## 1 Introduction  
Accurate pre-harvest yield parameter estimation in high value fruit tree crops, such as avocado (_Persea americana_ cv. [PERSON]), offers improved decision making from the grower to the industry level. For individual orchards, a strong understanding of yield variability allows growers to form improved decisions regarding the variable rate application of inputs (water, fertilizer, pesticides) and the logistical planning of field operations (e.g., harvest scheduling, number of pickers and bins required, etc.) [1; 2; 3]. At the start of the picking season when the price of high quality fruit is at its premium, an understanding of fruit size distribution across an orchard prior to harvest can greatly reduce labour and fuel costs through selective harvesting [4]. Additionally, accurate yield estimation during a growing season supports post-harvest decisions such as the storage, handling, packing, and forward selling of produce [4; 5]. Currently, yield estimation in avocado orchards is undertaken by the visual counting of fruit growing on a small number of selected trees [4]. However, this method possesses several disadvantages: (1) poor accuracy as the avocado fruit is often occluded by leaves that are the same colour and shape; (2) time consuming and labour intensive, with many man hours required to undertake the counts on selected trees; (3) limited sample size, with the trees selected not accurately representing the high spatial and temporal variability of an entire orchard block. Crop simulation models have been developed for a number of tree crops to estimate fruit number based on (i) yield capacity, (ii) average fruit set density, and (iii) average weight of the fruit at harvest. The 'Bavendorf Crop Estimation Model' initially developed by [PERSON] [6] for apple and pear orchards was later evaluated for avocado by [PERSON] [3]. However, these methods are only conducted on a limited number of sample trees and therefore suffer the same inaccuracies as those identified by the visual count technique. [PERSON] et al. [7] and [PERSON] et al. [8] identified empirical relationships between fruit yield to number of new leaves and to number of flower buds after thinning in the spring. However, highly erroneous results occurred when the thinning process was not completed by spring. [PERSON] et al. [9] developed a 'pheno-physiological model' for 'cv. [PERSON] avocado grown in south-eastern Queensland Australia to establish a stronger understanding between physiological status of the tree in terms of its vegetative (roots and shoots) and reproductive (flowers and fruit) components to growth parameters including fruit set, fruit retention and yield. A relationship between the level of starch concentration in the tissues of the tree trunks immediately prior to flowering and subsequent fruit yield was identified. However, the occurrence of additional biotic or abiotic conditions between final crop set and harvest largely influenced the prediction accuracy. Digital imaging or machine-vision techniques using stereo and colour photographs have also been investigated for yield estimation and/or real-time yield mapping of a number of horticultural crops including wild blueberries [10] and 'Gala' apple [11]. However, occlusion of fruit by branches and leaves, variations in illumination conditions, and thresholding errors were identified to be sources of error [12; 13; 14; 15]. For avocado, fruit occlusion is likely to cause similar issues due to the leaves being the same shape and colour as the fruit, the fruit themselves growing inside the canopy, and the size of trees. Satellite and aerial remote sensing platforms present as an accurate and time efficient alternative to the manual fruit count method [4] and for the non- invasive measure of avocado yield.