## 5 Conclusions  
In the present research paper, an exhaustive comparative study has been carried out between several cutting-edge deep learning models and machine learning ones. Moreover, the performance of the best-resulting network has been improved by using various activation functions and deep learning optimization algorithms. The findings are very promising and strongly indicate the dominance of the DL models over classical ML algorithms. Thus, the recall, the obtained accuracy and above all the simplicity of the approach confirm that the DL method is the best way forward for image classification problems with relatively large datasets. Nevertheless, DL algorithms also have some constraints, namely that a very powerful GPU/TPU is mandatory for training, as CNN models are time-consuming to train and might take hours to weeks depending on the dataset size.  
Therefore, to lessen the learning time, we employed pre-trained models. Moreover, merging the machine learning algorithms and deep networks requires much fewer CPU resources and will use about half of the memory bandwidth while generating better models. So, going forward, a web application can be implemented with a complete system composed of server-side elements containing a hybrid model based on the best-found functions (SGD optimizer+ swish activation function + RF+ InceptionV3) trained with features such as a display of the crop's recognized diseases that can be applied in the field for validation and testing. Moreover, the application may provide a forum for agronomists and farmers to discuss treatments and precautions for diseases they have experienced. Moreover, we will strive to decrease the learning time, computational complexity, and deep model size to run them on embedded or mobile platforms.  
Conceptualization, H.O. and M.S.; methodology, H.O.; software, H.O.; validation, M.S., M.K. and E.S.; formal analysis, M.S.; investigation, M.S.; resources, M.K.; data curation, M.K.; writing--original draft preparation, H.O.; writing--review and editing, M.K.; visualization, E.S.; supervision, E.S.; project administration, M.S. and M.K.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
This research k was done within the framework \"Agrometeorological Stations Platform\" project funded by the Moroccan Ministry of Higher Education and Scientific Research-National Centre for Scientific and Technical Research (NCSTR) (PPR2 project).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimizers** & **Advantages** & **Disadvantages** \\ \hline SGD & The computation time for each update is not depending on the overall number of training samples, and many computations cost is being saved. & It is challenging to select an appropriate learning rate, as well as using the same learning rate for all the parameters is inadequate. The solution in certain cases might be trapped at the saddle point. \\ \hline Adagrad & At the beginning of training, the accumulative gradient is smaller, the learning rate is higher, and the learning speed becomes faster. It is adequate for addressing sparse gradient problems. & As the training time expands, the cumulative gradient becomes increasingly larger, causing the learning rate to tend toward zero, leading to inefficient parameter updates. \\ \hline RMSProp & RMSProp converges more swiftly than SGD. & The learning rate must be chosen manually. \\ \hline Adadelta & Addressing the inefficient learning problem in the later phase of & During The late training phase, The updating process might be repeated around The local minimal value. \\ \hline Adam & The gradient descent process is quite steady. It suits mostly & The technique may fail to converge in some instances. \\ \hline \hline \end{tabular}
\end{table}
Table 10: Strengths and weaknesses of deep learning optimization.