Timely estimation and diagnosis of crop diseases is very critical for the higher yield of the crop. In current scenario identification of disease is done by few experts available having experience and most of the cases farmers generally rely on insecticide/pesticide vendors advices which is generally focused towards profit making rather than scientific approach. Another case is many farmers try to apply the successful case of their neighbor farmers irrespective of neighbor's various important factors like type and age of crop, type of diseases, type soil etc as these factors are having very crucial. This approach is not a right approach in scientific view but this is the common practice in Indian agriculture system. The main reason for this is unavailability of the experts and lack of the resources. Our aim is to bridge this gap of knowledge and availability of expertise by developing the self learning system. During our interactions with farmers we understood the reason for non scientific approach due to a lack of knowledge or lack of availability of expertise. The objective of the paper is to address both the issue by making a self learning system to bridge the gap. The interpretation, analysis of defected image content is subjective. A self learning approach by automating symptoms detection will help to address the problems effectively. This self learning system has many secondary advantages beside the diagnosis like identification of severity of diseased crop, crop management system, reminder and information system, advisory system etc. Automated system will ensure accurate and timely diagnosis as per need.

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Automated Disease Diagnosis Of Crops Self Learning Approach Machine Vision In Agriculture