# Web Mapping for Farm Management Information Systems: A Review and Australian Orchard Case Study
## 1 Introduction  
Context  
Current Global Navigation Satellite System (GNSS)-based geodetic techniques allow up to millimeter accuracy in positioning to be achieved globally, and high-precision mass-market positioning is becoming available to an accuracy of 5-10 cm [1]. In the case of 'build it and they will come', many applications have been built on this GNSS capacity, with applications increasingly requiring higher positioning accuracy [2, 3]. For example, auto-steer guidance of tractors has enabled controlled traffic in broadacre cropping, with vehicles following the same wheel tracks within a few centimeters to limit soil compaction [4], and UAV imagery can be used to map the position of small weeds for ground platform-based precision spraying [5]. GNSS-enabled applications are also developing in row and tree crop horticulture, e.g., linking chemical spray intensity for flowering thinning to machine vision and learning measurements of flower density [6] and the detection of weeds in real time [7].  
With an increase in spatially tagged data comes a need to visualize such data, as seen in commercial farm management information systems (FMIS). Web mapping resources, such as Google Maps, provide an interactive display of geographic data and informationin the form of a web page, enabling access from any internet-connected device [8]. Given their ubiquitous availability, web mapping resources are commonly used in FMIS, e.g., for denoting plot boundaries (e.g., for apple orchards [9]), displaying data (e.g., location of sensors for pest detection [10]), management functions (e.g., irrigation, [11]), and interpretation of data (e.g., geochemical data [12]). Most commercially available FMIS use web map resources, e.g., onside.com.nz (accessed on 3 July 2023); [[https://www.datafarming.com.au/](https://www.datafarming.com.au/)]([https://www.datafarming.com.au/](https://www.datafarming.com.au/)) (accessed on 3 July 2023).  
However, while position data can be very accurately assessed using GNSS services and tagged to field collected data, their accuracy can also be degraded in storage, processing, and delivery [13]. Indeed, the geodetic field is in a state of flux as the availability and use of high spatial resolution data increases.  
Given the expanding uses for sub-meter spatial resolution in agricultural applications, it is timely to summarize the technologies behind spatial data collection ('geodesy') and web mapping and consider limitations in the use of these resources in the context of use in FMIS. A history and description of GNSS technology and datums, web mapping, and the source of positional error is presented in Section 2, serving as a tutorial for researchers seeking to visualize geolocated data. The impact of approximations that are routinely made is also described, including those incurred in moving between reference frames and in the use of web mapping, including ellipsoid to spheroid conversion, georeferencing, and orthorectification errors. A case study involving Australian mango orchards is presented in Sections 3 and 4, documenting positional errors associated with web maps. This case study arises from our need to process and display machine vision-derived flower and fruit counts collected at multiple time points for entire orchards [3]. In this application, positional accuracy to around 1 m is required to maintain data at the 'tree level'. Section 4.5 provides recommendations on the procedures required to maintain positional accuracy for records collected across time within FMIS. Definition of terminologies used are listed in Appendix A. The manuscript structure is not that of a classic'review'; however, we believe this resource will be very useful to others working in this field.