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Open AccessJournal ArticleDOI

Evaluation of Efficacy of Fungicides for Control of Wheat Fusarium Head Blight Based on Digital Imaging

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TLDR
A new method to rapidly assess the severity of FHB and evaluate the efficacy of fungicide application programs and the results show that the segmentation algorithm could segment wheat ears from a complex field background and the counting algorithm could effectively solve the problem of wheat ear adhesion and occlusion.
Abstract
Fusarium head blight (FHB) is one of the most important diseases in wheat worldwide. Evaluation and identification of effective fungicides are essential for control of FHB. However, traditional methods based on the manual disease severity assessment to evaluate the efficacy of fungicides are time-consuming and laborsome. In this study, we developed a new method to rapidly assess the severity of FHB and evaluate the efficacy of fungicide application programs. Enhanced red-green-green (RGG) images were processed from acquired raw red-green-blue (RGB) images of wheat ear samples; the images were transformed in color spaces through K-means clustering for rough segmentation of wheat ears; a random forest classifier was used with features of color, texture, geometry and vegetation index for fine segmentation of disease spots in wheat ears; a newly proposed width mutation counting algorithm was used to count wheat ears; and the disease severity of the wheat ears groups was graded and the efficacy of six fungicides was evaluated. The results show that the segmentation algorithm could segment wheat ears from a complex field background. And the counting algorithm could effectively solve the problem of wheat ear adhesion and occlusion. The average counting accuracy of all and diseased wheat ears were 93.00% and 92.64%, respectively, with the coefficients of determination (R 2 ) of 0.90 and 0.98, and the root mean square error (RMSE) of 10.56 and 7.52, respectively. The new method could accurately assess the diseased levels of wheat eat groups infected by FHB and determine the efficacy of the six fungicides evaluated. The results demonstrate a potential of using digital imaging technology to evaluate and identify effective fungicides for control of the FHB disease in wheat and other crop diseases.

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Citations
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Journal ArticleDOI

Enhancing wheat Fusarium head blight detection using rotation Yolo wheat detection network and simple spatial attention network

TL;DR: In this paper , the authors proposed two new networks: the Rotation Yolo Wheat Detection (RYWD) network and the Simple Spatial Attention (SSA) network, which achieved an average accuracy of 94.66% in predicting the levels of Fusarium head blight across two different years and locations.
Book ChapterDOI

Crop Disease Prediction Using Computational Machine Learning Model

John Bissett
TL;DR: In this paper , an alternative approach proposed the training model which is used to accurately detect the various diseases occurring merely on plant's life span, and various factors are considered functionally to achieve precisely more appropriate performance of the experimental model.
Journal ArticleDOI

Design of Device for Optical Luminescent Diagnostic of the Seeds Infected by Fusarium

TL;DR: In this article , a method for determining the degree of infected seeds with Fusarium was developed, where the seeds are placed in a light-tight chamber, they are excited by radiation, and photoluminescence is recorded.
Journal ArticleDOI

Survey on crop pest detection using deep learning and machine learning approaches

TL;DR: In this article , a survey of modern approaches for keeping an eye on agricultural fields for pest detection and contains a definition of plant pest detection to identify and categorise citrus plant pests, rice, and cotton as well as numerous ways of detecting them.
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Journal ArticleDOI

A Unified Effort to Fight an Enemy of Wheat and Barley: Fusarium Head Blight.

TL;DR: A sustained, coordinated, and collaborative research program that was put in place shortly after the 1993 Fusarium head blight epidemic is summarized, a program intended to quickly lead to improved management strategies and outreach implementation and serves as a model to deal with other emerging plant disease threats.
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