1. Introduction

2. Materials and Methods

2.1. Dataset

In (Medhi and Deb, 2022), the authors created a comprehensive data set comprising diverse images of banana diseases and healthy plant samples. The data set used in this study for the purpose of training the CNN model is a subset of their data. The initial data set consisted of approximately 100 images of Fusarium wilt infected banana leaves, as well as 100 images featuring healthy banana leaves. These images were captured at different times of the day, under varying environmental conditions, ensuring a wide range of scenarios for greater model robustness.

The original images from the data set had dimensions of 256 x 256 pixels. However, to align with the dimensions of the input layer in the CNN model, the images needed to be resized. The images were adjusted to a standard size of 224 x 224 pixels. This resizing ensured that all input images were consistent in terms of dimensions, enabling seamless processing within the CNN model.

By adjusting the image dimensions to match the input layer’s size, the CNN model was able to receive and process the image inputs. This harmonization of image sizes enabled a consistent and fair comparison across different samples during the training process, ensuring that the model learned from the entire data set uniformly.

Figure 1. A comparison between healthy and diseased banana leaves: (a) An image of a healthy banana leaf and (b) An image of a Panama-diseased banana leaf.

Figure 1. A comparison between healthy and diseased banana leaves: (a) An image of a healthy banana leaf and (b) An image of a Panama-diseased banana leaf.

3. Results

3.1. Training

Prior to the actual training of the model, the pre-trained layers of the ResNet-50 model were frozen. By freezing the pretrained layers, they can be used as fixed feature extractors, allowing meaningful features to be extracted from the new dataset without modifying the learned representations. These layers will later be unfrozen to fine-tune the model after training.

The CNN model was trained on Google Colab using the aforementioned Panama disease dataset. The training was accelerated using the cloud-based Graphics Processing Unit (GPU). The CNN model was trained with a range of epochs, spanning from 5 to 35, in order to determine the optimal training duration. It was found that a training time of approximately 17 epochs yielded the best results. This careful exploration was necessary to strike a balance between underfitting and overfitting.

After the training, the CNN model achieved an accuracy of approximately 0.99, meaning it correctly classified 0.99 of the input samples. Additionally, the model achieved a relatively low loss value of around 0.1, indicating a small discrepancy between the predicted and actual values (see Figure 5 and Figure 6).

It is worth noting that during the training phase, both the accuracy and loss of the model exhibited an similar trend. Around the 8-epoch mark, the model experienced both its highest accuracy and its lowest loss (see Figure 5 and Figure 6). This suggests that the model rapidly grasped the essential patterns within the data, leading to a notable improvement in its predictive performance.

Figure 3. Visualization of the learning process where the training and the validation accuracy for our CNN model improve after successive epochs.

Figure 3. Visualization of the learning process where the training and the validation accuracy for our CNN model improve after successive epochs.

3.2. Fine Tuning

During the process of fine-tuning, the ResNet-50 layers of the CNN model were initially unfrozen for the entire model to be tuned. The learning rate of the model was also adjusted to 10^-5. This is done to ensure that the weights of the model are not drastically altered during this process. By fine-tuning, we are essentially adapting the knowledge of the base model to this identification task.

Figure 4. These outputs are the values of accuracy and loss of the training and validation sets during fine-tuning.

Figure 4. These outputs are the values of accuracy and loss of the training and validation sets during fine-tuning.

3.3. Model Evaluation

In order to determine the ability of the CNN model in identifying fusarium wilt infections, it was tested on a dataset consisting of 500 images.1 The images, previously unseen by the model, were taken from a google search. Using the OpenCV-Python3 library, the data augmentation techniques of rotation, noise addition, and resizing were used to expand the 200 healthy and diseased images to 500 images.

The metrics used to determine the overall performance of the model were: accuracy, precision, recall, and F-1 score. These measures are calculated using equations (1), (2), (3), and (4), respectively. Accuracy is a straightforward metric that measures the overall correctness of the model’s predictions. Precision is a metric that focuses on the positive predictions made by the model. It is defined as the ratio of true positive predictions (correctly predicted positive samples) to the total number of positive predictions (both true positives and false positives). Recall, also known as sensitivity or true positive rate, measures the ability of the model to correctly identify positive samples. It is defined as the ratio of true positive predictions to the total number of actual positive samples (true positives and false negatives). The F-1 score is the harmonic mean of precision and recall. It provides a balanced measure that considers both precision and recall simultaneously. Additionally, a confusion matrix was visualized through the OpenCV library packages to see the exact number of true and false positive and negative identification classes. It should be noted that the accuracy of random guessing identification for fusarium wilt will yield an accuracy of 0.50. It should also be noted that there is no method to truly quantify the accuracy of a farmer in identifying fusarium wilt as this depends on many variables including experience, age, genetics and more.

$$\begin{array}{cc}\hfill \mathrm{Accuracy}& =\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{TN}+\mathrm{FP}+\mathrm{FN}}.\hfill \end{array}$$

(2)

$$\begin{array}{cc}\hfill \mathrm{Precision}& =\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}}.\hfill \end{array}$$

(3)

$$\begin{array}{cc}\hfill \mathrm{Recall}& =\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}}.\hfill \end{array}$$

(4)

$$\begin{array}{cc}\hfill \mathrm{F}1\mathrm{Score}& =\frac{2\times \mathrm{Precision}\times \mathrm{Recall}}{\mathrm{Precision}+\mathrm{Recall}}.\hfill \end{array}$$

(5)

The CNN model was able to correctly identify 492 out of the 500 total test images to the correct classification class (see Figure 5). The 8 errors consisted of 5 false positive and 3 false negative identifications. The greater number of false positives in this case is considered a more desirable outcome because the farmer can verify that the banana crop is not truly infected before any potential removal of the entire plant. If the number of false negatives were to be higher, that would pose a larger issue of missing infections, and thus, allowing a greater spread of fusarium wilt.

Figure 5. The confusion matrix represents the predictions made by the CNN model (500 total predictions).

Figure 5. The confusion matrix represents the predictions made by the CNN model (500 total predictions).

Figure 6. The classification report shown here depicts the precision, recall, F-1 score, accuracy, and the macro and weighted averages of these values.

Figure 6. The classification report shown here depicts the precision, recall, F-1 score, accuracy, and the macro and weighted averages of these values.

The CNN model was able to perform with an accuracy of 0.99 on the healthy images and 0.98 on the fusarium wilt diseased images. These measures combined with the 0.98 F1 score show the model was able to generalize its prior training to new data. As a comparison for metrics, (Sangeetha et al., 2023) trained a deep learning model to identify fusarium wilt and was only able to achieve 0.9156 accuracy, 0.9161 of precision, 0.8856 of recall, and 0.8156 of score. Their model was also able to substantially outperform existing models with an accuracy nearly 0.20 higher than the 2nd best model compared in their study. The model in this paper was able to outperform all the metrics of their model, however, this may be due to the original size of the evaluation set being substantially less than the set in their paper. In future real-world testing, this CNN model may yield slightly lower metrics; however, it is still expected that the model will outperform most existing models based on its stellar performance on this dataset.

This convolutional neural network approach to disease detection can be used in a wide range of applications in the future. Current industries where this technology is used include medicine, agriculture, transportation, astronomy, and many others. The model developed in this paper can be trained to identify many other banana diseases such as black sigatoka, banana mosaic virus, and banana bunchy top virus. The limitation of this study was the inability to perform real-world evaluation of the model due to the low availability of banana farms in the proximity of the southern United States. The next step for this system would be to test and implement it on a real-world banana plantation.

4. Discussion

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