# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 5 Results and Discussion
### Comparison with the State-of-the-Art
\begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{2}{*}{**Architecture**} & \multicolumn{2}{c}{**Sequences**} \\ \cline{2-3}  & **Sequence 1** & **Sequence 2** \\ \hline \multirow{7}{*}{2D-CNN 1} & **Convolutional layers** & **Convolutional layers** \\  & Conv1: 4.5 M & Conv1: 5.5 M \\  & Conv2: 8.1 M & Conv2: 9.6 M \\  & Conv3: 14.1 M & Conv3: 16.8 M \\ \cline{2-3}  & **Fully connected layers** & **Fully connected layers** \\  & Hidden layer 1: 3.5 M & Hidden layer 1: 4.2 M \\  & Output layer: 3.5 K & Output layer: 4.2 K \\ \cline{2-3}  & **Total** & **Total** \\  & 30.2 M & 36.2 M \\ \cline{2-3}  & **Prediction time** & **Prediction time** \\  & 6 h & 5 h \\ \hline \multirow{7}{*}{RNN} & **Memory layer** & **Convolutional layers** \\  & Layer1: 1.1 M & Layer1: 1.3 M \\ \cline{2-3}  & **Fully connected layers** & **Fully connected layers** \\  & Hidden layer 1: 2.1 K & Hidden layer 1: 2.1 K \\  & Hidden layer 2: 6.4 K & Hidden layer 1: 6.4 K \\  & Output layer: 1 K & Output layer: 0.9 K \\ \cline{2-3}  & **Total** & **Total** \\  & 1.12 M & 1.31 MM \\ \cline{2-3}  & **Prediction time** & **Prediction time** \\  & 6 h & 5 h \\ \hline \multirow{7}{*}{FCNN by [34]} & Conv1: 1.5 M & Conv1: 4.1 M \\  & Conv2: 1.5 M & Conv2: 4.1 M \\ \cline{2-3}  & Conv3: 1.5 M & Conv3: 4.1 M \\ \cline{2-3}  & Conv4: 102 K & Conv4: 315 K \\ \cline{2-3}  & RNN layer: 23.5 M & RNN layer: 66 M \\ \cline{2-3}  & **Total** & **Total** \\  & 28.1 M & 78.7 M \\ \hline \multirow{7}{*}{FCNN by [32]} & Conv1: 4.4 M & Conv1: 12.4 M \\  & Conv2: 829 K & Conv2: 2.3 M \\ \cline{2-3}  & Conv3: 2.5 M & Conv3: 7.2 M \\ \cline{2-3}  & Conv4: 102 K & Conv4: 315 K \\ \cline{2-3}  & DB 1: 7.3 M & DB 1: 20.6 M \\ \cline{2-3}  & DB 2: 2.5 M & DB 2: 7.2 M \\ \cline{2-3}  & DB 3: 184 K & DB 3: 516 K \\ \cline{2-3}  & DB 4: 737 K & DB 4: 2.1 M \\ \cline{2-3}  & Sa Mpling 1: 819 K & Sa Mpling 1: 2.2 M \\ \cline{2-3}  & Sa Mpling 2: 286 K & Sa Mpling 2: 802 K \\ \cline{2-3} \cline{2-3}  & **Total** & **Total** \\  & 19.9 M & 55.7 M \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Computational complexity of the compared DL models. to the post-processing phase and the exhaustive parameter research carried out during the experiments. From Table 7, it can be observed that the models with a higher number of _MAC_ operations (often deeper models), do not always lead to higher accuracy scores as a proper parameter setting per model is required. For instance, FCNN [34] though being among the most complex models for sequence 2, showed a relatively low _OA_ score. On the contrary, in addition to involving the lowest number of _MAC_ operations, the proposed 2D-CNN 1 achieves an _OA_ score higher than most of the compared DL models. As it is well known, the CNN depth has a fundamental impact on the overall classification, as well as on the computational resources required for training and deployment purposes. However, the proposed methodology is based on consecutive convolutional layers with a significant reduction in depth regarding the other architectures. For example, in [34] an LSTM and an FCNN are used at the same time to classify Campo Verde, and in [31], 100 filters are used in the convolutional layers. In Table 7, the complexity of the 2D-CNN 1 is compared with the most outstanding architectures used by the works mentioned in Table 6.