# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 4 Discussion
### Using 2-D RGB Imagery for Yield Estimation  
The presented results using RGB imagery for yield estimation are robust and support the 2-D PRS technique. Experiment one (r\({}^{2}\) = 0.889; RMSE = 17.978 g) illustrated the success of RGB imagery in a controlled environment for yield estimation at bunch-level. At plant-level, similar results were presented for the LR treatment in experiment three (r\({}^{2}\) = 0.877; RMSE = 443.235 g). The success of the methodology under both laboratory and field conditions supports the use of colour thresholding for image segmentation, and the adapted pixel area metric for yield estimation.  
Colour thresholding for image segmentation was favoured by several studies [8; 9; 14]. In our study, the use of the HSV colour space for thresholding has proven fruitful. The HSV colour space is supported by [PERSON] et al. [14], who achieved favourable results (estimation error of 13.55%) when working with the H layer for segmentation purposes. At bunch-level, experiment one's result (r\({}^{2}\) = 0.889) shows an improvement to the result (r\({}^{2}\) = 0.77) presented by [PERSON], [PERSON] and [PERSON] [9]; similarly conducted under laboratory conditions. This aspect was a noteworthy improvement, especially considering the manual nature of the colour thresholding. At plant-level, the manually produced results of experiment three (FC: r\({}^{2}\) = 0.779; LR: r\({}^{2}\) = 0.877) align with the automated classification results (r\({}^{2}\) = 0.865) of [PERSON], [PERSON] and [PERSON] [9]. Here, [PERSON], [PERSON] and [PERSON] [9] use the same 1\(\times\)1 m image dataset as [PERSON] and [PERSON] [8], as opposed to the plant-level imagery used in our study. Additionally, the colour thresholding approach of our study outperformed the image classification approach (test r\({}^{2}\) = 0.73) [13] to segmentation.  
We presented an adaption of the pixel count metric for yield estimation, which expands on current literature [8; 9; 13; 14]. Of the five metrics tested by [PERSON], [PERSON] and [PERSON] [9], the pixel count produced the best results. We found the incorporation of a calibration length (the ruler) for pixel count resulted in an improved quantitative pixel area (cm\({}^{2}\)) for yield estimation. Again, our results at bunch-level (r\({}^{2}\) = 0.889) improved the bunch-level results (r\({}^{2}\) = 0.77) conducted in laboratory conditions--as  
Figure 14: Illustration of _Screened Poisson Surface Reconstruction_. The reconstructed bunch (circled in red—Figure 13b) with the incorrect volume (**a**), and an example of a reconstructed bunch of the correct volume (**b**).  
presented by [PERSON], [PERSON] and [PERSON] [9], who were specifically testing the various yield estimation metrics. At plant-level, our LR treatment (r\({}^{2}\) = 0.877) outperformed all current literature to date, with our FC treatment (r\({}^{2}\) = 0.779) representing a slight improvement for in-situ measurements. The presented pixel area (cm\({}^{2}\)) metric also improved on the berry count results [4; 11], with the highest berry count r\({}^{2}\) (0.74) presented still being lower than our plant-level pixel area r\({}^{2}\) (0.779) for the FC treatment. However, the potential of the berry count is applicable across all cultivars, as it does not depend on the colour of the berry [11].  
The limitation of slight distance variations between the camera and bunches within each image was resolved by the incorporation of the reference length (the ruler). The necessity of determining the calibration length for each image outweighs the added processing requirement of this step. Overall, this allows improved yield estimation success, as represented in our results. Future work could attempt to automate this process. However, a more restrictive limitation was the human involvement in determining the appropriate threshold values; this could explain the lowered in-situ estimation performance at bunch-level. Future work could investigate a more automated methodology, thus alleviating this limitation.