Boost Yields, Save Crops: How Machine Learning Can Help Farmers Combat Disease

It is a well-known fact that economy of a nation highly depends on agricultural productivity.

Occurrence of diseases in plants is quite natural. Thus, detection of diseases in plants forms a vital

aspect of enhancing agricultural productivity. Lack of proper care can cause serious effects on

plants which in turn affects quality, quantity or productivity of the plant product. For example,

The Great Famine (1845-1849) was a period of crop failure which led to diseases, mass

starvation, emigration and death. Biologists later arrived at a conclusion that the famine was

caused by a potato blight, a natural event. Death was widespread and the toll rose to a 100,000.

Automatic disease detection techniques can be used on crops in big farms, which will help reduce

the manual monitoring of crops and detect disease or their symptoms at very early stages. This

gives time for proper remedy. This paper presents an application with machine learning algorithms

to detect and classify diseases in tomato plants by processing images of the leaves.