Abstract
In traditional practices, detection of rice plant diseases by experts is a subjective matter whereas by testing in the laboratory is time-consuming. As a consequence, it causes reduction on agricultural production and economic loss to farmers. To overcome this, there is a demand to develop fast and effective systems to detect and classify rice plant diseases. Therefore, the development of image-based automated systems for classification of rice plant diseases is an interesting growing research area in the agriculture domain. Color is one of the important features to classify rice plant diseases. In this study, we have presented an image-based rice plant disease classification approach using color features only. We have explored 14 different color spaces and extracted four features from each color channel leading to 172 features. Moreover, the performance of seven different classifiers have been compared and demonstrated that a highest classification accuracy of 94.65% has been achieved using support vector machine (SVM) classifier. Training and testing of models were performed on the dataset that consists of 619 images. This dataset was collected from the real agriculture field that belongs to four classes: (a) Bacterial Leaf Blight (BLB), (b) Rice Blast (RB), (c) Sheath Blight (SB) and (d) Healthy Leave (HL). The encouraging results of this paper show that color features can play an important role in developing rice plant disease classification system and enable the farmers to take preventive measures resulting in better product quality and quantity.
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References
Agrios GN (2005) Plant Pathology, 5th edn. Elsevier Academic Press. Burlington, MA, pp 79–103
Barbedo JGA (2013) Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus 2(1):660
Barbedo JGA (2016) A review on the Main challenges in automatic plant disease identification based on visible range images. Biosyst Eng 144:52–60
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Cheeseman P, Stutz J (1996) Bayesian classification (AutoClass): theory and results. Advances in Knowledge Discovery and Data Mining, pp. 153–180
Chen J, Zhang D, Nanehkaran YA, Li D (2020) Detection of rice plant diseases based on deep transfer learning. J Sci Food Agric 100(7):3246–3256
Duda RO, Hart PE (1973) Pattern recognition and scene analysis. Wiley, New York
Duda RO, Hart PE, Stork DG (2000) Pattern classification and scene analysis part 1: pattern classification. Wiley, Chichester
Gianessi L (2014) Importance of pesticides for growing Rice in south and South East Asia. International pesticide benefit case study 108
Johannes A, Picon A, Alvarez-Gila A, Echazarra J, Rodriguez-Vaamonde S, Díez NA, Ortiz-Barredo A (2017) Automatic plant disease diagnosis using Mobile capture devices, applied on a wheat use case. Comput Electron Agric 138:200–209
Joshi AA, Jadhav BD (2016) Monitoring and controlling rice diseases using image processing techniques. In: 2016 International Conference on Computing, Analytics and Security Trends (CAST). IEEE, pp 471–476
Kamal KC, Yin Z, Wu M, Wu Z (2019) Depthwise separable convolution architectures for plant disease classification. Comput Electron Agric 165:104948
Khan MS, Uandai SB, Srinivasan H (2019) Anthracnose disease diagnosis by image processing, support vector machine and correlation with pigments. J Plant Pathol 101:749–751
Kumar A, Mishra AK, Jain AK (2016) In-silico identification of inhibitors for controlling rice blast. In: 2016 3rd international conference on computing for sustainable global development (INDIACom). IEEE, pp 1888–1892
Lee FN, Rush MC (1983) Rice sheath blight: a major rice disease. Plant Dis 67:829–832
Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378–384
Majid K, Herdiyeni Y, Rauf A (2013) I-PEDIA: mobile application for paddy disease identification using fuzzy entropy and probabilistic neural network. In: 2013 International conference on advanced computer science and information systems (ICACSIS). IEEE, pp 403–406
Mitchell TM (1997) Machine learning, vol 45 (37). McGraw Hill, Burr Ridge 45(37):870–877
Patil B, Jagadeesh GB, Karegowda C, Naik S, Revathi RM (2017) Management of bacterial leaf blight of rice caused by Xanthomonas oryzae pv. oryzae under field condition. J Pharmacogn Phytochem 6(6):244–246
Phadikar S, Sil J, Das AK (2013) Rice diseases classification using feature selection and rule generation techniques. Comput Electron Agric 90:76–85
Prajapati HB, Shah JP, Dabhi VK (2017) Detection and classification of Rice Plant diseases. Decis Technol 11(3):357–373
Qin Z, Zhang M (2005) Detection of Rice sheath blight for in-season disease management using multispectral remote sensing. Int J Appl Earth Obs Geoinf 7(2):115–128
Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106
Rahman CR, Arko PS, Ali ME, Khan MAI, Apon SH, Nowrin F, Wasif A (2020) Identification and recognition of rice diseases and pests using convolutional neural networks. Biosyst Eng 194:112–120
Rajmohan R, Pajany M, Rajesh R, Raman DR, Prabu U (2018) Smart Paddy crop disease identification and management using deep convolution neural network and SVM classifier. Int J Pure Appl Math 118(15):255–264
Rezaei S, Tavakolian K, Naziripour K (2006) Comparison of five different classifiers for classification of mental tasks. In: 2005 IEEE engineering in medicine and biology 27th annual conference. IEEE, pp 6007–6010
Saleem MH, Potgieter J, Arif KM (2019) Plant disease detection and classification by deep learning. Plants 8(11):468
Sanyal P, Patel SC (2008) Pattern recognition method to detect two diseases in rice plants. Imaging Sci J 56(6):319–325
Shah JP, Prajapati HB, Dabhi VK (2016) A Survey on detection and classification of rice plant diseases. In: 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC). IEEE, pp 1–8
Shrivastava S, Singh SK, Hooda DS (2014) Color sensing and image processing-based automatic soybean plant foliar disease severity detection and estimation. Multimed Tools Appl 74(24):11467–11484
Shrivastava VK, Londhe ND, Sonawane RS, Suri JS (2015) Exploring the color feature power for psoriasis risk stratification and classification: a data mining paradigm. Comput Biol Med 65:54–68
Shrivastava S, Singh SK, Hooda DS (2017) Soybean plant foliar disease detection using image retrieval approaches. Multimed Tools Appl 76(24):26647–26674
Shrivastava VK, Pradhan MK, Minz S, Thakur MP (2019) Rice plant disease classification using transfer learning of deep convolution neural network. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 423:631–635
Singh AK, Ganapathysubramanian B, Sarkar S, Singh A (2018) Deep learning for plant stress phenotyping: trends and future perspectives. Trends Plant Sci 23(10):883–898
Suresha M, Shreekanth KN, Thirumalesh BV (2017) Recognition of diseases in paddy leaves using Knn classifier. In: 2017 2nd international conference for convergence in technology (I2CT). IEEE, pp 663–666
Thomas S, Kuska MT, Bohnenkamp D, Brugger A, Alisaac E, Wahabzada M, Mahlein AK (2018) Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. J Plant Dis Prot 125(1):5–20
Tolles J, Meurer WJ (2016) Logistic regression: relating patient characteristics to outcomes. JAMA 316(5):533–534
Vapnik V, Cortes C (1995) Support vector networks. Mach Learn 20:273–297. Kunwer Acedemic Publisher
Verma T, Dubey S (2019) Impact of color spaces and feature sets in automated plant diseases classifier: a comprehensive review based on rice plant images. Arch Comput Methods Eng 27:1611–1632
Xiao M, Ma Y, Feng Z, Deng Z, Hou S, Shu L, Lu Z (2018) Rice blast recognition based on principal component analysis and neural network. Comput Electron Agric 154:482–490
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Shrivastava, V.K., Pradhan, M.K. Rice plant disease classification using color features: a machine learning paradigm. J Plant Pathol 103, 17–26 (2021). https://doi.org/10.1007/s42161-020-00683-3
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DOI: https://doi.org/10.1007/s42161-020-00683-3