Paper Title : PLANT LEAF SHAPE CLUSTERING AND ROT DETECTION
ISSN : 2394-2231
Year of Publication : 2021
MLA Style: Ms. N. Zahira Jahan MCA., M.Phil., P. Karuppusamy "PLANT LEAF SHAPE CLUSTERING AND ROT DETECTION " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
APA Style: Ms. N. Zahira Jahan MCA., M.Phil., P. Karuppusamy "PLANT LEAF SHAPE CLUSTERING AND ROT DETECTION " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
Leaf disease detection plays a vital role in agricultural field. However, it requires huge manpower, more processing time and extensive knowledge about plant diseases. Hence, machine learning is applied to detect diseases in plant leaves as it analyses the data from different aspects, and classifies it into one of the predefined set of classes. The first effort in learning about plants is observing plant features. This project developed a plant search system that allows users to do a search even when they do not know the plant name simply by observing plant characteristics. The system consists of a plant features, searches for the features according to the input features, and returns the leaves with selected clusters. At present, leaf classification uses machine vision to extract and analyse colour, size, shape, and surface texture. However, the proposed extraction margin method can only be carried out roughly and there is still a difference between the margin of the extracted shape, polygon, and the margin of the shape of the original image. This project clusters the leaves using image area size, pixel colour values similarity, based on brightness values of the image and leaf shapes. In addition, the project aims in finding the rots in the leaves. Based on the count of pixels of rot colours, the total rot percent in the leaf is calculated and displayed. This assists in evaluating the leaf quality. If future researchers were to expand to other features, leaf apex, etc., even those that are hard to quantify, can also be quantified. The project is designed using R Studio 1.0. The coding language used is R 3.4.4
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—Artificial Neural Network, Classification, Disease Detection, Support Vector Machine, Machine Learning.