Rice plant disease classification using color features: a machine learning paradigm
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ORIGINAL ARTICLE
Rice plant disease classification using color features: a machine learning paradigm Vimal K. Shrivastava 1 & Monoj K. Pradhan 2 Received: 20 December 2019 / Accepted: 9 October 2020 # Società Italiana di Patologia Vegetale (S.I.Pa.V.) 2020
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. Keywords Agriculture . Color features . Classification . Feature extraction . Machine learning . Rice plant diseases
Introduction Rice is a staple food in India and worldwide. Over half of the world population depends on rice food (Qin and Zhang 2005) and it has been estimated that there will be more than nine billion people in the world by 2050 (Johannes et al. 2017). Moreover, a loss of 10–15% in rice production occurs due to rice plant diseases (Gianessi 2014). Hence, it is a big challenge before the agricultural community to ensure the food security of such a large population. It is believed that the primary reasons for these diseases are fungus and bacteria. These
* Monoj K. Pradhan [email protected] Vimal K. Shrivastava [email protected] 1
School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India
2
Department of Agricultural Statistics and Social Sciences (L), Indira Gandhi Agricultural University, Raipur, India
diseased rice plants result in a reduction of rice production which may lead to great economic loss to the farmers every year. Hence, the diagnosis of diseases in agricultural products in their earlier stage is essenti
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