## 5 Discussion and Conclusions  
Considering the works on plant leaf disease severity prediction by deep learning models, we introduce a novel approach of assigning the diseased plant leaf images (maize common rust in our case) to their severity classes guided by the fuzzy decision rules that were derived from the calculated percentages of the diseased leaf areas. To accomplish this, we first converted the colour images from a 3-dimesional array to a 2-dimensional array. A 3-dimensional array has three channels of red, green and yellow colours. Each of these channels is made up of 8-bit pixels that determine the colour intensity in different parts of the image. For instance, a green colour is a result of setting the intensities of the pixels in the same dimensional space to \(255\text{G}+\text{0B}+\text{0R}\). The approach proposed in this study uses segmentation, which is in fact tedious when performed in the 3-dimesional space. The best way to achieve our goals was to first convert colour images from a 3-dimensional space to a 2-dimensional space. Figure 11 shows the differences between two colour spaces that the image can take. Next, we segmented the grayscale images and calculated the percentages of the diseased leaf areas in the maize Common Rust images. This enabled us to create fuzzy decision rules as guided by an experienced plant pathologist. For instance, he mentioned that a dark common rust which covers more than 50% of the leaf area is categorized as a late-stage disease. These fuzzy logic rules were then used to develop a training dataset that was used train the VGG-16 network. The maize common rust severity classes developed in this way were used to train the fine-tuned VGG-16 that obtained a validation accuracy of 95.63% and a testing accuracy of 89%.  
Figure 10: Number of correctly classified images in each class by the VGG-16 network.  
The approach that was proposed by [PERSON], [PERSON], and [PERSON] [1] for plant leaf disease severity prediction using Deep Learning was biased in a way because the methods they used to assign the images of the Common Rust disease to their severity classes were totally based on decisions that were made by human eye observations. The proposed approach is therefore unbiased as it utilizes the decisions of computerized fuzzy decision rules to assign the Common Rust images to their severity classes for maize Common Rust disease. To the best of our knowledge, this is the first report of the approach to predict the severity of maize common rust disease. Broadly translated, our findings indicate that the fine-tuned model (VGG-16) trained with the datasets compiled in this study's approach has higher validation and testing accuracies in the prediction of maize common rust disease severity. Future investigations are necessary to validate the kinds of conclusions that can be drawn from this study if other types of maize leaf diseases, or any other leaf disease types of different plant species were to be used in this study's approach.  
The following are available online at [[https://www.mdpi.com/2076-0817/10/2/131/s1](https://www.mdpi.com/2076-0817/10/2/131/s1)]([https://www.mdpi.com/2076-0817/10/2/131/s1](https://www.mdpi.com/2076-0817/10/2/131/s1)), The training data set is available on request by sending an email to [EMAIL_ADDRESS].  
Conceptualization, M.S. ([PERSON]); funding acquisition, M.S. ([PERSON]); methodology, M.S. ([PERSON]); supervision, M.S. ([PERSON]); validation, M.S. ([PERSON]); visualization, M.S. ([PERSON]); writing--original draft, M.S. ([PERSON]); writing--review and editing, M.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Council for Scientific and Industrial Research (CSIR), grant number UNISA-43447619.  
Not applicable.  
The training data set is available on request by sending an email to [EMAIL_ADDRESS].  
The research presented in this paper was supported by the Council for Scientific and Industrial Research (CSIR).  
The authors declare no conflict of interest.