A CNN Based System For Tomato Disease Detection
Abstract
Rapid human population growth requires
corresponding increase in food production. Easily
spreadable diseases can have a strong negative
impact on plant yields and even destroy whole
crops. That is why early disease diagnosis and
prevention are of very high importance. Traditional
methods rely on lab analysis and human expertise
which are usually expensive and unavailable in a
large part of the undeveloped world. Since
smartphones are becoming increasingly present
even in the most rural areas, in recent years
scientists have turned to automated image analysis
as a way of identifying crop diseases. This paper
presents the most recent results in this field, and a
comparison of deep learning approach with the
classical machine learning algorithms. One of the
important and tedious task in agricultural practices
is detection of disease on crops. It requires huge
time as well as skilled labor. This paper proposes a
smart and efficient technique for detection of crop
disease which uses computer vision and machine
learning techniques. The proposed system is able to
detect 20 different diseases of 5 common plants with
93% accuracy.
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