## 3 Results and Discussion
### Performance of the Models
For a better understanding of the precision and sensitivity (recall) values acquired for each class and model, it is possible to determine the number of images that constitute true positives or false positives for each class. Table 8 of the confusion matrix illustrates this by showing that, of the 50 samples used to categorise CDC-Leader, 46 were correctly identified (TP = 46), whereas 4 were mistakenly classified as CDC Orion (FP = 4). As a result, the CDC Leader's accuracy was 92% and its false positive rate was 8%. From the confusion tables, it can be observed that the varieties CDC-Leader and CDC-Orion often mispredict each other's labels, which may be attributed to their close colour shades and subtle differences in seed texture. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Training**} & \multicolumn{1}{c}{**Validation**} & \multicolumn{1}{c}{**Testing**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Time Per** \\ **Image (ms)** \\ \end{tabular} } \\ \hline  & **Loss** & **Accuracy** & **Loss** & **Accuracy** & **Loss** & **Accuracy** & \\ \hline NasNet-A & & & & & & \\ (mobile) & 0.499 & 0.946 & 0.459 & 0.959 & 0.455 & 0.968 & 38.36 \\ MobileNetV3 & 0.465 & 0.966 & 0.427 & 0.981 & 0.425 & 0.990 & 26.82 \\ EfficientNetB0 & 0.495 & 0.950 & 0.442 & 0.977 & 0.426 & 0.981 & 42.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Training, validation, and testing results of the three models for chickpea varied classification. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Consul** & **Cory** & **Leader** & **Orion** & **Specificity** & **Sensitivity** \\ \hline Consul & 61 & 0 & 0 & 0 & 1 & 1 \\ Cory & 0 & 58 & 0 & 0 & 1 & 1 \\ Leader & 0 & 0 & 50 & 0 & 0.988 & 1 \\ Orion & 0 & 0 & 2 & 49 & 1 & 0.961 \\ \hline Precision & 1 & 1 & 0.961 & 1 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Confusion matrix of NasNet—A (mobile). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Consul** & **Cory** & **Leader** & **Orion** & **Specificity** & **Sensitivity** \\ \hline Consul & 61 & 0 & 0 & 0 & 1 & 1 \\ Cory & 0 & 58 & 0 & 0 & 1 & 1 \\ Leader & 0 & 0 & 50 & 0 & 0.988 & 1 \\ Orion & 0 & 0 & 2 & 49 & 1 & 0.961 \\ \hline Precision & 1 & 1 & 0.961 & 1 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: MobileNetV3 (small) confusion matrix. Further, the value of the area under the average receiver operating characteristic (ROC) curve, AUC, is provided in Figure 7a for all the three models. It was observed that the AUC values were the highest for the MobileNetV3 and EfficientNetB0 models followed by NasNet-Mobile. The high AUC values are an indicator of the excellent classification performance by the classifier models. Besides, the one-vs-all classification approach was also conducted with ROC curve and AUC (Figure 7b). Since there are four chickpea varieties, four ROC curves were generated along with their AUC values. For each variety, it was taken as the positive class and the other varieties were jointly grouped as the negative class. The high AUC values in all the three models gives us an indication of the good performance of the models at classifying individual varieties. Figure 7: (**a**) Average ROC curve and AUC value of MobileNetV3, NasNet-Mobile, and EfficientNetB0 models; (**b**) one-vs-all ROC curves and AUC values of MobileNetV3, NasNet-Mobile, and EfficientNetB0 models. It is crucial to determine the time taken by the various models to identify a test image because this reveals the model's real-time detection speed [40]. For a massive number of convolutional operations, deep CNN needs powerful computing power. The development of small models is urgently needed because smartphones and single-board computers frequently have resource limitations due to their small size. Smaller models can significantly increase detection speed while lowering resource costs. Table 7 shows the length of time required by various CNN models to categorise a single image.