House Plant Leaf Disease Detection and Classification Using Machine Learning

House Plant Leaf Disease Detection and Classification Using Machine Learning

Bhimavarapu Usharani
Copyright: © 2022 |Pages: 10
DOI: 10.4018/978-1-7998-8161-2.ch002
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Abstract

Hibiscus is a fantastic herb, and in Ayurveda, it is one of the most renowned herbs that have extraordinary healing properties. Hibiscus is rich in vitamin C, flavonoids, amino acids, mucilage fiber, moisture content, and antioxidants. Hibiscus can help with weight loss, cancer treatment, bacterial infections, fever, high blood pressure, lower body temperature, treat heart and nerve diseases. Automatic leaf disease detection is an essential task. Image processing is one of the popular techniques for the plant leaf disease detection and categorization. In this chapter, the diseased leaf is identified by concurrent k-means clustering algorithm and then features are extracted. Finally, reweighted KNN linear classification algorithms have been used to detect the diseased leaves categories.
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Ii. Literature Survey

Leaf images are one of the most important resources for the recognition and categorisation of plant groups and their diseases. WanMohdFadzil et al (Emad Mohamed, et al.,2014) conversed a bug identification process for orchid plant shrubberies. The proposed procedure used the combination of several approaches of edge segmentation methods, morphological and filtering procedures cast-off for classifying given images into two bug class as black leaf spot and solar scorch.

Rong Zhou et al (Rong Zhou, et al.,2013), explained method to identity leaflet patch in sugar beet. The procedure implements cross methods for corresponding and support vector machine|(SVM). This technique uses R for segmentation, filtering and support vector machine(SVM) classifier for categorization.

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