. Feature selection based on rough sets and particle swarm optimization. Pattern Recognit Lett. 2007;28(4):459–471.
   Web of Science ®Google Scholar
• Hassanien AE, Gaber T, Mokhtar U, et al. An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Comput Electron Agric. 2017;136:86–96.
   Web of Science ®Google Scholar
• Amara J, Bouaziz B, Algergawy A, et al. A deep learning-based approach for banana leaf diseases classification. Business, Technologie and Web (BTW Workshops); Stuttgart, Germany: 2017. p. 79–88.
   Google Scholar
• Sladojevic S, Arsenovic M, Anderla A, et al. Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci. 2016:1–11.
   Web of Science ®Google Scholar
• Jagan K, M B, Palanivel S. Detection and recognition of diseases from paddy plant leaf images. Int J Comput Appl. 2016;144:34–41.
   Google Scholar
• Sinha A, Shekhawat RS. Detection, quantification and analysis of neofabraea leaf spot in olive plant using image processing techniques. 2019 5th International conference on signal processing, computing and control (ISPCC), Solan, Himachal Pradesh, India; Oct 2019. p. 348–353.
   Google Scholar
• Ahmad IS, Reid JF, Paulsen MR, et al. Color classifier for symptomatic soybean seeds using image processing. Plant Disease. 1999;83(4):320–327.
   PubMed Web of Science ®Google Scholar
• Aleixos N, Blasco J, Navarron F, et al. Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Comput Electron Agric. 2002;33(2):121–137.
   Web of Science ®Google Scholar
• López-García F, Andreu-García G, Blasco J, et al. Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Comput Electron Agric. 2010;71(2):189–197.
   Web of Science ®Google Scholar
• Smith S, Dickson S. Quantification of active vesicular-arbuscular mycorrhizal infection using image analysis and other techniques. Funct Plant Biol. 1991;18(6):637–648.
   Google Scholar
• DeChant C, Wiesner-Hanks T, Chen S, et al. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology. 2017;107(11):1426–1432.
   PubMed Web of Science ®Google Scholar
• Zhang S, Zhu Y, You Z, et al. Fusion of superpixel, expectation maximization and PHOG for recognizing cucumber diseases. Comput Electron Agric. 2017;140:338–347.
   Web of Science ®Google Scholar
• Youwen T, Tianlai L, Yan N. The recognition of cucumber disease based on image processing and support vector machine. Image and signal processing 2008. CISP'08. Congress; Vol. 2. Siver Spring (MD): IEEE; 2008. p. 262–267.
   Google Scholar
• Kurniawati NN, S. Abdullah SNH, Abdullah S, et al. Investigation on image processing techniques for diagnosing paddy diseases. Soft computing and pattern recognition 2009. SOCPAR'09. International conference. Silver Spring (MD): IEEE; 2009. p. 272–277.
   Google Scholar
• Kurniawati NN, Abdullah SNHS, Abdullah S, et al. Texture analysis for diagnosing paddy disease. Electrical Engineering and Informatics. 2009. ICEEI'09. International conference. Vol. 1. Silver Spring (MD): IEEE; 2009. p. 23–27.
   Google Scholar
• Pydipati R, Burks T, Lee W. Statistical and neural network classifiers for citrus disease detection using machine vision. Trans ASAE. 2005;48(5):2007–2014.
   Google Scholar
• Huang K-Y. Application of artificial neural network for detecting phalaenopsis seedling diseases using color and texture features. Comput Electron Agric. 2007;57(1):3–11.
   Web of Science ®Google Scholar
• Sanyal P, Bhattacharya U, Parui SK, et al. Color texture analysis of rice leaves diagnosing deficiency in the balance of mineral levels towards improvement of crop productivity. Information Technology (ICIT 2007). 10th International conference. Silver Spring (MD): IEEE; 2007. p. 85–90.
   Google Scholar
• Hairuddin MA, Tahir NM, Baki” SRS. Overview of image processing approach for nutrient deficiencies detection in Elaeis guineensis. 2011 IEEE international conference on system engineering and technology; 2011 Jun. Silver Spring (MD): IEEE; 2011. p. 116–120.
   Google Scholar
• Al Bashish D, Braik M, Bani-Ahmad S. A framework for detection and classification of plant leaf and stem diseases. 2010 International conference on signal and image processing; 2010 Dec; Chennai; 2010. p. 113–118.
   Google Scholar
• Kai S, Zhikun L, Hang S, et al. A research of maize disease image recognition of corn based on bp networks. Measuring Technology and Mechatronics Automation (ICMTMA), 2011 Third international conference. Vol. 1. Silver Spring (MD): IEEE; 2011. p. 246–249.
   Google Scholar
• Yigit E, Sabanci K, Toktas A, et al. A study on visual features of leaves in plant identification using artificial intelligence techniques. Comput Electron Agric. 2019;156:369–377.
   Web of Science ®Google Scholar
• Phadikar S, Sil J, Das AK. Classification of rice leaf diseases based on morphological changes. Int J Inform Electron Eng. 2012;2(3):460–463.
   Google Scholar
• Pydipati R, Burks TF, Lee WS. Identification of citrus disease using color texture features and discriminant analysis. Comput Electron Agric. 2006;52:49–59.
   Web of Science ®Google Scholar
• Sanyal P, Bhattacharya U, Parui SK, et al. Color texture analysis of rice leaves diagnosing deficiency in the balance of mineral levels towards improvement of crop productivity. Information Technology (ICIT 2007). 10th International conference. Silver Spring (MD): IEEE; 2007. p. 85–90.
   Google Scholar
• Barbedo JGA, Godoy CV. Automatic classification of soybean diseases based on digital images of leaf symptoms. Ponta Grossa; Brazil: Embrapa Soja-Artigo em anais de congresso (ALICE); 2015.
   Google Scholar
• Camargo A, Smith J. An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems Eng. 2009;102(1):9–21.
   Web of Science ®Google Scholar
• Barbedo JGA, Koenigkan LV, Santos TT. Identifying multiple plant diseases using digital image processing. Biosystems Eng. 2016;147:104–116.
   Web of Science ®Google Scholar
• Munisami T, Ramsurn M, Kishnah S. Plant leaf recognition using shape features and colour histogram with k-nearest neighbour classifiers. Procedia Computer Science. Second international symposium on computer vision and the internet (VisionNet'15), Kerala, India, Vol. 58; 2015. p. 740–747.
   Google Scholar
• Bakhshipour A, Jafari A, Nassiri SM, et al. Weed segmentation using texture features extracted from wavelet sub-images. Biosystems Eng. 2017;157:1–12.
   Web of Science ®Google Scholar
• Kazmi W, Garcia-Ruiz F, Nielsen J, et al. Exploiting affine invariant regions and leaf edge shapes for weed detection. Comput Electron Agric. 2015;118:290–299.
   Web of Science ®Google Scholar
• Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3. 1973;3:610–621.
   Web of Science ®Google Scholar
• Shearer SA, Holmes RG. Plant identification using color co-occurrence matrices. Trans ASAE. 1990;33:1237–1244.
   Google Scholar
• Donohue K, Huang L, Burks T, et al. Tissue classification with generalized spectrum parameters. Ultrasound Med Biol. 2001;27(11):1505–1514.
   PubMed Web of Science ®Google Scholar
• Gharge S, Singh P. Image processing for soybean disease classification and severity estimation. In: Shetty NR, Prasad N, Nalini N, editors. Emerging research in computing, information, communication and applications. New Delhi: Springer India; 2016. p. 493–500.
   Google Scholar
• Dandawate Y, Kokare R. An automated approach for classification of plant diseases towards development of futuristic decision support system in indian perspective. 2015 International conference on advances in computing, communications and informatics (ICACCI). Silver Spring (MD): IEEE; 2015. p. 794–799.
   Google Scholar
• Shearer S, Holmes RG, Plant identification using color co-occurrence matrices. Trans ASABE. 1990;33:1237–1244.
   Google Scholar
• Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;SMC-3:610–621.
   Web of Science ®Google Scholar
• Zhang S, Wu X, You Z, et al. Leaf image based cucumber disease recognition using sparse representation classification. Comput Electron Agric. 2017;134:135–141.
   Web of Science ®Google Scholar
• Phadikar S, Sil J, Das AK. Rice diseases classification using feature selection and rule generation techniques. Comput Electron Agric. 2013;90:76–85.
   Web of Science ®Google Scholar
• Camargo A, Smith JS. Image pattern classification for the identification of disease causing agents in plants. Comput Electron Agric. 2009;66:121–125.
   Web of Science ®Google Scholar
• Jia Y, Shelhamer E, Donahue J, et al. Caffe: convolutional architecture for fast feature embedding. Preprint (2014) arXiv:1408.5093.
   Google Scholar
• Deng J, Dong W, Socher R, et al. Imagenet: a large-scale hierarchical image database. 2009 IEEE conference on computer vision and pattern recognition, Miami, Florida, USA; 2009 Jun. p. 248–255.
   Google Scholar
• Singh V, Misra A. Detection of plant leaf diseases using image segmentation and soft computing techniques. Infor Proc Agric. 2017;4(1):41–49.
   Google Scholar
• Jorge David Salgado JS, Paul* PA. Northern corn leaf blight. 2019. [cited 2018 Nov]. Available from: https://ohioline.osu.edu/factsheet/plpath-cer-10 accessed Nov 2018.
   Google Scholar
• Aaaaa A. Isariopsis leaf spot. 2019. [cited 2018 Nov]. Available from: https://plantvillage.psu.edu/topics/grape/infos.
   Google Scholar
• Tomato B. Bacterial spot of pepper and tomato. Nc state extension publications; 2019.
   Google Scholar
• Septoria leaf spot. [cited 2018 Nov]. Available from: https://www.thespruce.com/identifying-and-controlling-septoria-leaf-spot-of-tomato-1402974.
   Google Scholar
• Mohanty S. spmohanty/plantvillage-dataset. 2016. [cited 2019 Nov]. Available From: https://github.com/spMohanty/PlantVillage-Dataset/.
   Google Scholar
• Mohanty SP, Hughes DP, Salathé M. Using deep learning for image-based plant disease detection. Front Plant Sci. 2016;7:346.
   PubMed Web of Science ®Google Scholar
• Plant village. [cited 2018 Nov]. Available from: http://leafsnap.com/dataset/.
   Google Scholar
• k-means. Available From: https://in.mathworks.com/help/stats/kmeans.html.
   Google Scholar
• Analyst I. Matlab central file exchange, color segmentation by delta e color difference. Available From: https://www.mathworks.com/matlabcentral/fileexchange/31118-color-segmentation-by-delta-e-color-difference, Dec. 2019.
   Google Scholar
• Sabrol H, Kumar S. Intensity based feature extraction for tomato plant disease recognition by classification using decision tree. Int J Computer Science and Information Security. 2016;14(9):622–00.
   Google Scholar