Citation: Dhonju, H.K.; Bhattarai, T.; Amaral, M.H.; Matzner, M.; Walsh, K.B. Management Information Systems for Tree Fruit–2: Design of a Mango Harvest Forecast Engine. Horticulturae 2024, 10, 301. https:// doi.org/10.3390/horticulturae10030301 Academic Editors: Xinyang Yu and Long Li Received: 19 February 2024 Revised: 9 March 2024 Accepted: 19 March 2024 Published: 20 March 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). horticulturae Review Management Information Systems for Tree Fruit–2: Design of a Mango Harvest Forecast Engine Hari Krishna Dhonju 1, * , Thakur Bhattarai 1,2 , Marcelo H. Amaral 1 , Martina Matzner 1 and Kerry B. Walsh 1 1 Institute of Future Farming Systems, Central Queensland University, Rockhampton, QLD 4701, Australia; t.bhattarai@cqu.edu.au (T.B.); m.m.amaral@cqumail.com (M.H.A.); k.walsh@cqu.edu.au (K.B.W.) 2 Thomas Elder Sustainable Agriculture, Elders P/L, Adelaide, SA 5000, Australia * Correspondence: harikrishna.dhonju@cqumail.com; Tel.: +61-418-981-361 Abstract: Spatially enabled yield forecasting is a key component of farm Management Information Systems (MISs) for broadacre grain production, enabling management decisions such as variable rate fertilization. However, such a capability has been lacking for soft (fleshy)-tree-fruit harvest load, with relevant tools for automated assessment having been developed only recently. Such tools include improved estimates of the heat units required for fruit maturation and in-field machine vision for flower and fruit count and fruit sizing. Feedback on the need for and issues in forecasting were documented. A mango ‘harvest forecast engine’ was designed for the forecasting of harvest timing and fruit load, to aid harvest management. Inputs include 15 min interval temperature data per orchard block, weekly manual or machine-vision-derived estimates of flowering, and preharvest manual or machine-vision-derived estimates of fruit load on an orchard block level across the farm. Outputs include predicted optimal harvest time and fruit load, on a per block and per week basis, to inform harvest scheduling. Use cases are provided, including forecast of the order of harvest of blocks within the orchard, management of harvest windows to match harvesting resources such as staff availability, and within block spatial allocation of resources, such as adequate placement of harvest field bin and frost fans. Design requirements for an effective harvest MIS software artefact incorporating the forecast engine are documented, including an integrated database supporting spatial query, data analysis, processing and mapping, an integrated geospatial database for managing of large spatial–temporal datasets, and use of dynamic web map services to enable rapid visualization of large datasets. Keywords: estimation; fruit load; geospatial database; orchard; planning; prediction 1. Introduction 1.1. Need for Harvest Forecast Commercial orchards require management of irrigation, plant nutrition, disease and pests, and tree canopy architecture to meet agronomic needs, and documentation of labor and chemical usage to meet administrative requirements. As reviewed in a companion paper [1], the development of electronic Management Information Systems (MISs) for tree-fruit management lags behind that for broadacre cropping. The existing orchard management systems have focused on issues with regulatory requirements, e.g., chemical and labor usage, with capacities more recently developing around management decision support, e.g., when to spray chemicals based on weather and pest pressures inputs. Of the various management tasks involved in the production of soft tree fruit, the annual organization of harvesting is a major event. Harvesting and grading costs represent approximately 50% of total production costs for soft tree fruit [2], given the current need to hand pick most commodities. Summarizing the review of [3], harvest forecast is essential to the planning of on-farm resourcing (of labor and materials), transport and marketing, with all of these areas having lead times of week if not months. Harvest forecasts are Horticulturae 2024, 10, 301. https://doi.org/10.3390/horticulturae10030301 https://www.mdpi.com/journal/horticulturae