Using multilevel systematic sampling to study apple fruit (Malus domestica Borkh.) quality and its variability at the orchard scale

Vega, M. V. M.; Wulfsohn, D.; Clemmensen, Line Katrine Harder; Toldam‐Andersen, Torben Bo

doi:10.1016/j.scienta.2013.06.020

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…In comparison, the winery's estimation of bunch counts for the same blocks required three days of effort by a five-person crew, equivalent to 16 days by a two-person team. Similarly, Martinez Vega et al [33] reported sampling of a 17 ha apple orchard using an SUR approach in 5 h. A marketable yield estimate of 356.6 ± 89.2 t compared well to the 374.9 t packed for export.…”

Section: Examplesmentioning

confidence: 83%

“…Manual estimates of fruit size of fruit on-tree require a sampling strategy, with attention to both variation between trees and to variation within a tree. The SUR sampling methodology used by Martinez Vega et al [33] provides one approach.…”

Section: Current (Manual) Methodsmentioning

confidence: 99%

“…Examples of commercial yield estimations using the SUR sampling method have been provided by Martinez Vega et al [33] and Wulfsohn and Lagos [34]. Surveys were carried out by two-person teams, with one person counting fruit on small portions of the trees and taking subsamples of fruit for the measurement of mass and other properties, the other person guiding the sampling and recording counts and masses using the app [25].…”

Section: Examplesmentioning

confidence: 99%

See 2 more Smart Citations

Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

Anderson

Walsh

Wulfsohn³

2021

Agronomy

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The management and marketing of fruit requires data on expected numbers, size, quality and timing. Current practice estimates orchard fruit load based on the qualitative assessment of fruit number per tree and historical orchard yield, or manually counting a subsample of trees. This review considers technological aids assisting these estimates, in terms of: (i) improving sampling strategies by the number of units to be counted and their selection; (ii) machine vision for the direct measurement of fruit number and size on the canopy; (iii) aerial or satellite imagery for the acquisition of information on tree structural parameters and spectral indices, with the indirect assessment of fruit load; (iv) models extrapolating historical yield data with knowledge of tree management and climate parameters, and (v) technologies relevant to the estimation of harvest timing such as heat units and the proximal sensing of fruit maturity attributes. Machine vision is currently dominating research outputs on fruit load estimation, while the improvement of sampling strategies has potential for a widespread impact. Techniques based on tree parameters and modeling offer scalability, but tree crops are complicated (perennialism). The use of machine vision for flowering estimates, fruit sizing, external quality evaluation is also considered. The potential synergies between technologies are highlighted.

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Section: Examplesmentioning

confidence: 83%

Section: Current (Manual) Methodsmentioning

confidence: 99%

Section: Examplesmentioning

confidence: 99%

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Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

Anderson

Walsh

Wulfsohn³

2021

Agronomy

Self Cite

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“…Martínez Vega et al [97] has advocated the use of a multi-stage sampling techniq termed Systematic Uniform Random Sampling. This method involves systematic sa pling of trees, branches and branch segments, and simple random sampling of fruit with the branch segment [97]. The periodicity of sampling requires prior knowledge of t source of variation (within vs. between trees).…”

mentioning

confidence: 99%

“…The method involves measurement o high number of the sampling units, distributed through the volume of the tree canopi The method resulted in a decrease in sample size compared to a random sampling str egy [1] and made the task of locating sampling units (tree branch segments) easi Martínez Vega et al [97] has advocated the use of a multi-stage sampling technique termed Systematic Uniform Random Sampling. This method involves systematic sampling of trees, branches and branch segments, and simple random sampling of fruit within the branch segment [97]. The periodicity of sampling requires prior knowledge of the source of variation (within vs. between trees).…”

mentioning

confidence: 99%

Fruit Sizing in Orchard: A Review from Caliper to Machine Vision with Deep Learning

Neupane

Pereira

Koirala

et al. 2023

Sensors

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Forward estimates of harvest load require information on fruit size as well as number. The task of sizing fruit and vegetables has been automated in the packhouse, progressing from mechanical methods to machine vision over the last three decades. This shift is now occurring for size assessment of fruit on trees, i.e., in the orchard. This review focuses on: (i) allometric relationships between fruit weight and lineal dimensions; (ii) measurement of fruit lineal dimensions with traditional tools; (iii) measurement of fruit lineal dimensions with machine vision, with attention to the issues of depth measurement and recognition of occluded fruit; (iv) sampling strategies; and (v) forward prediction of fruit size (at harvest). Commercially available capability for in-orchard fruit sizing is summarized, and further developments of in-orchard fruit sizing by machine vision are anticipated.

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Assessing ranked set sampling and ancillary data to improve fruit load estimates in peach orchards

Uribeetxebarria

Martínez‐Casasnovas

Tisseyre

et al. 2019

Computers and Electronics in Agriculture

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Using multilevel systematic sampling to study apple fruit (Malus domestica Borkh.) quality and its variability at the orchard scale

Cited by 6 publications

References 19 publications

Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

Fruit Sizing in Orchard: A Review from Caliper to Machine Vision with Deep Learning

Assessing ranked set sampling and ancillary data to improve fruit load estimates in peach orchards

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