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dc.contributor.authorSiddiqui, Mohammad Junaid
dc.contributor.authorShaikh, Umair (15DCE71)
dc.contributor.authorQais, Mohd. Hamid (15DCE66)
dc.contributor.authorKhan, Aminuddin B. (15DCE63)
dc.contributor.authorAnsari, Md. Aasif (14CE09)
dc.date.accessioned2019-09-06T04:53:48Z
dc.date.available2019-09-06T04:53:48Z
dc.date.issued2018-05
dc.identifier.urihttp://www.aiktcdspace.org:8080/jspui/handle/123456789/3246
dc.descriptionSubmitted in partial fulfillment of the requirements for the degree of Bachelor of Engineering 2019en_US
dc.description.abstractIndia, the country where the main source of income is from agriculture. Farmers grow a variety of crops based on their requirement. Since the plants su er from the disease, the production of crop decreases due to infections caused by several types of diseases on its leaf, fruit, and stem. Leaf diseases are mainly caused by bacteria, fungi, virus etc. Diseases are often di cult to control. Diagnosis of the disease should be done accurately and proper actions should be taken at the appropriate time. Image Processing is the trending technique in detection and classi cation of plant leaf disease. This work describes how to automatically detect leaf diseases. The given system will provide a fast, spontaneous, precise and very economical method in detecting and classifying leaf diseases. This paper is envisioned to assist in the detecting and classifying leaf diseases using Multiclass SVM classi cation technique. First, the a ected region is discovered using segmentation by K- means clustering, then features (color and texture) are extracted. Lastly, classi cation technique is applied in detecting the type of leaf disease. Keywords: Image Processing, Leaf diseases detection, K-means clustering, feature extraction, Multiclass SVM Classi cation.en_US
dc.language.isoenen_US
dc.publisherAIKTCen_US
dc.relation.ispartofseriesPE0449;
dc.subjectProject Report - EXTCen_US
dc.titleEffect of rate of loading on the compressive strength of concreteen_US
dc.typeProject Reporten_US


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