No abstract

The performance of a multi-view machine vision method was documented at an orchard level, relative to packhouse count. High repeatability was achieved in night-time imaging, with an absolute percentage error of 2% or less. Canopy architecture impacted performance, with reasonable estimates achieved on hedge, single leader and conventional systems (3.4, 5.0, and 8.2 average percentage error, respectively) while fruit load of trellised orchards was over-estimated (at 25.2 average percentage error). Yield estimations were made for multiple orchards via: (i) human count of fruit load on ~5% of trees (FARM), (ii) human count of 18 trees randomly selected within three NDVI stratifications (CAL), (iii) multi-view counts (MV-Raw) and (iv) multi-view corrected for occluded fruit using manual counts of CAL trees (MV-CAL). Across the nine orchards for which results for all methods were available, the FARM, CAL, MV-Raw and MV-CAL methods achieved an average percentage error on packhouse counts of 26, 13, 11 and 17%, with SD of 11, 8, 11 and 9%, respectively, in the 2019–2020 season. The absolute percentage error of the MV-Raw estimates was 10% or less in 15 of the 20 orchards assessed. Greater error in load estimation occurred in the 2020–2021 season due to the time-spread of flowering. Use cases for the tree level data on fruit load was explored in context of fruit load density maps to inform early harvesting and to interpret crop damage, and tree frequency distributions based on fruit load per tree.

No abstract

Forecast of tree fruit yield requires prediction of harvest time fruit size as well as fruit number. Mango (Mangifera indica L.) fruit mass can be estimated from correlation to measurements of fruit length (L), width (W) and thickness (T). On-tree measurements of individually tagged fruit were undertaken using callipers at weekly intervals until the fruit were past commercial maturity, as judged using growing degree days (GDD), for mango cultivars ‘Honey Gold’, ‘Calypso’ and ‘Keitt’ at four locations in Australia and Brazil during the 2020/21 and 21/22 production seasons. Across all cultivars, the linear correlation of fruit mass to LWT was characterized by a R2 of 0.99, RMSE of 29.9 g and slope of 0.5472 g/cm3, while the linear correlation of fruit mass to )2, mimicking what can be measured by machine vision of fruit on tree, was characterized by a R2 of 0.97, RMSE of 25.0 g and slope of 0.5439 g/cm3. A procedure was established for the prediction of fruit size at harvest based on measurements made five and four or four and three weeks prior to harvest (approx. 514 and 422 GDD, before harvest, respectively). Linear regression models on weekly increase in fruit mass estimated from lineal measurements were characterized by an R2 > 0.88 for all populations, with an average slope (rate of increase) of 19.6 ± 7.1 g/week, depending on cultivar, season and site. The mean absolute percentage error for predicted mass compared to harvested fruit weight for estimates based on measurements of the earlier and later intervals was 16.3 ± 1.3% and 4.5 ± 2.4%, respectively. Measurement at the later interval allowed better accuracy on prediction of fruit tray size distribution. A recommendation was made for forecast of fruit mass at harvest based on in-field measurements at approximately 400 to 450 GDD units before harvest GDD and one week later.

A forward estimate of mango (Mangifera indica L.) harvest timing is required for farm management (e.g., for organization of harvest labour and marketing). This forward estimate can be based on accumulated growing degree days (GDD) from an early stage of flowering to fruit harvest maturity, with fruit maturity judged on a destructive assessment of flesh colour and dry matter content. The current study was undertaken to improve GDD targets for Australian mango cultivars, to improve estimation of harvest maturity, and to document a methodology recommended for future work characterizing fruit maturation GDD for other mango cultivars. An alternate algorithm on GDD calculation involving use of a function that penalizes high temperatures as well as low temperatures was demonstrated to better predict harvest maturity in warmer climates. Across multiple locations and seasons, the required heat units (GDD, Tb = 12 °C, TB = 32 °C; where TB is upper base temperature of 32 °C and Tb is lower base temperature of 12 °C) to achieve maturity from asparagus stage of flowering was documented as 2185, 1728, and 1740 for the cultivars Keitt, Calypso and Honey Gold, respectively. GDD difference between the asparagus and two-thirds floral opening stages of flowering was 188 ± 18 for Calypso, 184 ± 12 for Honey Gold, 238 ± 21 for Keitt and 175 ± 10 for KP. Colour specifications for a colour card set suitable for maturity assessment of all cultivars was also proposed. A flesh colour harvest maturity card specification of 9 was proposed for the cultivar Honey Gold and 13 for the cultivar Keitt.