# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Machine Vision Methods
#### 2.4.3 Implementation on Ground Vehicles  
Initially, machine vision technology on ground vehicles was used in orchard fruit load estimation using a so-called 'Dual View' (DV) method, in which two images are collected per tree, one from each interrow. This approach has been superseded by the use of multiple frames of each side of each tree, obtained while driving through the orchard ('Multi View', (MV)), e.g., [PERSON] et al. [37] and [PERSON] et al. [50]. In this method, fruits are detected in each frame and the expected position of each fruit in subsequent frames is estimated. If  
Figure 4: Example of a machine vision system employing LED lighting, GNSS for geolocation and cameras facing in two directions, mounted to a tractor–spray rig. a fruit appears in the expected position, it is assumed to be the same fruit. If a fruit does not appear in the expected position after a set number of subsequent frames, the fruit is assumed to have passed from view, and the total fruit count is incremented by one. [PERSON] et al. [37] reported that the DV system achieved a higher repeatability (precision) but a lower accuracy (i.e., greater difference to reference count) than the MV system. Both DV and MV methods can suffer double counts of fruit, if seen from the two sides of the tree. This error can be reduced by imposing a size limit on detected fruit, as more distant fruit appear smaller, or by imposing a camera to fruit distance limit if the imaging system provides for distance measurement. Alternatively, fruit can be located in 3D space. For example, the localization of apples in 3D space was achieved using cameras positioned on an over-the-row gantry for viewing of both sides of the tree canopy simultaneously; however, a 21% error in identifying duplicate apples was reported due to a position error [51]. Within a system employing GNSS and an Inertial Navigation System, epipolar projection using the multiple RGB images collected for each tree in driving the inter-row was used to assign a 3D position, within 20 cm, to every fruit in a mango orchard [37]. Fruit in the same 3D position as estimated from images from the two inter-rows were assumed to be the same fruit. The same suite of technologies was compared to the use of a Semantic Structure From Motion (SSFM) algorithm for the estimation of the 3D position of fruit based on tracking between images acquired using a single camera [49]. The lower cost SSFM system achieved an R\({}^{2}\) = 0.78, RMSEP = 27.8 fruit/tree, and slope = 0.87 for fruit counts, relative to manual counts of 18 trees, while the multi-sensor imaging system achieved an R\({}^{2}\) = 0.88, RMSEP = 19.8 and slope = 0.97. Both DV and MV methods can fail to detect fruit occluded by other fruit or foliage, although this error is greatly decreased when using the MV method. In the MV example illustrated in Figure 5, estimates suffer a bias that represents 26.8% of the error, with the remaining 73.1% associated with random noise (precision) on an orchard of smaller, open trees, and a bias that represents 36.8% of the total uncertainty for an orchard with denser canopies. MV estimates can be corrected using an 'occlusion factor' estimated from the regression of a manual estimate of total fruit load to the machine vision estimate of a set of 'calibration' trees in a given orchard. For example, [PERSON] et al. [52] used a 'visibility function' to relate the visible estimate of fruit to the total fruit of grapevines. The relationship was site-specific, but consistent over seasons for a particular vineyard. Most wine grape and table grape producers manage vines and grape loading (using pruning and thinning) consistently from season to season, which would explain this finding. However, the estimation of an occlusion factor requires the tedious, manual counting of fruit on trees. There have therefore been attempts to develop a machine vision-based estimation of proportion of occluded fruit, e.g., through correlation to foliage density or the proportion of partly occluded fruit.