## 3 Results and Discussion  
### Performance of the Models  
The performance of the models is displayed in Figure 6. It is vital to monitor the progress of training a deep learning network. The training metrics for each iteration are shown in the training graphs (Figure 6). This visualisation not only displays the changes in network accuracy as the network is trained but it also displays any overfitting of the training data [9]. Figure 6 shows the classification accuracy and cross-entropy loss for the best overall classification accuracy of the pre-trained CNN networks. The holdout validation monitors the training progress and assists in model optimisation. The variation between their training and validation accuracy is negligible, which indicates the model has generalised well enough on the training dataset and performed well on the validation dataset. The figures revealed that NASNet Mobile (Figure 6a), MobileNetV3 (Figure 6b), and EfficientNetB0 (Figure 6c) appeared to have comparable outcomes. The models achieved validation accuracy values that were higher than their training accuracy (Table 7) and the figures indicated no overfitting, with the models converging within five epochs. Further, when the model was trained beyond five epochs, it resulted in model overfitting and poor accuracy. \begin{table}
\begin{tabular}{c c} \hline \hline Processor & Broadcom BCM2711, Quad core Cortex-A72 (ARM v8) 64-bit SoC @ 1.5 GHz \\ \hline RAM & 8 GB LPDDR4-3200 SDRAM \\ Bluetooth & Bluetooth 5.0, BLE \\ Wi-Fi & 2.4 GHz and 5.0 GHz IEEE 802.11 ac wireless \\ USB & 2 USB 3.0 ports; 2 USB 2.0 ports \\ Ethernet & Gigabit Ethernet \\ HDMI & 2 \(\times\) micro-HDMI ports (up to 4 kp60 supported) \\ Storage & MicroSD card slot \\ Power supply & 5.1V 3A USB type C power \\ Dimensions & 85.6 mm \(\times\) 56.5 mm \\ Operating temperature & 0 to 50 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Technical attributes of the single-board computer Raspberry Pi 4. Figure 5: Experimental arrangement for model deployment in Raspberry Pi 4 using industrial camera. The analysis of Table 7 indicated that the test accuracy obtained with NasNet-A, MobileNetV3, and EfficientNetB0 was 96.82%, 99.09%, and 98.18%, respectively. The high classification accuracy of NasNet can be ascribed to its architecture, where there are only two types of modules or cells. The normal cell extracts features and a reduction cell down-samples the input [27]. The ultimate architecture is created by stacking these cells in a specific pattern, resulting in faster convergence of the network with high accuracy. The good classification accuracy of the MobileNetV3 model can be attributed to their bottleneck residual block, which uses \(1\times 1\) convolutions to generate a bottleneck. When a bottleneck is used, the number of parameters and matrix multiplications is reduced, which makes the residual blocks as thin as possible in order to maximise depth while using fewer parameters [38]. The original EfficientNet's basic module is Mobile Inverted Bottleneck Convolution, which contains an expansion convolution to allow for a significantly larger number of channels for depth-wise convolution, resulting in higher accuracy [39]. Furthermore, these networks have a complex architecture and are classified as directed acyclic graph (DAG) networks, which have one layer that receives input from multiple layers  
Figure 6: Accuracy and loss graph for each model: (**a**) NasNet-A (mobile); (**b**) MobileNet V3 (small); (**c**) EfficientNet B0. and also outputs to multiple layers [9]. Since the chickpea varieties appeared to be very similar, it was critical for the network to learn the complexity among the varieties in order to perform well in classification. A useful tool for visualising the performance of the CNN networks is the confusion matrix. Tables 8-10 display the confusion matrix of the three models used to categorise the four varieties of chickpeas, making it simpler to identify the classes that led to the greatest amount of inaccuracy in the trained models.