# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 1 Introduction  
Modern-day viticulture has seen an increase in the use of robust scientific methods combined with new technologies to improve overall production [1]. Precision farming, a direct result of the modernisation in agriculture, can be discipline-specific--i.e., specific to horticulture or viticulture. Precision viticulture aims to effectively manage production inputs to improve yield and grape quality while reducing the environmental impact of farming [2]. The use of remote sensing technology and techniques in precision viticulture allows variability to be monitored at vineyard level, per individual block or on a vine basis. Aspects such as vine shape, size and vigour can be observed, providing more accurate yield and fruit quality information [3]. Yield estimation provides information to the winegrower that can be used to manage the vineyard, optimising quality and yield [4]. Awareness of the estimated yield allows the vineyard manager to manipulate the vines to obtain the desired grape characteristics, and provides an effective plan foruse during the winemaking process [5]. Accurate yield forecasting assists with logistical planning, both during and after the harvest; for example, what volume will be harvested, where the grapes will be stored, and an expected market price [5]. [PERSON] and [PERSON] [6] outlined a two-step method for vineyard yield estimation. The start of the process determines the number of bunches situated on individual vines early in the season. Subsequent determination of bunch weights occurs at veraison. Unfortunately, the two-step method is labour-intensive, error-prone and destructive in the estimation process. Additionally, [PERSON] et al. [7] presented yield prediction models using destructive, manually collected data between fruit-set and veraison, aligning with the more 'classical' two-step method. To overcome the limitations of the manual methods, modern techniques have employed sensors attached to automatic harvesters to monitor yield during the harvesting process [3]. Yield estimation before harvest is becoming possible, with increasing accuracies when making use of non-invasive proximal remote sensing (PRS) technology and techniques [8; 9; 10; 11; 12]. A common PRS approach employs 2-D (2-dimensional) RGB (Red, Green, Blue) imagery, captured with a digital camera for yield estimation; for example [8; 9; 13]. The 2-D approach can be categorised into two steps: (i) image segmentation (to differentiate the bunch from the background); and (ii) yield estimation, using a suitable bunch metric (i.e., the pixel count of the bunch area in an image). [PERSON] et al. [13] used image classification (for segmentation) and a bunch metric to classify 'background noise' and 'grape' classes. The authors achieved a testing r\({}^{2}\) of 0.73. An alternative segmentation approach to image classification uses colour thresholding to differentiate grapes from the background. [PERSON] and [PERSON] [8] presented an RGB colour thresholding approach that applied specific thresholds to the colour properties of an RGB image, generating a binary image--background and grapes. The authors achieved an r\({}^{2}\) of 0.72. A similar thresholding approach was adopted by [PERSON], [PERSON] and [PERSON] [9] and [PERSON] et al. [14]. [PERSON], [PERSON] and [PERSON] [9] introduced a bunch-level experiment under laboratory conditions with manual colour thresholding for image segmentation, which resulted in a yield estimation r\({}^{2}\) of 0.77. Additionally, these authors [9] presented a more complex automatic process for image segmentation on the same dataset presented by [PERSON] and [PERSON] [8], resulting in an improved r\({}^{2}\) of 0.86. Automatic segmentation stemming from colour thresholding removes the human factor of manual thresholding, resulting in a more robust methodology [15; 16]. However, these techniques are more sophisticated than manual thresholding, and therefore require adequate knowledge of computer vision techniques.