# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 6 Measurement of Fruit Maturity
### Thermal Time from Flowering
#### 6.1.2 Identifying Flowering Events  
A goal of farm management is to optimize flowering in terms of extent and period, using tools such as timing of pruning, water stress followed by relief of stress, trunk application of pacobutrazol, etc. However, flowering is rarely synchronous across a tree or between trees in an orchard. The flowering of a canopy or of an orchard may occur in 'waves' of different magnitudes. Commercial practice for identifying flowering events is typically periodic (e.g., weekly) visual observations of several rows in each orchard block to identify the date of the main flowering events. However, human assessment is dependent on the expertise and condition of the assessor.  
Machine vision has been used in the estimation of flowering level in several crop types, including apple, almond and mango [38; 40; 45; 113; 114; 115; 116]. [PERSON] et al. [117] provide a short review of the deep learning models used in the recognition of flowers across all crop types. Considerable work has been undertaken for the estimation of flowering load in apple, although the focus of this work is the control of flower thinning methods, which is undertaken to regulate fruit size, rather than as input to a harvest maturation time model. Broadly, two approaches have been applied--the measurement of flower to canopy area in images following the segmentation of flower and vegetative components of the canopy, and the detection of flowers or infloreserences as objects within an image.  
In an early attempt, free palmate trained apple trees were photographed from the side against a black background although 137 of 250 acquired images were not able to be used due to lighting problems related to sunlight [113]. The extent of flowering was based on the estimation of the area of flowers in each image, as derived from a segmentation based on grayscale values. Flower pixel count was correlated to fruit yield. The correlation of the ratio of pixel counts of infloreserences and of canopy per tree to a human assessment of % of terminals with an infloreserence across 48 trees was described by an R\({}^{2}\) = 0.87 and an RMSEP = 10.9% [118]. The use of FRCNN and SURF algorithms allowed the detection of mango panicles instead of pixel counts with a result of R\({}^{2}\) = 0.79 and an RMSEP = 35.3 panicles per tree against a field count of panicles of 48 trees [40]. Similarly, in recent work, a pixel-based classification of a 3D point cloud was recommended over an object-based estimation for the level of apple flowering. However, this approach depends on synchronous flowering, as pixel area per flower cluster will vary with the developmental stage of flowering. This was recognized by [PERSON] et al. [45] who noted that 'preconditions of the transferability of the developed method are the flower phenology stage'.  
The detection and count of mango panicles at three developmental stages was achieved using YOLOv3, a deep learning architecture, for the same dataset as used for panicle pixel area estimation by [PERSON] et al. [40]. The linear correlation of machine vision count to human count of panicles per tree across 48 trees was described by an R\({}^{2}\) = 0.80 and RMSEP = 35.6 panicles per tree for total panicle count per tree, with a precision of 69, 78, and 61% and a F1 score of 75, 79 and 70 for the three maturation stages, respectively. A time series of the frequency of the three stages was used to identify the times of peak flowering. Flower detection and count in apple canopy images has also been achieved using a CNN, with an average precision of 68% [114] and 59% [115] reported, and with YOLOv4, with mean average precision of 97.3% [116].  
There is thus potential for use of machine vision to augment human assessment of flowering. Beyond the assessment of the timing of peak flowering events in a block, the technology also has the capacity to map flowering load across a block. Such information can inform flower thinning and selective early harvest operations.