A Cloud-Based Environment for Generating Yield Estimation Maps From Apple Orchards Using UAV Imagery and a Deep Learning Technique

Apolo-Apolo, O.E.; Pérez-Ruiz, M.; Martínez-Guanter, Jorge; Valente, João

doi:10.3389/fpls.2020.01086

Cited by 68 publications

(41 citation statements)

References 61 publications

(89 reference statements)

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“…A 3D object-based method using multi view perspectives from drone imagery outperformed a 2D top-view (R 2 > 0.7 compared to 0.53, against field-based counts) for the estimation of pear flower cluster number per tree [45]. Apolo-Apolo et al [46] report that a 3D reconstructed image from an aerial view accounted for only 27% of fruit on the 19 apple trees assessed, with an R 2 of 0.80, MAE of 129 and RMSE of 131 fruit per tree achieved for a linear regression of machine vision estimated fruit counts against hand harvest counts. Trees had an average of 255 fruit/tree.…”

Section: Implementation On Ground Vehiclesmentioning

confidence: 99%

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: Implementation On Ground Vehiclesmentioning

confidence: 99%

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

Anderson

Walsh

Wulfsohn³

2021

Agronomy

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“…Individual LA of trees of the fully developed canopies may be estimated from the early season LA or the number of spurs and extensions that shoots in a growth model of growing degree days (Lakso and Johnson, 1990 ). Furthermore, when the actual crop load data of each individual tree will become available (Apolo-Apolo et al, 2020 ; Tsoulias et al, 2020a ), the difference between FBC and the actual crop load will provide a decision support for each individual tree, enabling VR thinning.…”

Section: Discussionmentioning

confidence: 99%

“…The mapping of canopy and yield parameters within an orchard can be performed by georeferencing each tree and the application of remote sensing, e.g., based on photogrammetry (Mu et al, 2018 ), time-of-flight reading (Coupel-Ledru et al, 2019 ; Tsoulias et al, 2019 ), or thermal imaging (Huang et al, 2020 ). Most recently, the number of flower clusters (Vanbrabant et al, 2020 ) and fruit per tree (Apolo-Apolo et al, 2020 ; Tsoulias et al, 2020a ) were mapped in pome fruit orchards by analyzing the point clouds generated from RGB images or a light detection and ranging (LiDAR) analysis. The sensors may be mounted on various platforms, i.e., ground or aerial vehicles, or satellites, and the measurements carried out throughout the growth season (Zude-Sasse et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Modeling of Individual Fruit-Bearing Capacity of Trees Is Aimed at Optimizing Fruit Quality of Malus x domestica Borkh. ‘Gala’

et al. 2021

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The capacity of apple trees to produce fruit of a desired diameter, i.e., fruit-bearing capacity (FBC), was investigated by considering the inter-tree variability of leaf area (LA). The LA of 996 trees in a commercial apple orchard was measured by using a terrestrial two-dimensional (2D) light detection and ranging (LiDAR) laser scanner for two consecutive years. The FBC of the trees was simulated in a carbon balance model by utilizing the LiDAR-scanned total LA of the trees, seasonal records of fruit and leaf gas exchanges, fruit growth rates, and weather data. The FBC was compared to the actual fruit size measured in a sorting line on each individual tree. The variance of FBC was similar in both years, whereas each individual tree showed different FBC in both seasons as indicated in the spatially resolved data of FBC. Considering a target mean fruit diameter of 65 mm, FBC ranged from 84 to 168 fruit per tree in 2018 and from 55 to 179 fruit per tree in 2019 depending on the total LA of the trees. The simulated FBC to produce the mean harvest fruit diameter of 65 mm and the actual number of the harvested fruit >65 mm per tree were in good agreement. Fruit quality, indicated by fruit's size and soluble solids content (SSC), showed enhanced percentages of the desired fruit quality according to the seasonally total absorbed photosynthetic energy (TAPE) of the tree per fruit. To achieve a target fruit diameter and reduce the variance in SSC at harvest, the FBC should be considered in crop load management practices. However, achieving this purpose requires annual spatial monitoring of the individual FBC of trees.

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“…The fruit size and yield of citrus and apple were estimated based on region CNN (RCNN); its detection accuracy reached 90%. This method facilitates fruit classification and picking and is vital for the precision marketing of fruit products (Apolo et al., 2020a; Apolo et al., 2020b). Based on mask images from mask RCNN, a visual localization method for picking points has been applied to ripe strawberries.…”

Section: Smart Horticulture Is the Trend Of The Futurementioning

confidence: 99%

Combining novel technologies with interdisciplinary basic research to enhance horticultural crops

et al. 2021

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SUMMARY Horticultural crops mainly include fruits, vegetables, ornamental trees and flowers, and tea trees (Melaleuca alternifolia). They produce a variety of nutrients for the daily human diet in addition to the nutrition provided by staple crops, and some of them additionally possess ornamental and medicinal features. As such, horticultural crops make unique and important contributions to both food security and a colorful lifestyle. Under the current climate change scenario, the growing population and limited arable land means that agriculture, and especially horticulture, has been facing unprecedented challenges to meet the diverse demands of human daily life. Breeding horticultural crops with high quality and adaptability and establishing an effective system that combines cultivation, post‐harvest handling, and sales becomes increasingly imperative for horticultural production. This review discusses characteristic and recent research highlights in horticultural crops, focusing on the breeding of quality traits and the mechanisms that underpin them. It additionally addresses challenges and potential solutions in horticultural production and post‐harvest practices. Finally, we provide a prospective as to how emerging technologies can be implemented alongside interdisciplinary basic research to enhance our understanding and exploitation of horticultural crops.

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A Cloud-Based Environment for Generating Yield Estimation Maps From Apple Orchards Using UAV Imagery and a Deep Learning Technique

Cited by 68 publications

References 61 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

Modeling of Individual Fruit-Bearing Capacity of Trees Is Aimed at Optimizing Fruit Quality of Malus x domestica Borkh. ‘Gala’

Combining novel technologies with interdisciplinary basic research to enhance horticultural crops

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