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Maize Lethal Necrosis Disease Detection for Maize Crop Real-Time Prediction Yield Modeling through Colour Pixel Feature

by Waheed Sanya, Hashim Chande, Haji Ali Haji
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 18
Year of Publication: 2021
Authors: Waheed Sanya, Hashim Chande, Haji Ali Haji
10.5120/ijca2021921522

Waheed Sanya, Hashim Chande, Haji Ali Haji . Maize Lethal Necrosis Disease Detection for Maize Crop Real-Time Prediction Yield Modeling through Colour Pixel Feature. International Journal of Computer Applications. 183, 18 ( Jul 2021), 1-9. DOI=10.5120/ijca2021921522

@article{ 10.5120/ijca2021921522,
author = { Waheed Sanya, Hashim Chande, Haji Ali Haji },
title = { Maize Lethal Necrosis Disease Detection for Maize Crop Real-Time Prediction Yield Modeling through Colour Pixel Feature },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2021 },
volume = { 183 },
number = { 18 },
month = { Jul },
year = { 2021 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number18/32023-2021921522/ },
doi = { 10.5120/ijca2021921522 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:17:08.198783+05:30
%A Waheed Sanya
%A Hashim Chande
%A Haji Ali Haji
%T Maize Lethal Necrosis Disease Detection for Maize Crop Real-Time Prediction Yield Modeling through Colour Pixel Feature
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 18
%P 1-9
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Maize Lethal Necrosis Disease (MLND) - is one of the diseases threatening maize production in a large area of East Africa. The disease is initiated by Maize Chlorotic Mottle Virus (MCMV) in blend with viruses of genus Potyvirus, commonly Sugarcane Mosaic Virus (SCMV). The simultaneous infection is the results in intensive to complete yield loss. Inability to predict disease parameters affecting maize crop yield has been a major drawback for the effectiveness and perfection of the existing manual maize crop yield prediction system and procedure in East Africa. Presently, human visual analysis is the furthermost commonly used method for detecting diseases. Due to this method, many errors were observed as the diagnosis is mainly based on the familiarity of the farmers. It consumes time to identify crop diseases founded on visually noticeable characteristics. This research sought to propose a real-time prediction system for maize crop yield using image-based mobile for detecting crop disease affecting crop production using SVM algorithms. In the proposed model, images of maize leaves from mobile were extracted their colour features and identify the Maize Lethal Necrosis Disease (MLND). The presence of SVM due to its fast processing speed as well as accuracy of its output these algorithms requires input training and test data for the model. This prediction model will be integrated into the mobile device for farmers to use. It determines the crop leaf area index that is helpful in predicted yields and its corresponding approximate. Evaluation is also conducted against the proposed model to measure the accuracy of a real-time prediction system in producing the results of crop maize disease. The results show for the SVM, the correlation(R) between estimated Leaf Area for maize and Leaf Area affected in Tunguu area was reported as 0.6959 and 06.099 respectively. So SVM classifiers offer good accuracy as well as perform faster prediction related to naïve Bayes algorithm. Since a combination of Real-time system-based farmers mobile application images collection and Leaf Area index has never been used in East Africa so far for researcher knowledge.

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Index Terms

Computer Science
Information Sciences

Keywords

Maize Lethal Necrosis Disease (MLND) ANN SVM LAI Mobile application Real-time systems.

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