Review on crop load management in apple (<i>Malus</i> x <i>domestica</i> Borkh.)

Verma, Pramod; Sharma, Shivani; Sharma, Nivedita; Chauhan, N. P. S.

doi:10.1080/14620316.2022.2149425

Cited by 4 publications

(2 citation statements)

References 152 publications

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“…For some fruit crops, cultivation and management strategies may vary according to the final destination, use, and quality of the produce (Minas et al, 2018;Musacchi and Serra, 2018;Sidu et al, 2022Pereyra et al, 2022Reynolds, 2022;Verma et al, 2023). These management variations may also need to be considered for pest surveillance.…”

Section: Agroecosystems and Main Usesmentioning

confidence: 99%

Characterisation of fruit trees in the EU: a tool for crop‐based survey of Union quarantine pests

de la Peña,

Petri,

Diaz‐Pendón

et al. 2024

EFS3

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This technical report was prepared in the context of the EFSA mandate on plant pest surveillance (M‐2020‐0114) at the request of the European Commission. Its purpose is to support Member States in planning and preparing crop‐based surveys of Union quarantine pests in line with Commission Implementing Regulation (EU) 2019/2072. In particular, the report includes a list of species reported as hosts of the regulated pests targeted by the call: GP/EFSA/PLANTS/2022/05 – 'Development of crop‐based survey tools for plant pests of fruit trees, conifers, and palms and ornamentals in the EU' – Lot 1. Additionally, a classification of their occurrence in the EU is provided. The report provides basic information regarding the cultivation in the EU of 19 species of fruit trees widely cultivated in the EU, hosting at least one pest of the call in the families of the Ebenaceae, i.e. persimmon (Diospyros kaki L. f.); Ericaceae, i.e. bilberry (Vaccinium myrtillus L.), high‐bush blueberry (Vaccinium corymbosum); Lythraceae, i.e. pomegranate (Punica granatum L.); Moraceae, i.e. silkworm mulberry (Morus alba), fig (Ficus carica L.); Rosaceae, i.e. apple (Malus domestica Borkh.), pear (Pyrus communis L.), quince (Cydonia oblonga Mill.), plum (Prunus domestica), almond (Prunus dulcis Miller D.A. Webb.), apricot (Prunus armeniaca L.), sour cherry (Prunus cerasus), peach (Prunus persica (L.) Batsch), sweet cherry (Prunus avium L.), Japanese plum (Prunus salicina Lindl.), loquat (Eriobotrya japonica (Thunb.) Lindl.); Rutaceae, i.e. bitter orange (Citrus × aurantium L.); and Vitaceae, i.e. grape (Vitis vinifera). For the selection of cultivated fruit‐tree species, information on the main phenological stages, management practices, agroecosystems, climate suitability and distribution, regulatory requirements and trade of key commodities are provided.

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Section: Agroecosystems and Main Usesmentioning

confidence: 99%

Characterisation of fruit trees in the EU: a tool for crop‐based survey of Union quarantine pests

de la Peña,

Petri,

Diaz‐Pendón

et al. 2024

EFS3

View full text Add to dashboard Cite

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“…There is a positive correlation between crop load and high fruit yield [4]. Crop load is a common parameter used in the industry that and can be defined simply as the number of fruits per tree or, more commonly, as the number of fruits per trunk cross-sectional area (fruit/TCSA) [5][6][7]. TCSA is often used to define the optimum target crop load for a tree or an orchard.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Computer Vision Systems and Applications to Estimate Trunk Cross-Sectional Area, Flower Cluster Number, Thinning Efficacy and Yield of Apple

Gonzalez Nieto,

Wallis,

Clements

et al. 2023

Horticulturae

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Precision crop load management of apple requires counting fruiting structures at various times during the year to guide management decisions. The objective of the current study was to evaluate the accuracy of and compare different commercial computer vision systems and computer applications to estimate trunk cross-sectional area (TCSA), flower cluster number, thinning efficacy, and yield estimation. These studies evaluated two companies that offer different vision systems in a series of trials across 23 orchards in four states. Orchard Robotics uses a proprietary camera system, and Pometa (previously Farm Vision) uses a cell phone camera system. The cultivars used in the trials were ‘NY1’, ‘NY2’, ‘Empire’, ‘Granny Smith’, ‘Gala’, ‘Fuji’, and ‘Honeycrisp’. TCSA and flowering were evaluated with the Orchard Robotics camera in full rows. Flowering, fruit set, and yield estimation were evaluated with Pometa. Both systems were compared with manual measurements. Our results showed a positive linear correlation between the TCSA with the Orchard Robotics vision system and manual measurements, but the vision system underestimated the TCSA in comparison with the manual measurements (R2s between 0.5 and 0.79). Both vision systems showed a positive linear correlation between nubers of flowers and manual counts (R2s between 0.5 and 0.95). Thinning efficacy predictions (in June) were evaluated using the fruit growth rate model, by comparing manual measurements and the MaluSim computer app with the computer vision system of Pometa. Both systems showed accurate predictions when the numbers of fruits at harvest were lower than 200 fruit/tree, but our results suggest that, when the numbers of fruits at harvest were higher than 200 fruit/tree, both methods overestimated final fruit numbers per tree when compared with final fruit numbers at harvest (R2s 0.67 with both systems). Yield estimation was evaluated just before harvest (August) with the Pometa system. Yield estimation was accurate when fruit numbers were fewer than 75 fruit per tree, but, when the numbers of fruit at harvest were higher than 75 fruit per tree, the Pometa vision system underestimated the final yield (R2 = 0.67). Our results concluded that the Pometa system using a smartphone offered advantages such as low cost, quick access, simple operation, and accurate precision. The Orchard Robotics vision system with an advanced camera system provided more detailed and accurate information in terms of geo-referenced information for individual trees. Both vision systems evaluated are still in early development and have the potential to provide important information for orchard managers to improve crop load management decisions.

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Advances in apple’s automated orchard equipment: A comprehensive research

Mhamed,

Zhang,

et al. 2024

Computers and Electronics in Agriculture

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Review on crop load management in apple (Malus x domestica Borkh.)

Cited by 4 publications

References 152 publications

Characterisation of fruit trees in the EU: a tool for crop‐based survey of Union quarantine pests

Characterisation of fruit trees in the EU: a tool for crop‐based survey of Union quarantine pests

Evaluation of Computer Vision Systems and Applications to Estimate Trunk Cross-Sectional Area, Flower Cluster Number, Thinning Efficacy and Yield of Apple

Advances in apple’s automated orchard equipment: A comprehensive research

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