Boost Yields, Save Crops: How Machine Learning Can Help Farmers Combat Disease (eBook, ePUB)
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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 vitalaspect of enhancing agricultural productivity. Lack of proper care can cause serious effects onplants 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, massstarvation, emigration and death. Biologists later arrived at a conclusion that the famine wascaused by a potato blight, a natur...
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.
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.
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