**5. Conclusions**

The generated UAV dataset with the use of object-detection techniques has managed to automate the procedure of monitoring and detecting the maturity level of broccoli in open-field conditions. The implementation of the developed methodology can drastically reduce labour and increase the efficiency in scouting operations, while ensuring effective yield quality through optimal harvest timing, eliminating potential fungal infection and quality degradation issues. The results of the experiments have clearly indicated that the models were able to perform very well for the task of automated maturity detection. All of the experimental iterations maintained high mAP@50 and mAP@75 values of over 80% and 70%. The results showed that, in general, Faster R-CNN and CenterNet were the best broccoli maturity detectors. Moreover, geometrical transformations for data augmentations reported improvements, while colour distortions were counterproductive. Specifically, RetinaNet displayed a significant improvement in performance with the use of augmentations.

Finally, the utmost caution should be taken regarding the numerous parameters that are involved in the design of each flight plan, because they are decisive factors in the success of low-altitude missions. At the same time, the technical flight-related difficulties and limitations similar to the ones encountered during this experiment will hopefully serve the potential future researchers who will continue this research in the same domain, or transfer this knowledge to their respective field. In future experiments, we aim to collect additional imagery using both different acquisition parameters and sensing devices in order to validate and expand our approach. Furthermore, different machine learning techniques, such as semi-supervised learning approaches, capsule networks [44] and transfer learning from backbones previously trained on agricultural datasets (i.e., domain transfer) are already under experimentation in similar trials, while the integration of the entire pipeline in a real-time system is also currently being tested.

**Author Contributions:** Conceptualization, V.P., A.C. and S.F.; methodology, V.P., B.E.-G.; K.K. and S.F.; software, V.P., B.E.-G. and A.C.; validation, A.D., K.K. and S.F.; investigation, V.P., B.E.-G. and A.D.; resources, A.D.; data curation, A.C.; visualization, V.P, and B.E.-G.; supervision, K.K and S.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy of the data collection location.

**Acknowledgments:** This work was supported by computational time granted from the National Infrastructures for Research and Technology S.A. (GRNET S.A.) in the National HPC facility-ARIS under project ID pr010012\_gpu.

**Conflicts of Interest:** The authors declare no conflict of interest.
