| [1] |
U.S. Department of Agriculture, National Agricultural Library. 2015. USDA pomological watercolor collection. https://agdatacommons.nal.usda.gov/articles/dataset/U_S_Department_of_Agriculture_USDA_Pomological_Watercolor_Collection/24660282 |
| [2] |
Georgiev P. 2021. An Illustrated Catalog of American Fruits & Nuts: The U. S. Department of Agriculture Pomological Watercolor Collection. Los Angeles: Atelier Éditions. https://atelier-editions.com/products/an-illustrated-catalog-of-american-fruits-nuts |
| [3] |
Volk GM, Chao CT, Norelli J, Brown SK, Fazio G, et al. 2015. The vulnerability of US apple (Malus) genetic resources. |
| [4] |
Gutierrez B, Battaglia K, Zhong GY. 2020. Preserving the future with the USDA plant genetic resource unit tart cherry, grape, and apple germplasm collections. |
| [5] |
Dougherty L, Wallis A, Cox K, Zhong GY, Gutierrez B. 2021. Phenotypic evaluation of fire blight outbreak in the USDA Malus collection. |
| [6] |
USDA Agricultural Research Service. 2015. Germplasm Resources Information Network (GRIN). https://data.nal.usda.gov/dataset/germplasm-resources-information-network-grin |
| [7] |
Routson KJ, Reilley AA, Henk AD, Volk GM. 2009. Identification of historic apple trees in the southwestern United States and implications for conservation. |
| [8] |
Volk GM, Henk AD. 2016. Historic American apple cultivars: identification and availability. |
| [9] |
Gross BL, Volk GM, Richards CM, Forsline PL, Fazio G, et al. 2012. Identification of “duplicate” accessions within the USDA-ARS national plant germplasm system Malus collection. |
| [10] |
Volk GM, Peace CP, Henk AD, Howard NP. 2022. DNA profiling with the 20K apple SNP array reveals Malus domestica hybridization and admixture in M. sieversii, M. orientalis, and M. sylvestris genebank accessions. |
| [11] |
Underhill AN, Hirsch CD, Clark MD. 2020. Evaluating and mapping grape color using image-based phenotyping. |
| [12] |
Li X, Ru S, He Z, Spiers JD, Xiang L. 2025. High-throughput phenotyping tools for blueberry count, weight, and size estimation based on modified YOLOv5s. |
| [13] |
Li M, Frank MH, Migicovsky Z. 2022. ColourQuant: a high-throughput technique to extract and quantify color phenotypes from plant images. High-Throughput Plant Phenotyping: Methods and Protocols. New York: Springer US. pp. 77−85 doi: 10.1007/978-1-0716-2537-8_9 |
| [14] |
Kirchgessner N, Hodel M, Studer B, Patocchi A, Broggini GAL. 2024. FruitPhenoBox–a device for rapid and automated fruit phenotyping of small sample sizes. |
| [15] |
Bouillon P, Fanciullino AL, Belin E, Bréard D, Boisard S, et al. 2024. Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity. |
| [16] |
Migicovsky Z, Li M, Chitwood DH, Myles S. 2017. Morphometrics reveals complex and heritable apple leaf shapes. |
| [17] |
Keller B, Jung M, Bühlmann-Schütz S, Hodel M, Studer B, et al. 2024. The genetic basis of apple shape and size unraveled by digital phenotyping. |
| [18] |
Bouillon P, Fanciullino AL, Belin E, Hanteville S, Muranty H, et al. 2024. Tracing the color: quantitative trait loci analysis reveals new insights into red-flesh pigmentation in apple (Malus domestica). |
| [19] |
DeViller K, Chitwood DH, Myles S, Li M, Migicovsky Z. 2025. Differences in apple fruit shape are independent of fruit size. |
| [20] |
Metuarea H, Laurens F, Guerra W, Lozano L, Patocchi A, et al. 2025. Individual segmentation of intertwined apple trees in a row via prompt engineering. |
| [21] |
Wang D, Song H, Wang B. 2025. YO-AFD: an improved YOLOv8-based deep learning approach for rapid and accurate apple flower detection. |
| [22] |
Niu Y, Lu M, Liang X, Wu Q, Mu J. 2023. YOLO-plum: a high precision and real-time improved algorithm for plum recognition. |
| [23] |
Wu F, Yang Z, Mo X, Wu Z, Tang W, et al. 2023. Detection and counting of banana bunches by integrating deep learning and classic image-processing algorithms. |
| [24] |
Wang Z, Zhang Z, Lu Y, Luo R, Niu Y, et al. 2022. SE-COTR: a novel fruit segmentation model for green apples application in complex orchard. |
| [25] |
Murphy KM, Ludwig E, Gutierrez J, Gehan MA. 2024. Deep learning in image-based plant phenotyping. |
| [26] |
Lin Q, Ye G, Wang J, Liu H. 2022. RoboFlow: a data-centric workflow management system for developing AI-enhanced robots. Proceedings of the 5th Conference on Robot Learning. London: PMLR. pp. 164:1789−1794 https://proceedings.mlr.press/v164/lin22c.html |
| [27] |
Haralick RM, Shanmugam K, Dinstein I. 1973. Textural features for image classification. |
| [28] |
van der Walt S, Schönberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, et al. 2014. Scikit-image: image processing in Python. |
| [29] |
Khoje S. 2018. Appearance and characterization of fruit image textures for quality sorting using wavelet transform and genetic algorithms. |
| [30] |
Gould HP. 1943. The variety apple orchard at Arlington Farm. Notes on specimens, Vols. 1–4. Biodiversity Heritage Library. www.biodiversitylibrary.org/item/286167 |
| [31] |
Beach SA, Booth NO, Taylor OM. 1905. The Apples of New York. Albany, New York, USA: J. B. Lyon Company, Printers. doi: 10.5962/bhl.title.17211 |
| [32] |
Copas L. 2013. Cider Apples: The New Pomona. Exeter, UK: Short Run Press Ltd. pp. 158 |
| [33] |
Dunbar-Wallis A, Volk GM, Johnson AM, Schuenemeyer A, Bunker J, et al. 2023. What’s in a name? The importance of identity in heirloom apple tree preservation. |
| [34] |
Woodall C, Liknes GC, Bunker JP, Peace CP, Frank JM. 2023. Forgotten forest relics: Apple trees (Malus spp.) in eastern U. S. forests. |
| [35] |
Dearborn J. 2023. Unifying biodiversity knowledge to support life on a sustainable planet. The Biodiversity Heritage Library doi: |
| [36] |
Watts S, Migicovsky Z, McClure KA, Yu CHJ, Amyotte B, et al. 2021. Quantifying apple diversity: a phenomic characterization of Canada’s Apple Biodiversity Collection. |
| [37] |
Bramel P, Volk GM. 2019. A global strategy for the conservation and use of apple genetic resources. Bonn, Germany: Global Crop Diversity Trust. pp. 52 doi: 10.13140/RG.2.2.34072.34562 |