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Computational Intelligence for Pathological Issues in Precision Agriculture

Computational Intelligence for Pathological Issues in Precision Agriculture

Sanjeev S. Sannakki, Vijay S. Rajpurohit, V. B. Nargund, Arun R. Kumar, Prema S. Yallur
Copyright: © 2013 |Pages: 24
ISBN13: 9781466639942|ISBN10: 1466639946|EISBN13: 9781466639959
DOI: 10.4018/978-1-4666-3994-2.ch043
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MLA

Sannakki, Sanjeev S., et al. "Computational Intelligence for Pathological Issues in Precision Agriculture." Image Processing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2013, pp. 850-873. https://doi.org/10.4018/978-1-4666-3994-2.ch043

APA

Sannakki, S. S., Rajpurohit, V. S., Nargund, V. B., Kumar, A. R., & Yallur, P. S. (2013). Computational Intelligence for Pathological Issues in Precision Agriculture. In I. Management Association (Ed.), Image Processing: Concepts, Methodologies, Tools, and Applications (pp. 850-873). IGI Global. https://doi.org/10.4018/978-1-4666-3994-2.ch043

Chicago

Sannakki, Sanjeev S., et al. "Computational Intelligence for Pathological Issues in Precision Agriculture." In Image Processing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 850-873. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3994-2.ch043

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Abstract

Plant Pathology is the scientific study of plant diseases, caused by pathogens and environmental conditions (physiological factors). Detection and grading of plant diseases by machine vision is an essential research topic as it may prove useful in monitoring large fields of crops. This can be of great benefit to those users, who have little or no information about the crop they are growing. Also, in some developing countries, farmers may have to go long distances to contact experts to dig up information which is expensive and time consuming. Therefore, looking for a fast, automatic, less expensive, and accurate method to detect plant diseases is of great realistic significance. Such an efficient system can be modeled by integrating the various tools/techniques of information and communication technology (ICT) in agriculture. The objective of the present chapter is to model an intelligent decision support system for detection and grading of plant diseases which encompasses image processing techniques and soft computing/machine learning techniques.

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