Abstract
Rice is a major food crop that plays an important role in the Indian economy. It is the most consumed staple food, greatly in demand in the market to meet the requirements of a growing population, which is only possible with increased production. To meet this demand, rice production should be increased. To maximize crop productivity, measures must be taken to eradicate rice plant diseases, namely, brown spot, bacterial leaf blight, and rice blast. In the proposed method, the modified K-means segmentation algorithm is used to separate the targeted region from the background of the rice plant image. Following segmentation, features are extracted through the three parameters of color, shape and texture. A novel intensity-based color feature extraction (NIBCFE) proposed method is used to extract color features, while the texture features are identified from the gray-level cooccurrence matrix (GLCM) and bit pattern features (BPF), and the shape features are extracted by finding the area and diameter of the infected portions. Thereafter, unique feature values are identified through the novel support vector machine-based probabilistic neural network (NSVMBPNN) to classify the images. A comparison in terms of performance is made using three classifiers, namely naïve Bayes, support vector machine and probabilistic neural network. This proposed method achieved better accuracy than the other three methods based on different performance measures. Finally, the result was validated under the fivefold cross-validation method with final accuracies of 95.20%, 97.60%, 99.20% and 98.40% for bacterial leaf blight, brown spot, healthy leaves and rice blast, respectively.
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Abu Bakar MN, Abdullah AH, Abdul Rahim N, Yazid H, Misman SN, Masnan MJ (2018) Rice leaf blast disease detection using multi-level color image thresholding. J Telecomm, Electr Comp Eng 10(1–15):1–6
Archana KS, Sahayadhas A (2018) Comparison of various filters for noise removal in paddy leaf images. Int J Eng Technol 7(2):372–374
Archana KS, Sahayadhas A (2018) Automatic rice leaf disease segmentation using image processing techniques. Int J Eng Technol (UAE) 7(3.27):182–185. https://doi.org/10.14419/ijet.v7i3.27.17756
Archana KS, Sahayadhas A (2019) Computer vision for predicting unhealthy region of rice leaves - a review. Ind J Environm Prot 39(7):609–613
Barbedo JGA (2017) A new automatic method for disease symptom segmentation in digital photographs of plant leaves. Eur J Plant Pathol. https://doi.org/10.1007/s10658-016-1007-6
Bauer SD, Korč F, Förstner W (2011) The potential of automatic methods of classification to identify leaf diseases from multispectral images. Precision Agric 12(3):361–377. https://doi.org/10.1007/s11119-011-9217-6
Caglayan, A., Guclu, O., & Can, A. B. (2013). A plant recognition approach using shape and color features in leaf images. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8157 LNCS(PART 2), 161–170. https://doi.org/10.1007/978-3-642-41184-7_17
Chouhan SS, Kaul A, Singh UP, Jain S (2018) Bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards plant pathology. IEEE Access 6:8852–8863. https://doi.org/10.1109/ACCESS.2018.2800685
Garcia J, Barbedo A, Koenigkan LV (2016) ScienceDirect Identifying multiple plant diseases using digital image processing. Biosys Eng 147:104–116. https://doi.org/10.1016/j.biosystemseng.2016.03.012
Gayathri Devi T, Neelamegam P (2018) Image processing based rice plant leaves diseases in Thanjavur. Cluster Computing, Tamilnadu. https://doi.org/10.1007/s10586-018-1949-x
Hamouchene I, Aouat S, Lacheheb H (2014) Intelligent Systems for Science and Information 542:389–407. https://doi.org/10.1007/978-3-319-04702-7
Hussein MA, Abbas AH (2018) Comparison of features extraction algorithms used in the diagnosis of plant diseases. Ibn AL- Haitham J Pure Appl Sci. https://doi.org/10.30526/2017.ihsciconf.1785
Jagan K, Balasubramanian M, Palanivel S (2016) Detection and recognition of diseases from paddy plant leaf images. Int J Comput Appl 144(12):34–41. https://doi.org/10.5120/ijca2016910505
Kanagalakshmi, K., & Chandra, E. (2011). Performance evaluation of filters in noise removal of the fingerprint image. ICECT 2011 - 2011 In: 3rd International conference on electronics computer technology, 1, 117–121. https://doi.org/10.1109/ICECTECH.2011.5941572
Kaur A, Bhardwaj V (2018) Rice Plant Disease Detection Based on Clustering and Binarization 5(4):245–249
Krstajic D, Buturovic LJ, Leahy DE, Thomas S (2014) Cross-validation pitfalls when selecting and assessing regression and classification models. J Cheminform 6(1):1–15. https://doi.org/10.1186/1758-2946-6-10
Latha A, Prasanna S, Hemalatha S, Sivakumar B (2019) A harmonized trust assisted energy efficient data aggregation scheme for distributed sensor networks. Cogn Syst Res 56(March):14–22. https://doi.org/10.1016/j.cogsys.2018.11.006
Majumdar, D., Kole, D. K., Chakraborty, A., Dutta Majumder, D., & Majumder, D. D. (2014). Review: detection & diagnosis of plant leaf disease using integrated image processing approach. Int J Comput Eng Appl, VI
Munisami T, Ramsurn M, Kishnah S, Pudaruth S (2015) Plant Leaf Recognition using shape features and colour histogram with k-nearest neighbour classifiers. Procedia Comput Sci 58:740–747. https://doi.org/10.1016/j.procs.2015.08.095
Nalini S, Krishnaraj N, Jayasankar T, Vinothkumar K, Sagai A et al (2021) Paddy leaf disease detection using an optimized deep neural network. Comput Mater Continua 68(1):1117–1128
Patil PSP, Zambre MRS (2014) Classification of cotton leaf spot disease using support vector machine 4(5):92–97
Phadikar S, Sil J, Das AK (2013) Rice disease classification using feature selection and rule generation techniques. Comput Electron Agric 90:76–85. https://doi.org/10.1016/j.compag.2012.11.001
Rawat P, Singh KD, Chaouchi H et al (2014) Wireless sensor networks: a survey on recent developments and potential synergies. J Supercomput 68:1–48
Rishi, N., & Gill, J. S. (2015). Detection and Classification of Plant Diseases by Image ProcessingRishi, N., & Gill, J. S. (2015). Detection and Classification of Plant Diseases by Image Processing. An Overview on Detection and Classification of Plant Diseases in Image Processing, 3(5),. An Overview on Detection and Classification of Plant Diseases in Image Processing, 3(5), 114–117.
Sanjeevi P, Prasanna S, Siva Kumar B, Gunasekaran G, Alagiri I, Vijay Anand R (2020) Precision agriculture and farming using Internet of Things based on wireless sensor network. Trans Emerg Telecommun Technol. 31(2):1–14. https://doi.org/10.1002/ett.3978
Sanjeevi P, Siva Kumar B, Prasanna S, Maruthupandi J, Manikandan R, Baseera A (2020) An ontology enabled internet of things framework in intelligent agriculture for preventing post-harvest losses. Complex Intell Syst. https://doi.org/10.1007/s40747-020-00183-y
Sarangi S, Umadikar J, Kar S (2016) Automation of agriculture support systems using Wisekar: a case study of a crop-disease advisory service. Comput Electron Agric 122:200–210. https://doi.org/10.1016/j.compag.2016.01.009
Sharma, A., & Dey, S. (2012). A comparative study of feature selection and machine learning techniques for sentiment analysis, 1. https://doi.org/10.1145/2401603.2401605
Shrivastava S, Singh SK, Hooda DS (2015) Color sensing and image processing-based automatic soybean plant foliar disease severity detection and estimation. Multimed Tools Appl 74(24):11467–11484. https://doi.org/10.1007/s11042-014-2239-0
Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Inform Processi Agricul. https://doi.org/10.1016/j.inpa.2016.10.005
Sivakumar B, Sowmya B (2016) An energy efficient clustering with delay reduction in data gathering (EE-CDRDG) using mobile sensor node. Wireless Pers Commun 90(2):793–806. https://doi.org/10.1007/s11277-016-3214-z
Varma, P. (2017). Rice productivity and food security in India: A study of the system of rice intensification. Rice Productivity and Food Security in India: A Study of the System of Rice Intensification, https://doi.org/10.1007/978-981-10-3692-7
Varshney S (2016) Plant disease prediction using image processing techniques-a review. Int J Comput Sci Mob Comput 55(5):394–398
William, J.,O., Cruz, J. Dela, L. A., Jensen, P., S., & Valenzuela, I. (2013). Information technology communication and control, environment and management (HNICEM) The Institute of Electrical and Electronics Engineers Inc. (IEEE)-Philippine Section 12–16. Nanotechnology.
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Archana, K.S., Srinivasan, S., Bharathi, S.P. et al. A novel method to improve computational and classification performance of rice plant disease identification. J Supercomput 78, 8925–8945 (2022). https://doi.org/10.1007/s11227-021-04245-x
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DOI: https://doi.org/10.1007/s11227-021-04245-x