Deep Neural Network and Color Imaging

Fusarium head blight (FHB) disease is extensively distributed worldwide. This disease 13 damages grain quality and reduces yield. The detection of this disease in a high throughput way is 14 crucial to planters and breeders. Our study focused on developing a method for processing wheat 15 color images and accurately detecting disease areas using deep learning and image processing 16 techniques. The color images of wheat at the milky stage were collected and processed to construct 17 datasets, which were used to retrain a deep convolutional neural network model using transfer 18 learning. Testing results showed that the model can detect spikes, and the coefficient of 19 determination of the number of spikes between the manual count and the detection was 0.80. The 20 model was assessed, and the mean average precision for the testing dataset was 0.9201. On the 21 basis of the results of spike detection, a new color feature was applied to obtain the gray image of 22 each spike. Then, a modified region growing algorithm was implemented to segment and detect 23 the diseased areas of each spike. Results show that the region growing algorithm performs better 24 than K-means and Otsu’s method in segmenting the FHB disease. Overall, this study demonstrates 25 that deep learning techniques enable the accurate detection of FHB in wheat using color images, 26 and the proposed method can effectively detect spikes and diseased areas, thereby improving the 27 efficiency of FHB detection. 28

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