Measuring the severity of fungi caused disease on leaf using triangular thresholding method

dc.contributor.authorMarfo, Richard
dc.date.accessioned2017-01-18T08:49:01Z
dc.date.accessioned2023-04-19T12:28:31Z
dc.date.available2017-01-18T08:49:01Z
dc.date.available2023-04-19T12:28:31Z
dc.date.issuedJUNE, 2016.
dc.descriptionA Thesis submitted to Department of Computer Science, Kwame Nkrumah University of Science and Technology in partial fulfillment of the requirement for the degree of Master of Science in Information Technology.en_US
dc.description.abstractIn recent years, agriculture has become much more important than it used to be some years back where plants were only used to feed humans and animals. This is due to the fact that plants are now used to generate electricity and other forms of energy to improve living conditions of humans. For this reason, there is the need to take proper care of plants in order to get the maximum benefits from them. One major area that needs urgent attention is curbing plant diseases. There are several diseases that affect plants that can cause great harm to various economies and societies. It can even lead to great ecological losses. For this reason, it is better to diagnose plant diseases accurately and timely to avoid such loses. Fungi caused diseases in plants are the most common diseases which appear as spots on plant leaves. These spots make it very difficult for such plants to prepare their food by means of Photosynthesis since they affect the green pigments (chlorophyll) in the leaf, hence to a large extent affects the growth and the yield of such plants. In case of severe infection, the leaf becomes totally covered with spots and this leads to the withering of the plant. Plant disease can be detected and severity estimated using Thresholding and Segmentation. This paper employs the Triangular thresholding method with a written algorithm to detect and measure the severity of fungi caused disease on leaves. It can also be used to detect diseases caused by bacterial on plant leaves. The algorithm is estimated to give up to about 97% accurate results.en_US
dc.description.sponsorshipKNUSTen_US
dc.identifier.urihttps://ir.knust.edu.gh/handle/123456789/9922
dc.language.isoenen_US
dc.titleMeasuring the severity of fungi caused disease on leaf using triangular thresholding methoden_US
dc.typeThesisen_US
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