Benchmarking new methods for estimation of quantity and harvest timing of the mango crop

Prediction of the optimal time of harvest and the harvest fruit load is required by mango orchard managers. Four technologies were assessed: (i) field temperature sensors and heat units; (ii) non-destructive assessment of fruit flesh colour; (iii) machine vision for flowering and fruit load assessment; and (iv) fruit size estimation

Benchmarking new methods for estimation of quantity and harvest timing of the mango crop: Dataset

A forward estimate of mango fruit harvest volume and scheduling is required for farm management, for organization in terms of labour planning and market sales. Harvest timing estimation in mango production is currently achieved using accumulated growing degree days (GDD) from from early stages of flower development, non-destructive estimates of fruit dry matter content by handheld near infra-red spectrometry, and destructive assessment of internal flesh colour. For fruit load estimation, current best practice involves manual counting of total fruit per tree. A range of technologies are becoming available that have relevance to assessment of mango crop harvest timing and fruit load forecast. Four activities were undertaken to assess relevant technologies: (i) A hardware system based on LoRa connected temperature sensors was characterised and recommended for field use based on measurement accuracy, battery life and reception range. An alternative algorithm on GDD calculation involving use of a function that penalises high temperatures as well as low temperatures was demonstrated to better predict harvest maturity in warmer climates. Required heat units (GDD, Tb = 12 °C, TB =32 °C) to achieve maturity were documented as 2185, 1728 and 1740 for the cultivars Keitt, Calypso, and Honey Gold, respectively. (ii) Vis-NIR spectrometry was trialled for non-invasive assessment of fruit flesh colour in the context of harvest maturity estimation, using a data set of 2034 spectra from 19 populations, where a population is an orchard/season/flowering event. The best leave-one-out-population cross validation prediction result was obtained using a Support Vector Regression (R2 of 0.63 and RMSEP of 5.52 on CIE B). However, this performance was inadequate for recommendation for use in non-invasive assessment of fruit maturity, which requires estimation to within 2.0 CIE B units. (iii) A procedure for prediction of fruit size at harvest based on measurements made prior to harvest was established, based a linear growth model for weight increment. The procedure was demonstrated for Honey Gold, Calypso and Keitt populations, with estimation error of 8.64 ± 13.7% and 0.61 ± 4.7% for measurements made between either five and four, or four and three weeks before harvest, respectively. (iv) A procedure for use of in-field machine vision-based count of fruit on tree in estimation of orchard fruit load was established, based on use of imaging on two dates to capture fruit arising from different flowering events. The two imaging estimations were accurate estimates of total orchard fruit load as measured by packhouse count, with R2 of 0.98 and slope of 0.99 across six orchards. These four activities demonstrate the potential of new technologies for improved estimation of harvest timing and load

estimation of quantity and harvesttiming of the mango crop

A forward estimate of mango fruit harvest volume and scheduling is required for farm management, for organization in terms of labour planning and market sales. Harvest timing estimation in mango production is currently achieved using accumulated growing degree days (GDD) from from early stages of flower development, non-destructive estimates of fruit dry matter content by handheld near infra-red spectrometry, and destructive assessment of internal flesh colour. For fruit load estimation, current best practice involves manual counting of total fruit per tree. A range of technologies are becoming available that have relevance to assessment of mango crop harvest timing and fruit load forecast. Four activities were undertaken to assess relevant technologies: (i) A hardware system based on LoRa connected temperature sensors was characterised and recommended for field use based on measurement accuracy, battery life and reception range. An alternative algorithm on GDD calculation involving use of a function that penalises high temperatures as well as low temperatures was demonstrated to better predict harvest maturity in warmer climates. Required heat units (GDD, Tb = 12 °C, TB =32 °C) to achieve maturity were documented as 2185, 1728 and 1740 for the cultivars Keitt, Calypso, and Honey Gold, respectively. (ii) Vis-NIR spectrometry was trialled for non-invasive assessment of fruit flesh colour in the context of harvest maturity estimation, using a data set of 2034 spectra from 19 populations, where a population is an orchard/season/flowering event. The best leave-one-out-population cross validation prediction result was obtained using a Support Vector Regression (R2 of 0.63 and RMSEP of 5.52 on CIE B). However, this performance was inadequate for recommendation for use in non-invasive assessment of fruit maturity, which requires estimation to within 2.0 CIE B units. (iii) A procedure for prediction of fruit size at harvest based on measurements made prior to harvest was established, based a linear growth model for weight increment. The procedure was demonstrated for Honey Gold, Calypso and Keitt populations, with estimation error of 8.64 ± 13.7% and 0.61 ± 4.7% for measurements made between either five and four, or four and three weeks before harvest, respectively. (iv) A procedure for use of in-field machine vision-based count of fruit on tree in estimation of orchard fruit load was established, based on use of imaging on two dates to capture fruit arising from different flowering events. The two imaging estimations were accurate estimates of total orchard fruit load as measured by packhouse count, with R2 of 0.98 and slope of 0.99 across six orchards. These four activities demonstrate the potential of new technologies for improved estimation of harvest timing and load

Estimation of fruit load in Australian mango orchards using machine vision

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