# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 4 Discussion
### Using 3-D RGB-D Imagery for Yield Estimation  
To date, the work presented by [PERSON] et al. [10] is the only literature supporting the use of the Kinect V1 sensor for vineyard yield estimation. [PERSON] et al. [10] used table grape bunches to determine the optimal viewing angle and distance for the sensor. The authors concluded that a side-on view of the bunch, with a distance of between 0.8-1.0 m, produced the least variability in mass estimations. The current findings of our study, which incorporated RGB-D imagery for yield estimation across the three experiments, are therefore the most comprehensive findings to date. The presented study exemplifies the potential of 3-D PRS techniques for yield estimation, specifically a cost-effective sensor like the Kinect V1.  
The nature of our study's datasets resulted in separate data analysis between the bunch- and plant-level datasets. Astounding results were obtained in experiment one (r\({}^{2}\) = 0.950; RMSE = 12.458 g). It was evident that the Kinect sensor favours ideal laboratory conditions, allowing accurate yield estimation at bunch-level. The ramification of less-favourable conditions, such as in-situ monitoring, became apparent in experiment two (bunch-level) and three (plant-level).  
Although no bunch-level studies have used a Kinect sensor for vineyard yield estimation, a similar approach under laboratory conditions, estimating the volume of cauliflowers, was presented by [PERSON] et al. [19]. The authors were able to achieve an r\({}^{2}\) of 0.868 when regressing the estimated volume against the known fruit mass. Conversely, our methodology enables the relationship between fruit volume and mass to be modelled, allowing the use of the adjusted mass (calculated from the model) for subsequent yield estimation. A fundamental difference between our methodology and that of [PERSON] et al. [19] is the method in which the 3-D model was captured. Our methodology captured a 3-D mesh of the fruit, while [PERSON] et al. [19] captured a 3-D point cloud of the vegetable. This required model reconstruction, constructively producing a 'watertight mesh'. Our improved results at this level (r\({}^{2}\) = 0.950) can be accredited to this fundamental difference. Interestingly, both methodologies used the same 3-D reconstruction method--_Screened Poisson Surface Reconstruction_[30] found in MeshLab [29].  
The primary limitation of the proposed methodology at bunch-level was a combination of dataset quality and the _Screened Poisson Surface Reconstruction_ method [30]. The consequence of a poor-quality mesh became apparent when bunch reconstruction resulted in significant defects, as seen in Figure 14a. Such imperfections directly affect the potential for accurate yield estimation, exemplified by the results of experiment two's FC dataset (r\({}^{2}\) = 0.020; in Figure 13b). Future research is necessary to gain a better understanding of this shortfall in the methodology.  
This study presented the novel use of a Kinect RGB-D sensor for in-situ vineyard yield estimation at plant-level (experiment three). The presented plant-level methodology produced promising results for the LR treatment (r\({}^{2}\) = 0.594; RMSE = 661.739 g), while the effect of the canopy coverage was evident in the FC treatment (r\({}^{2}\) = 0.487; RMSE = 673.535 g). Future work should improve on the presented methodology, potentially overcoming several limitations. Additionally, the nature of the Kinect V1 sensor results in the fundamental constraint of being suited to indoor use only, as solar illumination produces excessive interference in the captured imagery [19]. Future work should make use of the Kinect V2 sensor, since several improvements to the sensor have been implemented, effectively allowing improved outdoor imagery to be captured [37]. RGB-D sensors, specifically the Kinect V2, are being incorporated in terrestrial vehicles as cheap sensor alternatives for vineyard modelling and yield estimation, which is demonstrated by [38].