Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision
<p>An example of manual annotation of target images; (<b>a</b>) a typical full-size image of spikes from a single planted row of wheat in the field; (<b>b</b>) the same image with annotated wheat spikes; (<b>c</b>) a segmented sub-image of a single wheat spike; (<b>d</b>) the individual wheat spike with annotated diseased areas.</p> "> Figure 2
<p>The architecture of the mask region convolutional neural network (Mask-RCNN) approach for wheat Fusarium head blight (FHB) disease assessment.</p> "> Figure 3
<p>A flowchart of the proposed approach for evaluating FHB disease severity.</p> "> Figure 4
<p>Training curves of accuracy and loss against number of iterations on identifications of (<b>a</b>) wheat spikes and (<b>b</b>) diseased areas.</p> "> Figure 5
<p>Selected examples of instance segmentation on correct detections of wheat spikes under three complex conditions in the test dataset, (<b>a</b>) high-density wheat spikes, (<b>b</b>) wheat spikes with overlapping boundaries, and (<b>c</b>) wheat spikes cut at the image borders. For each example, the original image (left) and the corresponding recognition result (right) are displayed.</p> "> Figure 6
<p>(<b>a</b>–<b>d</b>) Various examples of wheat spike detections in the validation dataset; the spike with the blue box rectangle only has the ground truth (a.k.a. is labeled) with no prediction results (a.k.a. is missed), which is a false negative, while the spike with the red rectangle only has the prediction result (a.k.a. is detected) with no ground truth (a.k.a. is not labeled), which is false positive.</p> "> Figure 7
<p>Results of instance segmentation of selected wheat spikes (red rectangles); (<b>a</b>,<b>b</b>) examples of multi-detections; (<b>c</b>,<b>d</b>) examples of wheat spike occlusion.</p> "> Figure 8
<p>Illustration of (<b>II</b>) disease area detection and (<b>III</b>) segmentation results from (<b>I</b>) the individual wheat spikes in test set; (<b>a</b>) diseased areas with shadow; (<b>b</b>) diseased areas under strong light; (<b>c</b>) diseased areas under low light; (<b>d</b>,<b>e</b>) diseased areas under awn occlusion.</p> "> Figure 9
<p>The process of disease detection for an entire wheat spike partially occluded by the peduncle (neck) of another plant; (<b>a</b>) a selected example of an original image with multiple wheat spikes; (<b>b</b>) a zoom-in view of a spike occluded by a peduncle; (<b>c</b>) Mask-RCNN for wheat spike detection; (<b>d</b>) Mask-RCNN for disease area identification; (<b>e</b>) the segmentation result of diseased spikelets.</p> "> Figure 10
<p>Examples of misjudgment of the diseased areas on (<b>a</b>,<b>b</b>) two wheat spikes; (<b>a1</b>,<b>b1</b>) zoom-in views of the spike; (<b>a2</b>,<b>b2</b>) Mask-RCNN for wheat spike detection; (<b>a3</b>,<b>b3</b>) Mask-RCNN for disease area identification; (<b>a4</b>,<b>b4</b>) the segmentation result of diseased spikelets.</p> "> Figure 11
<p>The detection of diseased areas in selected wheat sub-images in the validation dataset; (<b>a</b>,<b>b</b>) examples of detection failures caused by model errors; (<b>c</b>) an example of detection failures caused by human error due to mis-annotation; (<b>d</b>) an example of detection failures caused by model error or human error (the spike with the blue box rectangle was only captured in the ground truth set with no prediction results, while the spike with the red rectangle was only captured in the prediction results not in the ground truth results).</p> "> Figure 12
<p>Frequency of wheat spikes of each disease grade; (<b>a</b>) the number of wheat spikes at different disease grades in the training set; (<b>b</b>) the number of predicted and ground truth spikes in the validation set.</p> "> Figure 12 Cont.
<p>Frequency of wheat spikes of each disease grade; (<b>a</b>) the number of wheat spikes at different disease grades in the training set; (<b>b</b>) the number of predicted and ground truth spikes in the validation set.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection
2.2. Data Annotation and Examination
2.3. Mask-RCNN
2.4. Evaluation Metrics
2.5. Equipment
3. Results
3.1. Model Training
3.2. Wheat Spike Identification
3.3. FHB Disease Evaluation
3.4. Examination of Wheat FHB Severity
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Modelling Parameters | Values |
---|---|
Base learning rate | 0.02 |
Image input batch size | 2 |
Gamma | 0.1 |
Number of classes | 2 |
Maximum iterations | 2,700,000 |
Application | Training Time | Validation Time |
---|---|---|
Wheat spike identification | 45 h 23 min 26 s | 16 min 30 s |
FHB disease detection | 23 h 46 min 58 s | 1 min 28 s |
Type | P (%) | R (%) | F1-Score (%) | IoU (%) | AP of Bbox (%) | AP of Mask (%) | MIoU (%) |
---|---|---|---|---|---|---|---|
Wheat spike | 81.52 | 71.00 | 74.78 | 46.41 | 56.69 | 57.16 | 52.49 |
FHB disease | 72.10 | 76.16 | 74.04 | 51.24 | 63.38 | 65.14 | 51.18 |
Dataset | Type | No. of Spikes | Severity (%) | ||
---|---|---|---|---|---|
Mean ± SD | Max | Min | |||
Training | Ground truth | 2382 | 13.23 ± 10.44 | 85.51 | 0.50 |
Validation | Ground truth | 922 | 12.01 ± 8.81 | 50.16 | 0.89 |
Prediction | 911 | 9.27 ± 6.15 | 34.68 | 0.86 |
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Su, W.-H.; Zhang, J.; Yang, C.; Page, R.; Szinyei, T.; Hirsch, C.D.; Steffenson, B.J. Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision. Remote Sens. 2021, 13, 26. https://doi.org/10.3390/rs13010026
Su W-H, Zhang J, Yang C, Page R, Szinyei T, Hirsch CD, Steffenson BJ. Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision. Remote Sensing. 2021; 13(1):26. https://doi.org/10.3390/rs13010026
Chicago/Turabian StyleSu, Wen-Hao, Jiajing Zhang, Ce Yang, Rae Page, Tamas Szinyei, Cory D. Hirsch, and Brian J. Steffenson. 2021. "Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision" Remote Sensing 13, no. 1: 26. https://doi.org/10.3390/rs13010026