Nutrient Stress Symptom Detection in Cucumber Seedlings Using Segmented Regression and a Mask Region-Based Convolutional Neural Network Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Seedling Preparation and Growth Condition
2.2. Image Acquisition
2.3. Dataset Preparation
2.3.1. Image Feature Extraction
2.3.2. Stress Symptom Initiation and Detection
2.3.3. Dataset Description for the Detection Model
2.4. Mask R-CNN Model
2.4.1. Mask R-CNN Backbone Network and Feature Pyramid Network (FPN)
2.4.2. FPN with Regional Proposal Network (RPN)
2.4.3. RoIAlign and Loss Function
2.5. Training Configuration
2.6. Evaluation Matrices
3. Results
3.1. Detection of Stress Initiation
3.2. Training and Validation of Data
3.3. Performance Detection on Stressed Seedlings
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Raspberry Pi 4B | Raspberry Pi Camera |
---|---|
|
|
Layer | Conv1_x | Conv2_x | Conv3_x |
Output size | 112 × 112 × 64 | 56 × 56 × 56 | 28 × 28 × 128 |
Filters | 7 × 7, 64 stride 2 | ||
Layer | Conv4_x | Conv5_x | Pooling |
Output size | 14 × 14 × 256 | 7 × 7 × 512 | 1 × 1 × 512 |
Filters | Average |
Stress Condition | Condition | Precision Rate (%) | Recall Rate (%) | F1 Score | Accuracy | mAP |
---|---|---|---|---|---|---|
0 dSm−1 | Average | 0.91 | 0.89 | 0.90 | 0.90 | 0.927 |
Best Fit | 0.93 | 0.94 | 0.934 | 0.92 | ||
6 dSm−1 | Average | 0.88 | 0.87 | 0.874 | 0.87 | 0.88 |
Best Fit | 0.90 | 0.89 | 0.894 | 0.90 |
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Islam, S.; Reza, M.N.; Ahmed, S.; Samsuzzaman; Lee, K.-H.; Cho, Y.J.; Noh, D.H.; Chung, S.-O. Nutrient Stress Symptom Detection in Cucumber Seedlings Using Segmented Regression and a Mask Region-Based Convolutional Neural Network Model. Agriculture 2024, 14, 1390. https://doi.org/10.3390/agriculture14081390
Islam S, Reza MN, Ahmed S, Samsuzzaman, Lee K-H, Cho YJ, Noh DH, Chung S-O. Nutrient Stress Symptom Detection in Cucumber Seedlings Using Segmented Regression and a Mask Region-Based Convolutional Neural Network Model. Agriculture. 2024; 14(8):1390. https://doi.org/10.3390/agriculture14081390
Chicago/Turabian StyleIslam, Sumaiya, Md Nasim Reza, Shahriar Ahmed, Samsuzzaman, Kyu-Ho Lee, Yeon Jin Cho, Dong Hee Noh, and Sun-Ok Chung. 2024. "Nutrient Stress Symptom Detection in Cucumber Seedlings Using Segmented Regression and a Mask Region-Based Convolutional Neural Network Model" Agriculture 14, no. 8: 1390. https://doi.org/10.3390/agriculture14081390