{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T14:40:29Z","timestamp":1776091229770,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T00:00:00Z","timestamp":1731024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Government Department of Agriculture and Water Resources","award":["ST15002"],"award-info":[{"award-number":["ST15002"]}]},{"name":"Horticulture Innovation Australia Ltd.","award":["ST15002"],"award-info":[{"award-number":["ST15002"]}]},{"name":"Applied Agricultural Remote Sensing Centre (AARSC) of the University of New England, Australia","award":["ST15002"],"award-info":[{"award-number":["ST15002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Tree- and block-level prediction of mango yield is important for farm operations, but current manual methods are inefficient. Previous research has identified the accuracies of mango yield forecasting using very-high-resolution (VHR) satellite imagery and an \u201918-tree\u2019 stratified sampling method. However, this approach still requires infield sampling to calibrate canopy reflectance and the derived block-level algorithms are unable to translate to other orchards due to the influences of abiotic and biotic conditions. To better appreciate these influences, individual tree yields and corresponding canopy reflectance properties were collected from 2015 to 2021 for 1958 individual mango trees from 55 orchard blocks across 14 farms located in three mango growing regions of Australia. A linear regression analysis of the block-level data revealed the non-existence of a universal relationship between the 24 vegetation indices (VIs) derived from VHR satellite data and fruit count per tree, an outcome likely due to the influence of location, season, management and cultivar. The tree-level fruit count predicted using a random forest (RF) model trained on all calibration data produced a percentage root mean squared error (PRMSE) of 26.5% and a mean absolute error (MAE) of 48 fruits\/tree. The lowest PRMSEs produced from RF-based models developed from location, season and cultivar subsets at the individual tree level ranged from 19.3% to 32.6%. At the block level, the PRMSE for the combined model was 10.1% and the lowest values for the location, seasonal and cultivar subset models varied between 7.2% and 10.0% upon validation. Generally, the block-level predictions outperformed the individual tree-level models. Maps were produced to provide mango growers with a visual representation of yield variability across orchards. This enables better identification and management of the influence of abiotic and biotic constraints on production. Future research could investigate the causes of spatial yield variability in mango orchards.<\/jats:p>","DOI":"10.3390\/rs16224170","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T06:05:41Z","timestamp":1731045941000},"page":"4170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Exploring the Relationship Between Very-High-Resolution Satellite Imagery Data and Fruit Count for Predicting Mango Yield at Multiple Scales"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9017-6821","authenticated-orcid":false,"given":"Benjamin Adjah","family":"Torgbor","sequence":"first","affiliation":[{"name":"Applied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2351, Australia"},{"name":"Forestry Commission, Accra P.O. Box MB 434, Ghana"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0278-6866","authenticated-orcid":false,"given":"Priyakant","family":"Sinha","sequence":"additional","affiliation":[{"name":"Applied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2351, Australia"},{"name":"Ecosystem Management, School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6430-0588","authenticated-orcid":false,"given":"Muhammad Moshiur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Applied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2351, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5762-8980","authenticated-orcid":false,"given":"Andrew","family":"Robson","sequence":"additional","affiliation":[{"name":"Applied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2351, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0721-2458","authenticated-orcid":false,"given":"James","family":"Brinkhoff","sequence":"additional","affiliation":[{"name":"Applied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2351, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4233-2172","authenticated-orcid":false,"given":"Luz Angelica","family":"Suarez","sequence":"additional","affiliation":[{"name":"Applied Agricultural Remote Sensing Centre, University of New England, Armidale, NSW 2351, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"ref_1","unstructured":"FAOSTAT (2023, January 08). Value of Agricultural Production. Available online: https:\/\/www.fao.org\/faostat\/en\/#data\/QV."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Torgbor, B.A., Rahman, M.M., Brinkhoff, J., Sinha, P., and Robson, A. (2023). Integrating Remote Sensing and Weather Variables for Mango Yield Prediction Using a Machine Learning Approach. Remote Sens., 15.","DOI":"10.3390\/rs15123075"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Song, T. (2022). Characterization of Soil-Plant Leaf Nutrient Elements and Key Factors Affecting Mangoes in Karst Areas of Southwest China. Land, 11.","DOI":"10.3390\/land11070970"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sarron, J., Mal\u00e9zieux, \u00c9., San\u00e9, C., and Mango, \u00c9.F. (2018). Yield Mapping at the Orchard Scale Based on Tree Structure and Land Cover Assessed by UAV. Remote Sens., 10.","DOI":"10.3390\/rs10121900"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1111\/agec.12204","article-title":"Forecast performance of WASDE price projections for U.S. corn","volume":"1","author":"Hoffman","year":"2015","journal-title":"Agric. Econ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Robson, A., and Bristow, M. (2018). Exploring the Potential of High Resolution WorldView-3 Imagery for Estimating Yield of Mango. Remote Sens., 10.","DOI":"10.3390\/rs10121866"},{"key":"ref_7","first-page":"78","article-title":"Applications of precision agriculture in horticultural crops","volume":"1","author":"Fountas","year":"2016","journal-title":"Eur. J. Hortic. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Jin, Y., Chen, B., and Brown, P. (2019). California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach. Front. Plant Sci., 1.","DOI":"10.3389\/fpls.2019.00809"},{"key":"ref_9","first-page":"1","article-title":"Light interception, leaf nitrogen and yield prediction in almonds: A case study","volume":"1","author":"Muhammad","year":"2015","journal-title":"Eur. J. Agron."},{"key":"ref_10","first-page":"823","article-title":"Estimation of fruit load in mango orchards: Tree sampling considerations and use of machine vision and satellite imagery","volume":"1","author":"Anderson","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106812","DOI":"10.1016\/j.compag.2022.106812","article-title":"Fruit yield prediction and estimation in orchards: A state-of-the-art comprehensive review for both direct and indirect methods","volume":"195","author":"He","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","first-page":"387","article-title":"Deep neural networks for grape bunch segmentation in natural images from a consumer-grade camera","volume":"1","author":"Marani","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Robson, A.J., Rahman, M.M., and Muir, J. (2017). Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia. Remote Sens., 9.","DOI":"10.3390\/rs9121223"},{"key":"ref_14","first-page":"126","article-title":"Ground based hyperspectral imaging for extensive mango yield estimation","volume":"1","author":"Wendel","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","unstructured":"Sinha, P., and Robson, A.J. (2022, August 12). Satellites Used to Predict Commercial Mango Yields. McPherson Media Group (MMG). Available online: https:\/\/www.treecrop.com.au\/news\/satellites-used-predict-commercial-mango-yields\/."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Torgbor, B.A., Rahman, M.M., Robson, A., Brinkhoff, J., and Khan, A. (2022). Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana. Horticulturae, 1.","DOI":"10.3390\/horticulturae8010011"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Stein, M., Bargoti, S., and Underwood, J. (2016). Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry. Sensors, 16.","DOI":"10.3390\/s16111915"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Apolo-Apolo, O.E., Perez-Ruiz, M., Martinez-Guanter, J., and Valente, J. (2020). A Cloud-Based Environment for Generating Yield Estimation Maps From Apple Orchards Using UAV Imagery and a Deep Learning Technique. Front. Plant Sci., 1.","DOI":"10.3389\/fpls.2020.01086"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Matese, A., and Di Gennaro, S.F. (2021). Beyond the traditional NDVI index as a key factor to mainstream the use of UAV in precision viticulture. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-81652-3"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.biosystemseng.2020.08.015","article-title":"Active thermal imaging for immature citrus fruit detection","volume":"1","author":"Gan","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bai, X. (2021). Comparison of Machine-Learning and CASA Models for Predicting Apple Fruit Yields from Time-Series Planet Imageries. Remote Sens., 13.","DOI":"10.3390\/rs13163073"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jeong, J.H. (2016). Random Forests for Global and Regional Crop Yield Predictions. PLoS ONE, 1.","DOI":"10.1371\/journal.pone.0156571"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.agwat.2012.07.003","article-title":"Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes","volume":"1","author":"Fukuda","year":"2013","journal-title":"Agric. Water Manag."},{"key":"ref_24","unstructured":"NTG (2023, January 08). Soils of the Northern Territory\u2014Factsheet. Department of Land Resource Management, Available online: https:\/\/depws.nt.gov.au\/rangelands\/technical-notes-and-fact-sheets\/land-soil-vegetation-technical-information."},{"key":"ref_25","unstructured":"BOM (2023, July 05). Regional Weather and Climate Guide. In the last 30 years in South East Queensland. Bureau of Meteorology, Available online: www.bom.gov.au\/climate\/climate-guides\/guides\/044-South-East-QLD-Climate-Guide.pdf."},{"key":"ref_26","unstructured":"ESRI (2019). ArcGIS Version 10.8 for Desktop, Environmental Systems Research Institute Inc."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jawak, S.D., Luis, A.J., Fretwell, P.T., Convey, P., and Durairajan, U.A. (2019). Semiautomated Detection and Mapping of Vegetation Distribution in the Antarctic Environment Using Spatial-Spectral Characteristics of WorldView-2 Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11161909"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/1011-1344(93)06963-4","article-title":"Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves","volume":"1","author":"Gitelson","year":"1994","journal-title":"J. Photochem. Photobiol. B Biol."},{"key":"ref_29","unstructured":"Barnes, E. (2000, January 16\u201319). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","article-title":"Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture","volume":"1","author":"Haboudane","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_31","first-page":"221","article-title":"Semi-empirical indices to assess carotenoids\/chlorophyll a ratio from leaf spectral reflectance","volume":"1","author":"Penuelas","year":"1995","journal-title":"Photosynthetica"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1017\/S2040470017000954","article-title":"Evaluating satellite remote sensing as a method for measuring yield variability in Avocado and Macadamia tree crops","volume":"1","author":"Robson","year":"2017","journal-title":"Adv. Anim. Biosci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"1","author":"Gitelson","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_34","unstructured":"Rouse, J., Haas, R., Schell, J., and Deering, D. (1974). Monitoring Vegetation Systems in the Great Plains with Erts, NASA Special Publication."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1078\/0176-1617-01176","article-title":"Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation","volume":"1","author":"Gitelson","year":"2004","journal-title":"J. Plant Physiol."},{"key":"ref_36","unstructured":"Bannari, A., Asalhi, H., and Teillet, P.M. (2002, January 24\u201328). Transformed difference vegetation index (TDVI) for vegetation cover mapping. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada."},{"key":"ref_37","first-page":"1355","article-title":"Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie","volume":"VIII","author":"Pearson","year":"1972","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of leaf-area index from quality of light on the forest floor","volume":"1","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"1","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"1","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"1","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building Predictive Models in R Using the caret Package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"02001","DOI":"10.1051\/e3sconf\/202126602001","article-title":"A brief comparative study of the potentialities and limitations of machine-learning algorithms and statistical techniques","volume":"266","author":"Litvinenko","year":"2021","journal-title":"E3S Web Conf."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"107907","DOI":"10.1016\/j.agwat.2022.107907","article-title":"Rice ponding date detection in Australia using Sentinel-2 and Planet Fusion imagery","volume":"273","author":"Brinkhoff","year":"2022","journal-title":"Agric. Water Manag."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Dong, X., Zc, Z., Yu, R., Tian, Q., and Zhu, X. (2020). Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard. Remote Sens., 1.","DOI":"10.3390\/rs12010133"},{"key":"ref_46","unstructured":"Kleissl, J. (2013). Chapter 2\u2014Semi-Empirical Satellite Models. Solar Energy Forecasting and Resource Assessment, Academic Press."},{"key":"ref_47","first-page":"316","article-title":"How to evaluate models: Observed vs. predicted or predicted vs. observed?","volume":"1","author":"Perelman","year":"2008","journal-title":"Ecol. Model."},{"key":"ref_48","first-page":"103434","article-title":"Early-Season forecasting of citrus block-yield using time series remote sensing and machine learning: A case study in Australian orchards","volume":"122","author":"Suarez","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhu, Y. (2022). Rapid Target Detection of Fruit Trees Using UAV Imaging and Improved Light YOLOv4 Algorithm. Remote Sens., 14.","DOI":"10.3390\/rs14174324"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"103457","DOI":"10.1016\/j.jag.2023.103457","article-title":"Object-Detection from Multi-View remote sensing Images: A case study of fruit and flower detection and counting on a central Florida strawberry farm","volume":"123","author":"Zheng","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinformation."},{"key":"ref_51","unstructured":"Donovan, J. (2023, March 29). Australian Mango Varieties. Available online: https:\/\/lawn.com.au\/australian-mango-varieties\/."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.compag.2012.11.009","article-title":"Estimation of mango crop yield using image analysis\u2014Segmentation method","volume":"1","author":"Payne","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_53","unstructured":"Smith, J., and Smith, P. (2007). Environmental Modelling: An Introduction, Oxford University Press."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Anderson, N.T., Walsh, K.B., and Wulfsohn, D. (2021). Technologies for Forecasting Tree Fruit Load and Harvest Timing\u2014From Ground, Sky and Time. Agronomy, 11.","DOI":"10.3390\/agronomy11071409"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"108369","DOI":"10.1016\/j.agrformet.2021.108369","article-title":"Block-level macadamia yield forecasting using spatio-temporal datasets","volume":"303","author":"Brinkhoff","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"102894","DOI":"10.1016\/j.agsy.2020.102894","article-title":"Mid-season empirical cotton yield forecasts at fine resolutions using large yield mapping datasets and diverse spatial covariates","volume":"184","author":"Filippi","year":"2020","journal-title":"Agric. Syst."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"112174","DOI":"10.1016\/j.rse.2020.112174","article-title":"A million kernels of truth: Insights into scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt","volume":"1","author":"Deines","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_58","first-page":"274","article-title":"Diversity and nature of damage of mango insect-pests in south Gujarat ecosystem","volume":"1","author":"Bana","year":"2018","journal-title":"J. Entomol. Zool. Stud."},{"key":"ref_59","first-page":"13","article-title":"Appraisal of soil fertility, leaf nutrient concentration and yield of mango (Mangifera indica L.) at Malihabad region, Uttar Pradesh","volume":"1","author":"Kumar","year":"2012","journal-title":"Curr. Adv. Agric. Sci. (Int. J.)"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"105709","DOI":"10.1016\/j.compag.2020.105709","article-title":"Crop yield prediction using machine learning: A systematic literature review","volume":"177","author":"Kassahun","year":"2020","journal-title":"Comput. Electron. Agric."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4170\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:28:43Z","timestamp":1760113723000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/22\/4170"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,8]]},"references-count":60,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2024,11]]}},"alternative-id":["rs16224170"],"URL":"https:\/\/doi.org\/10.3390\/rs16224170","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,8]]}}}