Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging

Ruicheng Qiu,Ali Moghimi,Brian J Steffenson,Cory D Hirsch,Ce Yang,Man Zhang

doi:10.3390/rs11222658

Ruicheng Qiu, Ali Moghimi + Show 4 more

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https://doi.org/10.3390/rs11222658

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Abstract

Fusarium head blight (FHB) is a devastating disease of wheat worldwide. In addition to reducing the yield of the crop, the causal pathogens also produce mycotoxins that can contaminate the grain. The development of resistant wheat varieties is one of the best ways to reduce the impact of FHB. To develop such varieties, breeders must expose germplasm lines to the pathogen in the field and assess the disease reaction. Phenotyping breeding materials for resistance to FHB is time-consuming, labor-intensive, and expensive when using conventional protocols. To develop a reliable and cost-effective high throughput phenotyping system for assessing FHB in the field, we focused on developing a method for processing color images of wheat spikes to accurately detect diseased areas using deep learning and image processing techniques. Color images of wheat spikes at the milk stage were collected in a shadow condition and processed to construct datasets, which were used to retrain a deep convolutional neural network model using transfer learning. Testing results showed that the model detected spikes very accurately in the images since the coefficient of determination for the number of spikes tallied by manual count and the model was 0.80. The model was assessed, and the mean average precision for the testing dataset was 0.9201. On the basis of the results for spike detection, a new color feature was applied to obtain the gray image of each spike and a modified region-growing algorithm was implemented to segment and detect the diseased areas of each spike. Results showed that the region growing algorithm performed better than the K-means and Otsu’s method in segmenting diseased areas. We demonstrated that deep learning techniques enable accurate detection of FHB in wheat based on color image analysis, and the proposed method can effectively detect spikes and diseased areas, which improves the efficiency of the FHB assessment in the field.

Highlights

As a widely cultivated crop and staple food in the world, wheat (Triticum aestivum L.) is crucial for ensuring food security
The potential of Fusarium head blight (FHB) detection using color images was explored with wheat plants infected in the field
To detect and distinguish spikes in images, numerous annotated images were divided into training and validation datasets to retrain a Mask Region-based Convolutional Network (RCNN) model through the transfer learning technique

Summary

Introduction

As a widely cultivated crop and staple food in the world, wheat (Triticum aestivum L.) is crucial for ensuring food security. Its production is limited by many abiotic and biotic stresses. One of the most devastating fungal diseases of wheat is Fusarium head blight (FHB) or scab, which is caused by a number of different Fusarium species, but most commonly F. graminearum. FHB attacks wheat spikes, which causes yield losses as high as 40% under severe epidemics [1]. In addition to causing yield losses, FHB pathogens produce mycotoxins that can contaminate the grain, often rendering it unusable. One of the most common mycotoxins produced by F. graminearum in grain is deoxynivalenol (DON), which is toxic to humans and animals, especially swine. When humans ingest grain contaminated with DON, the common symptoms include vomiting, dizziness, and nausea [2,3]. Due to the problems associated with this mycotoxin in food and feed chains, DON levels in grain are routinely assayed

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