## 3 Experimental Validation  
### Result Analysis of SSODTL-CC Model  
This section investigates the performance of the proposed model on test images. Figure 3 showcases the sample classification results obtained by the SSODTL-CC model. The figure implies that the proposed model has obtained effective classification results. In addition, some of the misclassified regions by the SSODTL-CC model are marked in blue circles. Figure 4 inspects the confusion matrices created by the SSODTL-CC model on the classification of nine classes under dataset-1. The figure reports that the SSODTL-CC model has categorized all the classes under different sets of datasets. For the entire dataset, the SSODTL-CC model recognized 956 samples under corn, 975 samples under cotton, 971 samples under sesame, 971 samples under broad-leaf soybean, 964 samples under narrow-leaf soybean, 949 samples under rice, 965 samples under water, 958 samples under roads and houses, and 967 samples under mixed weed. Similarly, the SSODTL-CC model has categorized the class labels proficiently on 70% of the training samples and 30% of the testing samples on dataset-1. Figure 3: Sample classification result of the SSODTL-CC technique under dataset-1: (**a**) input image, (**b**) class labels, and (**c**) classification output. Table 1 reports detailed crop classification outcomes of the SSODTL-CC model on all of dataset-1. The experimental values indicated that the SSODTL-CC model gained effectual outcomes under every individual class. For instance, in the corn class, the SSODTL-CC model offered \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 99.24%, 97.55%, and 95.60%, respectively. Similarly, on the mixed weed class, the SSODTL-CC model reached \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 99.27%, 96.70%, and 96.70%, respectively. Overall, the SSODTL-CC model showed a maximum average \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 99.20%, 96.43%, and 96.40%, respectively. Table 2 depicts a brief crop classification outcome of the SSODTL-CC approach on 70% of training dataset-1. The experimental values stated that the SSODTL-CC method gained effectual outcomes under every individual class. For instance, in the corn class, the SSODTL-CC model offered \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 99.19%, 97.04%, and 95.49%, respectively. In addition, in the mixed weed class, the SSODTL-CC system obtained \(accu_{y}\)  
Figure 4: Confusion matrix of the SSODTL-CC technique under dataset-1. (**a**) Entire dataset-1. (**b**) 70% of Training dataset-1 and (**c**) 30% of Testing dataset-1. \(prec_{n}\), and \(reca_{l}\) of 99.27%, 96.67%, and 96.67%, respectively. Overall, the SSODTL-CC model demonstrated maximum average \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 99.19%, 96.38%, and 96.35%, correspondingly. Table 3 defines the detailed crop classification outcomes of the SSODTL-CC model on 30% of testing dataset-1. The experimental values indicated that the SSODTL-CC model gained effectual outcomes under every individual class. For instance, in the corn class, the SSODTL-CC approach presented \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 99.37%, 98.68%, and 95.85%, correspondingly. Furthermore, in the mixed weed class, the SSODTL-CC methodology reached \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 99.26%, 96.76%, and 96.76%, respectively. Overall, the SSODTL-CC model portrayed enhanced average \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 99.23%, 96.54%, and 96.53%, correspondingly. Figure 5 illustrates the confusion matrices created by the SSODTL-CC approach on the classification of sixteen classes under dataset-2. The figure reveals that the SSODTL-CC model categorized all the classes under different sets of datasets. On the entire dataset, the SSODTL-CC model recognized 783 samples under class 1, 757 samples under class 2, 766 samples under class 3, 728 samples under class 4, 721 samples under class 5, 774 samples under class 6, 764 samples under class 7, 788 samples under class 8, 779 samples under