Ail Detect - Ornamental Plant Disease Detection using Machine Learning Algorithms
Author(s):
Ms. Yuvasri S, Mrs. R Vidhya, Ms. D.Abitha
Keywords:
Linear Regression, Accuracy, Feature extraction, Image recognition, Image Processing, Machine Learning, Supervised Learning, Unsupervised Learning.
Abstract
Plant disease identification by visual approach is a lot of effortful task and at constant time less correct and may be done solely in restricted areas. Whereas if automatic detection technique is employed it'll take less efforts, less time and a lot of correctness. In plants, some general diseases are brown and yellow spots, or early and late scorch, and different flora, infectious agent and microorganism diseases. Image process is that the technique that is employed for measurement affected space of unwellness, and to work out the distinction within the color of the affected space. Image classification refers to the task of extracting info categories from a multiband formation image. The ensuing formation from image classification is accustomed produce thematic maps. looking on the interaction between the analyst and also the pc throughout classification there are 2 kinds of classification. i) supervised and ii) unsupervised. a brand-new image recognition system supported multiple regression is projected. significantly, there are variety of innovations in image segmentation and recognition system. Meanwhile, the regional growth technique and true color image process are combined with this method to enhance the accuracy and intelligence. whereas making the popularity system, multiple regression and image feature extraction are used. once evaluating the results of various image coaching libraries, the system is evidenced to own effective image recognition ability, high exactness, and responsibility.
Article Details
Unique Paper ID: 151085

Publication Volume & Issue: Volume 7, Issue 11

Page(s): 584 - 588
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