In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

https://www.mdpi.com/2072-4292/13/1/26/pdf

Automatic evaluation of wheat resistance to fusarium head blight using dual mask-rcnn deep learning frameworks in computer vision

The monitoring of winter wheat Fusarium head blight via rapid and non-destructive measures is important for agricultural production and disease control. Images of unmanned aerial vehicles (UAVs) are particularly suitable for the monitoring of wheat diseases because they feature high spatial resolution and flexible acquisition time. This study evaluated the potential to monitor Fusarium head blight via UAV hyperspectral imagery. The field site investigated by this study is located in Lujiang County, Anhui Province, China. The hyperspectral UAV images were acquired on 3 and 8 May 2019, when wheat was at the grain filling stage. Several features, including original spectral bands, vegetation indexes, and texture features, were extracted from these hyperspectral images. Based on these extracted features, univariate Fusarium monitoring models were developed, and backward feature selection was applied to filter these features. The backpropagation (BP) neural network was improved by integrating a simulated annealing algorithm in the experiment. A multivariate Fusarium head blight monitoring model was developed using the improved BP neural network. The results showed that bands in the red region provide important information for discriminating between wheat canopies that are either slightly or severely Fusarium-head-blight-infected. The modified chlorophyll absorption reflectance index performed best among all features, with an area under the curve and standard deviation of 1.0 and 0.0, respectively. Five commonly used methods were compared with this improved BP neural network. The results showed that the developed Fusarium head blight monitoring model achieved the highest overall accuracy of 98%. In addition, the difference between the producer accuracy and user accuracy of the improved BP neural network was smallest among all models, indicating that this model achieved better stability. These results demonstrate that hyperspectral images of UAVs can be used to monitor Fusarium head blight in winter wheat.

Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery

Reflectance images of effective wavelengths from hyperspectral imaging for identification of Fusarium head blight-infected wheat kernels combined with a residual attention convolution neural network

In recent years, deep learning has greatly improved the ability of wheatear detection. However, there are still three main problems in wheatear detection based on unmanned aerial vehicle (UAV) platforms. First, dense wheat plants often overlap, and the wind direction will blur the pictures, which obviously interferes with the detection of wheatears; second, due to the different maturity, color, genotype, and head orientation, the appearance will also be different; third, UAV needs to take images in the field and conduct real-time detection, which requires the embedded module to detect wheatears quickly and accurately. Given the above problems, we studied and improved YoloV4, and proposed a robust method for wheatear detection using UAV in natural scenes. For the first problem, we modified the network structure, deleted the feature map with a size of $19\times 19$ , and used k-means algorithm to re-cluster the anchors, and we proposed a method of prediction box fusion. For the second problem, we used the pseudo-labeling method and data augmentation methods to improve the generalization ability of the model. For the third problem, we simplified the network structure, replaced the original network convolution with the improved depthwise separable convolution, and proposed an adaptive ReLU activation function to reduce the amount of calculation and speed up the calculation. The experimental results showed that our method can effectively mark the bounding of wheatears. In test sets, our method achieves 96.71% in f1-score, which is 9.61% higher than the state of the art method, and the detection speed is 23% faster than the original method. It can be concluded that our method can effectively solve the problems of wheatear detection based on the UAV platform in natural scenes.

/pdf/a-robust-method-for-wheatear-detection-using-uav-in-natural-4vel6s2bfm.pdf

A Robust Method for Wheatear Detection Using UAV in Natural Scenes

Assessing mangrove leaf traits under different pest and disease severity with hyperspectral imaging spectroscopy

Integration of spectroscopy and image for identifying fusarium damage in wheat kernels.

Integrating spectral and image data to detect Fusarium head blight of wheat

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.

/pdf/evaluation-of-efficacy-of-fungicides-for-control-of-wheat-2yexmujctb.pdf

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

The invention particularly relates to a wheat scab detection method integrating the front and back conditions of wheat ears. The wheat scab detection method comprises the following steps: collecting afront image and a back image of a wheat ear; respectively processing the front image and the back image of the wheat ear to obtain a wheat ear area S11 and a scab area S12 in the front image and a wheat ear area S21 and a scab area S22 in the back image; (C) calculating the ratio of the scab area to the wheat ear area of the wheat ears according to the following formula: R = (S12 + S22)/(S11 + S21); and (D) carrying out disease grading on the wheat ears according to the ratio R and the national standard. The invention further discloses a corresponding detection device. Compared with the conventional single-side detection, the wheat scab detection method segments the wheat ears on the front and back sides, calculates the wheat ear area and the disease spot area of the front side and the back side, linearly adds the disease area and the wheat ear area of the front side and the back side, and finally, calculates the ratio of the disease area to the wheat ear area, divides disease grades,and provides more reliable technical support for plant protection personnel to investigate the disease condition and accurately apply pesticide to prevent and treat the disease.

Wheat scab detection method and device integrating front and back conditions of wheat ears

The invention particularly relates to a diagnosis method of the illness state severity of wheat scab in a field environment. The method comprises the following steps that (A) 45-degree-angle images ofwheat ears infected with the wheat scab in a wheat field are collected; (B) a field wheat ear dividing model is used for dividing the disease images to obtain wheat ear areas; a wheat scab dividing model is used for dividing the disease images to obtain disease scab areas; (C) a concave point matching method is used for dividing adhesion areas of the wheat ears and disease scabs to obtain the area of each wheat ear and the disease scab area of the corresponding wheat ear in the wheat field; (D) the ratio R of the disease scab area of each wheat ear to the area of the corresponding wheat ear is calculated, it is judged whether or not R is greater than a set threshold, and if yes, it is determined that the wheat ear is a disease ear; (E) the ratio X of the quantity of the disease ears to the total quantity of the ears in the wheat field is calculated, and the disease states are graded according to the ratio X. The invention also discloses a corresponding detection device. The method hasthe advantages that the diagnosis of the disease state of the wheat scab in the field environment can be achieved, the diagnosis speed is quick, and the accuracy is high.

Chen Yu

Papers

Integration of spectroscopy and image for identifying fusarium damage in wheat kernels.

Integrating spectral and image data to detect Fusarium head blight of wheat

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

Wheat scab detection method and device integrating front and back conditions of wheat ears

Diagnosis method and device of illness state severity of wheat scab in field environment