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

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 timeconsuming 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. INDEX TERMS Fusarium head blight, K-means clustering, random forest, width mutation counting algorithm, pesticide spraying.

Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

“…A color imaging camera with built-in RGB filters can rapidly capture RGB images through three spectral channels (red, green, and blue), and has great potential for real-time detection of wheat FHB [39]. Although K-means clustering and random forest classifier have been used for segmentations of disease areas in wheat spikes [40], the advantage of this digital imaging technique is greater when a large number of datasets are available and a more powerful machine learning algorithm is utilized. Deep learning with great merit of automatic feature learning is a core part of the larger family of machine learning based on multiple layers of artificial neural networks, which allows greater learning capabilities and higher computational performance.…”

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

“…DCNN may utilised as a new tool for detecting and forecasting FHB in wheat was detected. Also, Zhang et al, [ 78 ] developed digital imaging, and tested the effects of fungi for the control of wheat Fusarium Head Blight. An unique way to estimate FHB symptoms and assessing the success of fungicide spraying programmes promptly was established.…”

“… hyperspectral image’s size reduced It is not developed real-time detection of agricultural diseases using affordable multispectral camera sensors. Zhang / 2020 [ 78 ] Created a novel technique to quickly determine the extent of FHB and gauge the success of fungicide treatment plans. fungicides that are efficient in controlling the FHB disease in wheat and other crop diseases are evaluated and identified using digital imaging technology.…”

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“…In formula (9), is the accuracy rate, is the recall rate, is the number of true positive samples, is the number of false positive samples, and is the number of false negative samples. When a single predicted object matches a ground-truth object whose is higher than the threshold, a true positive is calculated.…”