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Comparison of Various Methodologies used for Automating Plant Disease Detection using Image Processing

Divyansh Sharma, Pawan Kumar Patidar

Abstract


In India, major of economy and development depends on agriculture, so plant disease detection become
important for us so that if any crop is destroyed due to diseases is known to us prior and we can overcome
the loss of crops due to plant diseases. It is better to catch the disease in the initial phase so that the largescale
damage of crops can be prevented. Traditionally, the diseases incurred via the naked eyes and then
there is manual processing on the plants to overcome the diseases. Manual process takes lots of time, and
it is expensive and required lots of experience. So in order to increase the testing speed, we automate the
methodology. So we are using image processing techniques with the combination of various other
technologies like deep learning, IoT, etc. to reduce the effort and automate the things. This paper has a
vision to promote the sustainable development and implies the idea of smart city which includes the
combination of IoT (Internet of things, deep-learning, big data, etc.) in the area of agriculture. This paper
includes the comparison of various plant disease detection methods of various techniques.


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References


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