# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Machine Vision Methods
#### 2.4.2 Hardware and Imaging Platform  
One implementation of machine vision is within a handheld camera system, with the assessment of a sample of trees in the orchard. These systems can use edge processing to process images locally, using a shallow architecture, or rely on cellular network coverage or Wi-Fi to transfer images for cloud processing where a deeper architecture can be employed. Edge processing is attractive to give immediate results to the user, and to avoid the poor connectivity issues experienced on many orchards. [PERSON] et al. [42] described an Android phone-based system for counting kiwifruit on vine, although the detection rate was only 76% of the human count across 100 images. [PERSON] et al. [43] describe a mobile phone-based system to count fruit in images of mango trees, coupled with the use of an occlusion factor to estimate tree yield. Single point of view images of tree fruit canopies will, however, have a high occlusion factor on the actual tree fruit load. There is the potential for the use of video of a 'walk around' the tree, with post-processing for the counting of fruit in a generated 3D image. Camera chest harness supports can be used to aid such a task. Camera systems have been also mounted onto farm vehicles and used in the imaging of entire orchards. The basic hardware required of such a system is an RGB camera of adequate resolution to allow for the detection of fruit and a recording system able to tolerate the rigors of field use. Consideration must be given to camera pixel resolution, camera field of view, which requires consideration of lens focal length, camera to canopy distance and canopy height. For example, when using a 5 MP camera with 2.2 by 2.2 um pixels at a canopy distance of 2 m equipped with a lens with 6 mm focal length to achieve a field of view for imaging a 4 m high canopy, a 100 mm diameter fruit will be represented by an image 12 pixels in width. A side facing camera mounted to a vehicle is appropriate for most tree fruit crops, but some crops may require a different camera orientation. For example, for the vine crop of kiwifruit, cameras can be mounted to look upwards to hanging fruit [44]. Multiple cameras may be also employed, for the purpose of either achieving views of both rows when driving through an inter-row, or for stacking vertically to achieve coverage of a high canopy. The use of cameras facing in opposite directions to image both rows from one inter-row position requires a row spacing and tree height matched to the camera field of view. A GNSS device is typically employed to allow the geolocation of every acquired frame. LiDAR has also been employed in addition to RGB imaging [37; 38], allowing the development of a canopy mask for associating detected fruits to individual canopies. To reduce effort, there is potential to mount the imaging systems onto farm vehicles moving through the orchard for other purposes, e.g., onto a tractor undertaking spraying, given the camera enclosures are placed at a distance from the spray zone (Figure 4). Some researchers have trialed the use of aerial platforms, particularly UAVs, for carriage of the imaging systems. Given weight and power constraints, imaging from such platforms cannot utilize artificial lighting. Another issue for use of a UAV is the visibility of flowers or fruit in-tree when viewed from above, as opposed to a side of tree view. A 3D object-based method using multi view perspectives from drone imagery outperformed a 2D top-view (R\({}^{2}\) > 0.7 compared to 0.53, against field-based counts) for the estimation of pear flower cluster number per tree [45]. [PERSON] et al. [46] report that a 3D reconstructed image from an aerial view accounted for only 27% of fruit on the 19 apple trees assessed, with an R\({}^{2}\) of 0.80, MAE of 129 and RMSE of 131 fruit per tree achieved for a linear regression of machine vision estimated fruit counts against hand harvest counts. Trees had an average of 255 fruit/tree. This accuracy was acknowledged as inadequate for the task of yield prediction.