Digital Image Processing

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A Literature Survey on Detection of Leaf Disease in Plants


Affiliations
1 Department of School of Information Technology, RGPV, Bhopal, India
     

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According to the study and survey country India consist most of it parts as an agricultural part, hence given the name of agricultural country and about 70% of the people depends on agriculture for their survival. Early detection of leaf disease is very important research topic. Various numbers of disease caused by fungi, bacteria, nematodes etc. Disease in agriculture/horticulture crops causes a significant reduction in both quantity and quality of agriculture products. Early detection of disease and identification of symptoms of disease by naked eye is difficult for farmer which results the spreading of disease in whole crop. Detection of crops and its protection especially in large farms is done by using computerized image processing techniques by taking colour information of leaves. This paper presents a survey on early leaf disease detection by using image processing techniques.

Keywords

Artificial Intelligence, Colour Features, Image Processing, Leaf Diseases, Texture Features.
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  • Jagadeesh D Pujari, Rajesh Yakkundimath, Abdulmunaf S. Byadgi, “Image Processing Based Detection of Fungal Diseases in Plants”, International Conference on Information and Communication Technologies (ICICT 2014). Science Direct, Elsevier.

  • Pujari JD, Yakkundimath R, Byadagi AS. Automatic Fungal Disease Detection based on Wavelet Feature Extraction and PCA Analysis in Commercial Crops; International Journal of Image, Graphics and Signal Processing, 2014.

  • Al-Bashish, D., M. Braik and S. Bani-Ahmad, 2011. Detection and classification of leaf diseases using K-means-based segmentation and neural-networks-based classification, 2011.

  • Al- Hiary H, Bani-Ahmad S, Reyalat M, and ALRahamneh. Fast and Accurate Detection and Classification of Plant Diseases, 2013.

  • Huang KY , Application of Artificial Neutral Network for Detecting Phalaenopsis Seedling Diseases Using Color and Texture Features, 2007

  • Pujari JD, Yakkundimath R, Byadagi AS. Neuro-kNN Classification System for Detecting Fungal Disease affected on Vegetables using local binary patterns.

  • Guru DS, Mallikarjuna PB, Manjunath S. Segmentation and Classification of Tobacco Seedling Diseases, 2011.

  • Pujari JD, Yakkundimath R, Byadagi AS. Recognition and Classification of Normal and Affected Agriculture Produce Using Reduced Color and Texture Features, 2014.

  • Pujari JD, Yakkundimath R, Byadagi AS. Classification of Fungal Disease Symptoms affected on cereals using Color Texture Features. International Journal of Signal Processing, Image Processing and Pattern Recognition; 2013.

  • Pujari JD, Yakkundimath R, Byadagi AS. Statistical Methods for Quantitatively Detecting Fungal Disease from Fruits Images. International Journal of Intelligent Systems and Applications in Engineering; 2013.

  • Anand.H.Kulkarni, Ashwinpatil R.K. Applying Image Processing Techniques to detect plant diseases. Internantional journal of Modern Engineering Research; 2012.

  • Rumpf T. Mahlein AK, Steiner U, Oerke EC, Dehne HW, Plumer L. Early Detection and Classification of Plnat Diseases with Support Vector Mchines Bases on Hyperspectral Reflectance. Computers and Electronics in Agriculture; 2010.

  • Pranali Thorat, Somik Sutradhar, Ganesh Sanap, Meghna Singh, Prof. Anita Mahajan, “ Automatic Device for Analysing and Detecting Rotten and Infected Fruits” , International Journal of Innovative Research in Computer and Communication Engineering, 2016.

  • Raith Kartika Dewi, and R.V. Hari Ginardi, “Feature Extraction for Identification of Sugarcane Rust Disease,” IEEE International Conference on Information, Communication Technology and System (ICTS), Surabaya, pp 99-104, 2014.

  • Yuan Tian, Chunjiang Zhao, Shenglian Lu and Xinyu Guo,” SVM-based Multiple Classifier System for Recognition of Wheat Leaf Diseases,” Proceedings of 2010 conference on Dependable Computing (CDC’2010), November 20-22, 2010.

  • Tushar J. Haware, Ravindra D. Badgujar and Prashant G. Patil, “ Crop disease detection using image segmentation,” World Journal of Science and technology, ISSN 2231-2587, vol 2(4): 190-194, 2012.

  • Basvaraj .S. Anami , J.D. Pujari , Rajesh Yakkundimath, “Identifiaction and Calssification of Normal and Affected Agriculture/horticulture Produce Based on Combined Color and Texture Feature Extraction,” Internatinal Journal of Computer Applications in Engineering Sciences, Vol 1, Issue III, 2011.

  • Evy Kamilah Ratnasari, Mustika Mentari, Ratih Kartika Dewi and R.V. Hari Ginardi, “Sugarcane Leaf Diease Detection and Severity Estimation Based On Segmented Spots Image,” IEEE International Conference on Information, Communication Technology and System (ICTS), Surabaya, pp 93-98, 2014.


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  • A Literature Survey on Detection of Leaf Disease in Plants

Abstract Views: 388  |  PDF Views: 4

Authors

Anupma Mishra
Department of School of Information Technology, RGPV, Bhopal, India
Nishchol Mishra
Department of School of Information Technology, RGPV, Bhopal, India

Abstract


According to the study and survey country India consist most of it parts as an agricultural part, hence given the name of agricultural country and about 70% of the people depends on agriculture for their survival. Early detection of leaf disease is very important research topic. Various numbers of disease caused by fungi, bacteria, nematodes etc. Disease in agriculture/horticulture crops causes a significant reduction in both quantity and quality of agriculture products. Early detection of disease and identification of symptoms of disease by naked eye is difficult for farmer which results the spreading of disease in whole crop. Detection of crops and its protection especially in large farms is done by using computerized image processing techniques by taking colour information of leaves. This paper presents a survey on early leaf disease detection by using image processing techniques.

Keywords


Artificial Intelligence, Colour Features, Image Processing, Leaf Diseases, Texture Features.

References