## 4 Discussion  
Typically, research in this area is conducted by following a specific architecture or classifier on a single crop species using only a few performance measures. It might be challenging to gather several architectures and then compare them to determine which one is most suitable for a given task and yields the highest accuracy. Thus, we were motivated to perform an extensive comparison using multiple ML and DL algorithms on a large dataset containing multiple crops. What distinguishes our work from prior ones is that we have employed a wider array of performance evaluation metrics to properly appraise the model's reliability, while developing a comparison between various deep learning optimizers and activation functions to improve the results obtained from the comparison of machine learning algorithms with DL, with the aim of finding the most appropriate combination of the model, which can also be applied to further advance research on other agricultural applications, such as weed classification, grass/crop discrimination, and plant recognition. Since we are working on a binary classification where there are two classes: diseased/healthy leaves. The stakes of confounding a diseased leaf with a healthy one, or even missing a diseased leaf, might be highly raised, especially for viral diseases that are spreading rapidly in the field. Accordingly, our main objective is to prevent this confusion and to this end, we grant great importance to recall measuring the model's performance in disease classification. Moreover, we strive not only for high accuracy or recall. However, we aim at overcoming \begin{table}
\begin{tabular}{c c} \hline \hline
**Classifiers** & **Execution Time (Hour: Min)** \\ \hline InceptionV3 (over 100 epochs) & 14 h 20 \\ LDA & 8 h 30 \\ VGG16 (over 100 epochs) & 22 h 35 \\ VGG19 (over 100 epochs) & 29 h 46 \\ ResNet50 (over 100 epochs) & 32 h 21 \\ CNN (over 100 epochs) & 27 h 45 \\ KNN & 7 h 17 \\ CART & 9 h 28 \\ RF & 6 h 55 \\ NB & 4 h 32 \\ SVM & 10 h 35 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The comparison of execution time for deep and machine learning techniques. the models' weaknesses (fitting of hyperparameters, proper choice of parameters such as the loss function) and determining whether they have misclassified the crop leaves. Indeed, there is no point in deploying a model that provides the highest accuracy but misclassifies diseased leaves as healthy. Based on this comparison, we found that the InceptionV3 network performs better in terms of accuracy, precision, and recall. Nevertheless, we are seeking for decreasing FN rate (diseased leaves being misclassified as healthy) which is a bit high compared to the RF classifier. On the other hand, the RF classifier offers lower accuracy than the InceptionV3 model, but its FN rate is significantly lower than any other model, as shown in Figures 16 and 17, the RF has a wider area under the curve which means that the classifier has more ability to distinguish classes. It is noteworthy that the CNN-based models have key advantages over other machine learning algorithms, which are reflected in their outstanding ability to autonomously perform feature engineering. The DL parses the data for associating features and will combine them to facilitate faster learning without being explicitly prompted to do so. Conversely, the hand-crafted feature extractor requires elaborate features and captures only low-level edge information. This is why neural structures are of such relevance and is exemplified by the InceptionV3 model's successful use of the receptive field concept to acquire local visual features to depict the topology of the image. In fact, since the theoretical learning process of the InceptionV3 is similar to MLP, it represents an extension model of the MLP. A constraint of MLP is that it has a tendency to allocate a large value (almost +1) to one neuron in the output layer while all remaining neurons possess a low value (almost \(-1\)). This poses challenges in dismissing errors in real-time applications [65].