*3.2. Two-Class Approach*

Regarding the performance for each individual maturity class, as observed in our results, the highest performing class was maturity class 3, followed by class 2, and finally class 1. Most class-3 broccoli heads were both detected successfully and classified correctly. In the case of lower maturity classes (1 and 2), despite the fact that the broccoli heads were distinguishable and thus correctly detected as objects, they would often be mixed between them in the classification step, as the architectures could not always tell them apart, and thus misclassified them. An example of this instance is presented in Figure 11, in which the single class-3 broccoli head present in the image was detected and classified correctly, while two class-2 heads, although properly detected, were misclassified as class 1.

**Figure 11.** Examples of (**a**) broccoli head detections and (**b**) ground truth labels using the threeclass approach.

These observations led us to speculate that the architectures would potentially yield even better results and demonstrate a stronger performance if the problem presented was a simplified "ready to harvest" and "not ready yet" two-class problem. In order to test this hypothesis, the two best- and the two worst-performing pipelines (based on both data augmentation and architecture) were selected and evaluated on the two-class version of the dataset, where maturity classes 1 and 2 were merged into a single class, while class 3 was kept intact. Table 8 presents the performances of the four selected pipelines once the problem was simplified from three classes to two classes. As can be observed, all of the performances improved while the ranking of the pipelines was the expected. Finally, a similar detection example using the two-class approach, on the same image as Figure 11, is shown in Figure 12.


**Table 8.** The performance of the selected architectures and augmentation types on the two-class dataset. In the parentheses, the training performance is presented for mAP@50. The bold numbers correspond to the best performances.

**Figure 12.** Examples of (**a**) broccoli head detections and (**b**) ground truth labels using the twoclass approach.
