Abstract
The rapid increase in human population has necessitated a corresponding increase in agricultural production. The advancements made in the arena of genomics and bioinformatics can open doors for this. As disease detection is a primary factor in enhancing agricultural production, the current research focuses on coming up with a sound plant leaf disease detection and identification procedure for large areas of crop production. Since dimensionality reduction plays a significant role in effectively detecting a plant leaf disease, this study achieves this by using singular value decomposition (SVD) technique. At first, the leaf images are preprocessed, and background subtraction is carried out using Gaussian mixture model. After this, the diseased area is segmented with the introduction of artificial bee colony-based fuzzy C means algorithm. Then, the detection rate is increased by applying SVD, which reduces the dimensions of multiple feature vectors. Finally, the leaf disease is detected with the help of multi-kernel with parallel deep learning classifier. The implementation of all these steps is done via MATLAB simulation environment. The evaluation of the results of the proposed leaf disease approach is done for metrics including accuracy, recall, precision and F-measure.
Similar content being viewed by others
References
Arivazhagan S, Shebiah RN, Ananthi S, Varthini SV (2013) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric Eng Int CIGR J 15(1):211–217
Kaur L, Laxmi V (2016) Detection of unhealthy region of plant leaves using neural network. Dis Manag 1(05):34–42
Al Bashish D, Braik M, Bani-Ahmad S (2010) A framework for detection and classification of plant leaf and stem diseases. In: International Conference on Signal and Image Processing (ICSIP), pp 113–118
Arun CH, Emmanuel WS, Durairaj DC (2013) Texture feature extraction for identification of medicinal plants and comparison of different classifiers. Int J Comput Appl 62(12):1–9
Kadir A, Nugroho LE, Susanto A, Santosa PI (2013) Leaf classification using shape, color, and texture features, pp 225–231. arXiv:1401.4447
Al-Hiary H, Bani-Ahmad S, Reyalat M, Braik M, ALRahamneh Z (2011) Fast and accurate detection and classification of plant diseases. Int J Comput Appl 17(1):31–38
Chaudhari SI, Pathare MS, Haral S, Mule Y, Katti N (2016) A survey on detection of unhealthy region of plant leaves by using image processing. Int Res J Eng Technol 3(9):1318–1320
Velmurugan P, Renukadevi M (2017) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture based clustering features. Artific Intell Syst Mach Learn 9(1):8–10
Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf Process Agric 4(1):41–49
Sannakki SS, Rajpurohit VS, Nargund V. B, Kulkarni P (2013) Diagnosis and classification of grape leaf diseases using neural networks. In: Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp 1–5
Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D (2016) Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci 2016(3289801):1–11
Mokhtar U, Ali MA, Hassanien AE, Hefny H (2015) Identifying two of tomatoes leaf viruses using support vector machine. In: Satapathy SC, Bhateja V, Somanah R, Yang X-S, Senkerik R (eds) Information systems design and intelligent applications. Springer, Berlin, pp 771–782
Bharti NS, Mulajkar RM (2015) Detection and classification of plant diseases. Int Res J Eng Technol (IRJET) 2(02):2267–2272
Akhtar A, Khanum A, Khan SA Shaukat A (2013) Automated plant disease analysis (APDA): performance comparison of machine learning techniques. In: 2013 11th International Conference on Frontiers of Information Technology, pp 60–65
Dhingra G, Kumar V, Joshi HD (2019) A novel computer vision based neutrosophic approach for leaf disease identification and classification. Measurement 135:782–794
Gao L, Lin X (2018) A method for accurately segmenting images of medicinal plant leaves with complex backgrounds. Comput Electron Agric 155:426–445
Dey AK, Sharma M, Meshram MR (2016) Image processing based leaf rot disease, detection of betel vine (Piper BetleL.). Procedia Comput Sci 85:748–754
Landge PS, Patil SA, Khot DS, Otari OD, Malavkar U (2013) Automatic detection and classification of plant disease through image processing. Int J Adv Res Comput Sci Softw Eng 3(7):798–801
Athanikar G, Badar P (2016) Potato leaf diseases detection and classification system. Int J Comput Sci Mob Comput (IJCSMC) 5:76–78
Anami BS, Pujari JD, Yakkundimath R (2011) Identification and classification of normal and affected agriculture/horticulture produce based on combined color and texture feature extraction. Int J Comput Appl Eng Sci 1(3):356–360
Wang G, Sun Y, Wang J (2017) Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci 2017(2917536):1–8
Qin F, Liu D, Sun B, Ruan L, Ma Z, Wang H (2016) Identification of alfalfa leaf diseases using image recognition technology. PLoS ONE 11(12):e0168274
Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Frontiers in plant science 7:1419
Song YM, Noh S, Yu J, Park CW, Lee BG (2014) Background subtraction based on Gaussian mixture models using color and depth information. In: The International Conference on Control, Automation and Information Sciences (ICCAIS 2014), pp 132–135
Jiang M, Luo YP, Yang SY (2007) Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Information Processing Letters 102(1):8–16
Vijayalakshmi S, Murugan D (2018) comparative analysis on segmentation approaches for plant leaf disease detection. Int J Sci Res Eng Technol (IJSRET) 6(3):220–224
Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132
Kumar A, Kumar D, Jarial SK (2017) A hybrid clustering method based on improved artificial bee colony and fuzzy C-Means algorithm. Int J Artif Intell 15(2):24–44
Khac CN, Park JH, Jung HY (2009) Face detection using variance based Haar-like feature and SVM. World Acad Sci Eng Technol 60:165–168
Mohamed A, Issam A, Mohamed B, Abdellatif B (2015) Real-time detection of vehicles using the haar-like features and artificial neuron networks. Procedia Comput Sci 73:24–31
Chao-yang Z (2011) An improved binary image watermarking algorithm based on singular value decomposition. In: International Symposium on Intelligence Information Processing and Trusted Computing, pp 238–241
Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup and Workshop, vol 2007, pp 5–8
Keuper J, Preundt FJ (2016) Distributed training of deep neural networks: theoretical and practical limits of parallel scalability. In: Proceedings of the Workshop on Machine Learning in High Performance Computing Environments, pp 19–26
Karim AM, Güzel MS, Tolun MR, Kaya H, Çelebi FV (2018) A new generalized deep learning framework combining sparse autoencoder and Taguchi method for novel data classification and processing. Math Probl Eng 2018(3145947):1–13
Kan HX, Jin L, Zhou FL (2017) Classification of medicinal plant leaf image based on multi-feature extraction. Pattern Recognit Image Anal 27(3):581–587
Uluturk C, Ugur A (2012) Recognition of leaves based on morphological features derived from two half-regions. In: 2012 International Symposium on Innovations in Intelligent Systems and Applications, pp 1–4
Pravin Kumar SK, Sumithra MG, Saranya N (2019) Particle swarm optimization (PSO) with fuzzy c means (PSO‐FCM)–based segmentation and machine learning classifier for leaf diseases prediction. Concurr Comput Pract Exp. https://doi.org/10.1002/cpe.5312
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pravin Kumar, S.K., Sumithra, M.G. & Saranya, N. Artificial bee colony-based fuzzy c means (ABC-FCM) segmentation algorithm and dimensionality reduction for leaf disease detection in bioinformatics. J Supercomput 75, 8293–8311 (2019). https://doi.org/10.1007/s11227-019-02999-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-019-02999-z