## 3 Results and Discussions
### Classification Results and Evaluation
#### 3.2.2 Performance Analysis of Feature Extraction Technique
The LDA and GAN-LDA refer to the LDA models trained with the real image data, and combined real and artificial datasets, respectively. limited dataset. Furthermore, Figure 8 presents the best testing confusion matrices upon using the original and GAN-augmented images. In the case of LDA, the performance of certain classes, such as maize and Charlot did not change much on applying GAN-based augmentation. However, the results of classes, such as Cranesbill and Fat Hen, improved significantly. This is because Cranesbill and Fat Hen are relatively more complicated in shape, requiring more data by the network to learn the features. In contrast, the features of maize and charlock are simple and distinct; hence, they are easier for the classifiers to perform the classification task. From the \(F1\) scores of all the classes, it can be observed that the GAN-based image augmentation provided more information and enhanced the performance of transfer learning, as well as the feature extraction techniques for the crop/weed classifications. In previous works, classification accuracies greater than 90% have been achieved using SVM and LDA classifiers, especially for crop/weed classifications. Accuracies between 92 and95% were achieved using SVM on the color and texture features for identifying four common weed seedlings in the maize production systems [57]. When morphological features were added to the feature space, an improved accuracy of 96.5% was obtained using RBF-SVM [58]. In another study, local binary pattern-based texture features yielded a 98.5% accuracy with RBF as the kernel function [66]. [PERSON] et al. used the stepwise LDA to classify weeds into three classes: broad weed, narrow weed, and other weed species [67]. Their method accorded 98.1% overall accuracy on a database of 1200 images. In the case of deep-learning-based classifications, most studies used a transfer learning approach rather than training the CNN from scratch. For identifying weeds in cotton and tomato fields, the performances of seven state-of-the-art CNNs were evaluated [26]. All the models registered classification \(F1\)-scores greater than 88%. Moreover, the fine-tuning method was compared with a feature-extraction approach for all the adopted CNNs [26]. They observed that most of the networks gave better results through the feature-extraction approach--a similar inference from this study as well. Similarly, the Alexnet CNN architecture was transfer-learned with potato and sugar beet plant image datasets for binary classification [68]. The model's accuracy was 98.0%, with an average prediction time of fewer than 0.1 s, supporting real-time applications. As an improvement, [PERSON] et al. evaluated 35 CNN architectures for classifying 15 weed species in cotton production systems, for which ten of them achieved an \(F1\) score greater than 95% [27]. These results show that the classification models in this study have provided performances comparable to those previously developed. Overall, the results indicate that data augmentation through GANs can increase the training resources needed for classifiers, enabling researchers to develop better imaging-based predictors. The authors believe that the proposed methodology can revolutionize intelligent crop/weed classifiers. An interesting topic for future work could be to examine the capability of the proposed approach on other machine vision-based applications, such as fruit maturity detection [69; 70], fruit grading [71], agri-food product microstructural evaluation [65; 72; 73], crop disease identification [74], and crop growth and yield monitoring [75; 76; 77]. Figure 8: Best testing confusion matrices based on—(**a**) Transfer learning (GAN-TL), (**b**) Feature extraction—SVM (GAN-SVM), and (**c**) Feature extraction—LDA (GAN-LDA), using both real and GAN-synthesized datasets.