## 4 Experimental Results and Discussion  
### Vision Capabilities Implementation  
The ideation, training, validation and test cycle for neural networks has been managed with the help of Darknet, an open source framework for the creation of CNNs in C and CUDA. Darknet has become a standard thanks to the engineering of the YOLO (You Only Look Once) family of systems, state-of-the-art lightweight neural networks architectures for real-time object detection. Starting from YOLOv1, the framework has been enriched with ever more sophisticated variants over the course of multiple years (YOLOv2, YOLOv3, up to YOLOv4 and Scaled-YOLOv4). The fourth version of YOLO was the last to be written in Darknet in 2020; the AI company Ultralytics decided to further refine the YOLO architecture (up to YOLOv8 in 2023), but favored the PyTorch technology over Darknet.  
In the first experimental phase, two variants of model YOLOv2 have been adopted, i.e., YOLOv2-416 and YOLOv2-608. Although quite outdated, the choice is justified by the following:  
* Given the high dispersion rate of most chemicals in agricultural environments, the action of \"precision spraying\" does not necessarily require top-notch performances (for instance, in regard to IoU) to be considered effective. In our preliminary case study, YOLOv2 showed adequate performances with respect to the recognition of grape clusters as well as bounding box precision, with no need to scale to more refined versions (see Sections 4.1.2 and 4.2.4). Nonetheless, a comparative analysis with the YOLOv3/v4 performances is currently ongoing, and their adoption is not excluded in the foreseeable future;
* YOLOv2 allows a close-to-real-time performance for inference, even with GPU-less compute units such as the one adopted in our case;
* The (re-)training of YOLOv2 is significantly faster when compared to a more recent version of the architecture due to the relative simplicity of the underlying backbone;  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & Intel® NUC Kit NUCC7i7 DNKE \\
**OS** & Ubuntu 16.04 64 bit, Open Source \\
**CPU** & Intel® 17-8650U (8 M Cache, up to 4.20 GHz) \\
**RAM** & Crucial Single Rank 16 GB DDR4, 2400 MT/s \\
**NIC** & Intel® Dual-Band Wireless-AC 8265 Wi-Fi \\
**LAN** & Intel® 219-LM, up to 1000 BASE-T \\
**GPU** & — \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the cobot processor. NUC Kit, CPU, NIC and LAN acquired in Verona, Italy on [[https://www.amazon.it/INTEL-Barebone-BLKNUC7i7](https://www.amazon.it/INTEL-Barebone-BLKNUC7i7) DNK2E-Core-i7-8650U/dp/B07 BZCY271]([https://www.amazon.it/INTEL-Barebone-BLKNUC7i7](https://www.amazon.it/INTEL-Barebone-BLKNUC7i7) DNK2E-Core-i7-8650U/dp/B07 BZCY271). RAM acquired in Verona, Italy on [[https://it.crucial.com/memory/ddr4/ct8g4](https://it.crucial.com/memory/ddr4/ct8g4) sfs824a]([https://it.crucial.com/memory/ddr4/ct8g4](https://it.crucial.com/memory/ddr4/ct8g4) sfs824a). OS publicly available on [[https://releases.ubuntu.com/16.04/](https://releases.ubuntu.com/16.04/)]([https://releases.ubuntu.com/16.04/](https://releases.ubuntu.com/16.04/)).  
* Being an open source framework, Darknet implementations of YOLO (i.e., up to YOLOv4) are license-free by construction, whilst embedding Ultralytics software and AI models in commercial products and applications strongly recommends an enterprise licensing, unless the open source requirements of AGPL-3.0 are met. This aspect must be considered in view of a trade-off between cost reduction and performance.