Performance of Neural Networks in the Prediction of Nitrogen Nutrition in Strawberry Plants
Abstract
:1. Introduction
2. Materials and Methods
2.1. Obtaining and Processing the Images
2.2. Analysis of the Models
3. Results and Discussion
3.1. Foliar Chemical Analysis
3.2. Analysis of the Models
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Layers | Settings |
---|---|
2D Convolutional | 256 filters with size 5 × 5, activation by ReLU |
Batch Normalization | Default |
2D MaxPooling | Size pooling = 2 × 2, stride = 2 |
2D Convolutional | 128 filters with size 3 × 7, activation by ReLU |
Batch Normalization | Default |
2D MaxPooling | Size pooling = 2 × 2, stride = 2 |
2D Convolutional | 128 filters with size 7 × 3, activation by ReLU |
Batch Normalization | Default |
2D Convolutional | 32 filters with size 3 × 3, activation by ReLU |
Batch Normalization | Default |
Fully Connected | 3 neurons, activation by softmax |
Layer Name | Output Size | 50 Layers |
---|---|---|
conv1 | 112 × 112 | 7 × 7, 64, stride 2 |
conv2_x | 56 × 56 | 3 × 3 max pool, stride 2 |
conv3_x | 28 × 28 | |
conv4_x | 14 × 14 | |
conv5_x | 7 × 7 | |
1 × 1 | Average pool, 3-d fc, softmax |
Number of times | 10 |
Mini-lot size | 8 |
Option for data scrambling | every-epoch |
Positive scaling of the initial learning rate | 0.0001 |
Metric | Formula | Description |
---|---|---|
Accuracy | The overall efficiency of a model. | |
Sensitivity or Recall | The efficiency of a model as to positive samples. | |
Specificity | The efficiency of a model as to negative samples. | |
Precision | The proportion of actual positives out of all positives predicted by the model. | |
F1-score | The harmonic mean between precision and sensitivity. | |
G-mean | Maximum accuracy in each of the classes. |
Personalized CNN (%) | ResNet-50 (%) | |||||
---|---|---|---|---|---|---|
Metrics | T1 | T2 | T3 | T1 | T2 | T3 |
Accuracy | 48 | 48 | 48 | 78 | 78 | 78 |
Sensitivity | 60 | 44 | 40 | 84 | 73 | 77 |
Specificity | 42 | 50 | 52 | 75 | 81 | 79 |
Precision | 34 | 31 | 29 | 63 | 65 | 64 |
Recall | 60 | 44 | 40 | 84 | 73 | 77 |
F1-score | 44 | 36 | 34 | 72 | 69 | 70 |
G-mean | 50 | 47 | 46 | 80 | 77 | 78 |
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Share and Cite
Regazzo, J.R.; Silva, T.L.d.; Tavares, M.S.; Sardinha, E.J.d.S.; Figueiredo, C.G.; Couto, J.L.; Gomes, T.M.; Tech, A.R.B.; Baesso, M.M. Performance of Neural Networks in the Prediction of Nitrogen Nutrition in Strawberry Plants. AgriEngineering 2024, 6, 1760-1770. https://doi.org/10.3390/agriengineering6020102
Regazzo JR, Silva TLd, Tavares MS, Sardinha EJdS, Figueiredo CG, Couto JL, Gomes TM, Tech ARB, Baesso MM. Performance of Neural Networks in the Prediction of Nitrogen Nutrition in Strawberry Plants. AgriEngineering. 2024; 6(2):1760-1770. https://doi.org/10.3390/agriengineering6020102
Chicago/Turabian StyleRegazzo, Jamile Raquel, Thiago Lima da Silva, Marcos Silva Tavares, Edson José de Souza Sardinha, Caroline Goulart Figueiredo, Júlia Luna Couto, Tamara Maria Gomes, Adriano Rogério Bruno Tech, and Murilo Mesquita Baesso. 2024. "Performance of Neural Networks in the Prediction of Nitrogen Nutrition in Strawberry Plants" AgriEngineering 6, no. 2: 1760-1770. https://doi.org/10.3390/agriengineering6020102
APA StyleRegazzo, J. R., Silva, T. L. d., Tavares, M. S., Sardinha, E. J. d. S., Figueiredo, C. G., Couto, J. L., Gomes, T. M., Tech, A. R. B., & Baesso, M. M. (2024). Performance of Neural Networks in the Prediction of Nitrogen Nutrition in Strawberry Plants. AgriEngineering, 6(2), 1760-1770. https://doi.org/10.3390/agriengineering6020102