Image Processing Techniques for Diagnosing Rice Plant Disease: A Survey

Abstract Over the past decades, rice crops are crucially admitted as one of the powerful energy streams for the production of resources. Rice plant diseases are considered as a raising factor behind the agricultural, economic and communal loss in the upcoming development of the agricultural field. Since last 10 years diagnosis of plant disease in approach to image processing techniques have remained keen are of interest among the researcher. A number of disease detection, identification and quantification methods have been developed and applied in a wide variety of crops. This paper reviews related research papers from the period between 2007 and 2018 with a focus on the development of state of the art. The related studies are compared based image segmentation, feature extraction, feature selection and classification. This paper also outlines the current achievements, limitations, and suggestions for future research associated with the diagnosis of rice plant diseases.

[1]  Lei Shu,et al.  Rice blast recognition based on principal component analysis and neural network , 2018, Comput. Electron. Agric..

[2]  Kendall R. Kirk,et al.  Estimation of soybean leaf area, edge, and defoliation using color image analysis , 2018, Comput. Electron. Agric..

[3]  Cheng Fang,et al.  Machine Vision Analysis of Characteristics and Image Information Base Construction for Hybrid Rice Seed , 2005 .

[4]  Asit Kumar Das,et al.  Rice diseases classification using feature selection and rule generation techniques , 2013 .

[5]  S. Arivazhagan,et al.  Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features , 2013 .

[6]  Jayme Garcia Arnal Barbedo,et al.  Digital image processing techniques for detecting, quantifying and classifying plant diseases. , 2013 .

[7]  Alice N. Cheeran,et al.  Color Transform Based Approach for Disease Spot Detection on Plant Leaf , 2012 .

[8]  Yang Rui,et al.  Detecting sugarcane borer diseases using support vector machine , 2017 .

[9]  Jinsong Deng,et al.  Identification of Nitrogen, Phosphorus, and Potassium Deficiencies in Rice Based on Static Scanning Technology and Hierarchical Identification Method , 2014, PloS one.

[10]  Asit Kumar Das,et al.  Particle Swarm Optimization based incremental classifier design for rice disease prediction , 2017, Comput. Electron. Agric..

[11]  Nilamani Bhoi,et al.  Detection of Healthy and Defected Diseased Leaf of Rice Crop using K-Means Clustering Technique , 2017 .

[12]  Isaac Kofi Nti,et al.  Detection of Plant Leaf Disease Employing Image Processing and Gaussian Smoothing Approach , 2017, International Journal of Computer Applications.

[13]  Long Qi,et al.  Hyperspectral image analysis based on BoSW model for rice panicle blast grading , 2015, Comput. Electron. Agric..

[14]  A. K. Misra,et al.  Detection of plant leaf diseases using image segmentation and soft computing techniques , 2017 .

[15]  Jian Tang,et al.  Automated Counting of Rice Planthoppers in Paddy Fields Based on Image Processing , 2014 .

[16]  Edward Jones,et al.  Automatic crop detection under field conditions using the HSV colour space and morphological operations , 2017, Comput. Electron. Agric..

[17]  Yang Zhang,et al.  Rice plant-hopper infestation detection and classification algorithms based on fractal dimension values and fuzzy C-means , 2013, Math. Comput. Model..

[18]  Yang Lu,et al.  Identification of rice diseases using deep convolutional neural networks , 2017, Neurocomputing.

[19]  Chao Zhang,et al.  Automated detection and identification of white-backed planthoppers in paddy fields using image processing , 2017 .

[20]  Surekha Borra,et al.  Machine Learning Based Plant Leaf Disease Detection and Severity Assessment Techniques: State-of-the-Art , 2018 .

[21]  Xuebing Bai,et al.  A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images , 2017, Comput. Electron. Agric..

[22]  Saurabh Maheshwari,et al.  Performance Analysis of Classifiers and Future Directions for Image Analysis Based Leaf Disease Detection , 2018 .

[23]  Jingfeng Huang,et al.  Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification , 2010, Journal of Zhejiang University SCIENCE B.