Estimation of Fruit Load in Australian Mango Orchards Using Machine Vision

Agronomy

Andrew James Robson¹  Priyakant Sinha¹  Geoff Robert Dickinson²  Kerry Brian Walsh³  Marcelo Henrique Amaral³  Anand Koirala³  Zhenglin Wang³  Nicholas Todd Anderson³ 

[1] Applied Agricultural Remote Sensing Centre (AARSC), University of New England, Armidale 2351, Australia;Department of Agriculture and Fisheries, Queensland Government, Brisbane 4880, Australia;Institute for Future Farming Systems, Central Queensland University, Rockhampton 4701, Australia;

关键词: deep learning;    fruit-culture;    fruit load;    precision horticulture;    machine vision;    prediction quantification;   

DOI  :  10.3390/agronomy11091711

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown