# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 4 Discussion
### The Operational Potential of Developed Methodologies  
Both the presented 2-D RGB and 3-D RGB-D methodologies achieved acceptable accuracies across the three experiments. Our results (Figures 12 and 13) support the use of PRS technology and techniques for vineyard yield estimation, especially for VSP-trained Shiraz vineyards. The nature of the presented work was conceptualised to assess 2-D and 3-D PRS sensors side-by-side, a novelty in the vineyard yield estimation domain. Experiment one illustrated the capability of these two techniques for successful yield estimation of individual bunches, where the Kinect RGB-D sensor (r\({}^{2}\) = 0.950) outperformed the digital RGB sensor (r\({}^{2}\) = 0.889). The suitability of the lighting under laboratory conditions coupled with the Kinect's ability to capture a 3-D model of the bunch both contribute to the success of the Kinect sensor over the RGB sensor. Nonetheless, robust methodologies were established in a controlled environment. Experiment two tested the established methodologies in situ, under both FC and LR canopy treatments. The produced results of experiment two--and experiment three--confirmed the hypothesis of a superior yield estimation agreement under the LR canopy treatment. If good canopy management practices are established early in the season, better yield estimation results than those obtained under the FC treatment could be achieved. Both sensors produced similar results for FC [approximate r\({}^{2}\) = 0.61; using Kinect's modified results (n = 20)] and LR (approximate r\({}^{2}\) = 0.75) treatments in experiment two. The success of the two PRS methodologies can be differentiated at plant-level by the results of experiment three. Unlike experiment one, the RGB methodology outperformed the RGB-D methodology for yield estimation. The RGB results (r\({}^{2}\) = 0.877) significantly outclassed the RGB-D results (r\({}^{2}\) = 0.487) under the FC treatment, whereas a smaller margin between the RGB (r\({}^{2}\) = 0.779) and RGB-D (r\({}^{2}\) = 0.594) sensors occurred under the LR treatment. Inference behind the differing results can lead to the following observations. The different lighting conditions influenced the results: the RGB imagery was collected at midday, while the RGB-D imagery was collected immediately before sunset. Additionally, the continuous movement of the Kinect sensor (RGB imagery captured in a stationary position) could have contributed to lowered RGB-D results. Further research is encouraged, using a standardised experimental setup, where feasible, to create more favourable conditions under which both sensors can operate. For the RGB and RGB-D datasets, the LR treatment yielded higher estimation agreements compared with the FC treatments. The LR treatment was adopted to create an ideal in-situ environment for yield estimation. The commercial feasibility of complete leaf removal in the bunch zone is not practical in some viticulture regions, since there are adverse effects associated with this practice (such as'sunburn'). However, this practice can be implemented in specific zones of the vineyards, enabling an approximation of the total yield using a monitoring strategy. The FC results provide a better indication of the commercial readiness of the developed methodologies. Future work should determine an optimal canopy coverage that favours both sensor operationality and commercial farming methods. Overall, the use of the Kinect sensor as a cost-effective RGB-D sensor for vineyard yield estimation, specifically at bunch-level, is supported by the results obtained for experiment one. However, the robustness of the RGB methodology is evident across all three experiments, with substantial plant-level results obtained in situ. A better understanding of the current limitations would allow further improvements to the methodologies. To this end, the results obtained in this study support the potential for operationalisation of both PRS sensors.