# Management Information Systems for Tree Fruit--1: A Review
## 3 Literature Evaluation
### Management Aims
#### 3.5.4 Plant Development  
Publications in the 'Plant development' category dealt with a selection of germplasm/ cultivars, planting issues, prediction of growth, flowering or fruiting, and thinning requirements. Several publications did not target orchard management per se but rather the management tasks in the other areas of the value chain, e.g., bayberry breeding [58]. Examples of models that could be used within an orchard MIS include a method for yield estimation of apple trees [90], a carbon balance model for use in guiding apple tree thinning [13], a model for apple and pear growth [55; 91], apple and orange yield estimations [14; 56; 87], an agent-based decision support system for mango flower initiation [92] and a tool for the forward estimation of mango harvest timing [93]. The most comprehensive management system was that of [PERSON] et al. [94], who applied research on plant growth modelling within a framework to enable management decisions on, e.g., pruning, irrigation, fertilisation, yield prediction and cultivation. Publications addressing harvest management were categorised as related to the estimation of fruit maturity for the timing of harvest, estimation of on-tree fruit load or the management activity during the harvest. These reports document the development of sensors and methods, with a number of studies also exploring the presentation of data to users. In particular, methods based on machine vision are rapidly developing for a direct assessment of tree-fruit attributes, underpinning the forecast of crop timing and loads. Indeed, a number of machine vision-based commercial systems have recently become available for in-orchard use, as reviewed by [PERSON] et al. [18]. The timing of the 'decision to pick' requires information on fruit maturation. This can be estimated based on the heat unit requirements between flowering and fruit harvest maturity, e.g., [PERSON] et al. [95] and [PERSON] et al. [22], given the measurements of orchard temperatures. Depending on the commodity, fruit maturity can also be assessed based on the level of storage reserves, fruit skin colour, flesh colour and/or fruit shape. For example, the classification of melon ripeness/maturity from field imagery of the fruit surface was reported to support the estimation of optimum harvest timing [14]. The development of portable, non-invasive assessment technologies has enabled the inclusion of internal attribute specifications into farm management systems. For example, a hand-held device for the assessment of the ratio of absorbances at two wavelengths was used to assess flesh pigment levels in fruit [96], and the use of hand-held near-infrared spectrometry has been recommended for the estimation of fruit storage reserve levels as a measure of fruit maturity [97], e.g., of olive fruit [98]. An online MIS was developed for the optimisation of olive orchard harvesting orders based on monitoring networks for environmental data and physio-chemical fruit analysis [99]. In other examples, a flower initiation model was adopted into a decision support system for mango production in a greenhouse environment [92], and a web-based MIS for mango harvest time forecasting, based on non-invasive in-field measurements of fruit dry matter, was implemented [70]. A MIS has also been developed that provides a financial assessment of the value of the use of sensor technologies in harvest timing [100]. The estimation of harvest loads, i.e., the fruit number per tree and the fruit size, can be based on manual assessments. [PERSON] et al. (2018) report on sampling strategies that are appropriate for use in these applications, with commercial use exemplified by Pronofrut (San Fernando, Chile) ([[https://pronofrut.cl/en](https://pronofrut.cl/en)]([https://pronofrut.cl/en](https://pronofrut.cl/en))) (accessed on 18 December 2023) in which a yield estimation support service is provided for the bud, flower and fruit counts and sizing using multistage systematic sampling designs and manual measurements.