An Algorithm for Severity Estimation of Plant Leaf Diseases by the Use of Colour Threshold Image Segmentation and Fuzzy Logic Inference: A Proposed Algorithm to Update a “Leaf Doctor” Application

This paper explains a proposed algorithm for severity estimation of plant leaf diseases by using maize leaf diseased samples. In the literature, a number of researchers have addressed the problem of plant leaf disease severity estimation, but a few, such as Sannakki et al., have used fuzzy logic to...

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Vydáno v:AgriEngineering Ročník 1; číslo 2; s. 205 - 219
Hlavní autoři: Sibiya, Malusi, Sumbwanyambe, Mbuyu
Médium: Journal Article
Jazyk:angličtina
Vydáno: 01.06.2019
Témata:
algorithms
color
corn
decision making
disease severity
foliar diseases
fuzzy logic
leaves
mobile telephones
precision agriculture
Zea mays
ISSN:2624-7402, 2624-7402
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Shrnutí:This paper explains a proposed algorithm for severity estimation of plant leaf diseases by using maize leaf diseased samples. In the literature, a number of researchers have addressed the problem of plant leaf disease severity estimation, but a few, such as Sannakki et al., have used fuzzy logic to determine the severity estimations of the plant leaf diseases. The present paper aims to update the current algorithm used in the “Leaf Doctor” application that is used to estimate the severities of the plant leaf diseases by introducing the benefits of fuzzy logic decision making rules. This method will contribute to precision agriculture technology as it introduces an algorithm that may be embedded in smartphone devices and used in applications, such as a “Leaf Doctor” application. The applications designed based on the algorithm proposed in this study will help users who are inexperienced and not plant pathologists understand the level of the estimated disease severity. The use of fuzzy logic inference rules along with image segmentation determines the novelty of this approach in comparison with the available methods in the literature.
Bibliografie:ObjectType-Article-1
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ISSN:2624-7402
2624-7402
DOI:10.3390/agriengineering1020015