{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T12:44:55Z","timestamp":1763642695336,"version":"3.45.0"},"reference-count":79,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\/MCTES","award":["UID\/00690\/2025","UID\/PRR\/00690\/2025"],"award-info":[{"award-number":["UID\/00690\/2025","UID\/PRR\/00690\/2025"]}]},{"name":"SusTEC","award":["LA\/P\/0007\/2020"],"award-info":[{"award-number":["LA\/P\/0007\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AgriEngineering"],"abstract":"<jats:p>Given the current climate change scenario, it is essential to find strategies to reduce environmental risks and obtain economically sustainable agricultural productions. This study investigated the impact of various agronomic treatments on an almond orchard in northeastern Portugal, focusing on their relationships with crop growth\/vigour and yield. The experiment was conducted using a factorial design that combined three variables: almond cultivar (Constant\u00ed and Vairo), irrigation regime (full and regulated deficit irrigation), and kaolin application (with or without application). These combinations resulted in eight distinct treatments, each replicated across two experimental plots. To monitor the crop physiological status, two drone flights equipped with a multispectral camera were flown during the kernel-filling stage (3 and 30 August 2021). Vegetation indices (VI) derived from the multispectral images were used to assess the crop vigour. In relation to the production data, including kernel and in-shell almond weights, these were collected in 14 representative trees of each treatment. Lastly, parametric and nonparametric regression analyses were performed to better understand relationships between VI and crop yields and derive predictive models. The main results can be summarised as follows: (a) cv. Vairo was more vulnerable to the regulated deficit irrigation strategy with striking repercussions on almond production, translating into an average reduction per tree of 22% and 16% in almond kernel and in-shell almonds compared to full irrigation, respectively; (b) kaolin application did not reflect statistically significant differences in the mean crop yield, as Tukey\u2019s pairwise comparisons involving kaolin as a differentiating factor (e.g., C100+k\u2014C100, V100+K\u2014V100) showed confidence intervals with central value close to zero; and (c) regression analysis using the nonparametric random forest model and individualised treatments demonstrated a better agreement with the observed data (R2 &gt; 0.7). This research provided valuable insights into how cultivar selection, irrigation strategy, and kaolin application can influence the almond crop performance. When integrating multispectral aerial monitoring and advanced statistical modelling, it enables an effective assessment of both crop vigour and expected yield, supporting the development of more informed and adaptive management practices to face emerging environmental challenges.<\/jats:p>","DOI":"10.3390\/agriengineering7110395","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T12:02:42Z","timestamp":1763640162000},"page":"395","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing the Impact of Deficit Irrigation and Kaolin Application on Almond Orchards: Statistical Relationships with Crop Yields and Spectral Vegetation Indices"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9424-174X","authenticated-orcid":false,"given":"Carlos","family":"Silveira","sequence":"first","affiliation":[{"name":"CIMO, LA SusTEC, Instituto Polit\u00e9cnico de Bragan\u00e7a, Campus de Santa Apol\u00f3nia, 5300-253 Bragan\u00e7a, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8425-0167","authenticated-orcid":false,"given":"David","family":"Barreales","sequence":"additional","affiliation":[{"name":"CIMO, LA SusTEC, Instituto Polit\u00e9cnico de Bragan\u00e7a, Campus de Santa Apol\u00f3nia, 5300-253 Bragan\u00e7a, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0647-8892","authenticated-orcid":false,"given":"Jo\u00e3o P.","family":"Castro","sequence":"additional","affiliation":[{"name":"CIMO, LA SusTEC, Instituto Polit\u00e9cnico de Bragan\u00e7a, Campus de Santa Apol\u00f3nia, 5300-253 Bragan\u00e7a, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0788-4710","authenticated-orcid":false,"given":"Fabiani","family":"Miranda","sequence":"additional","affiliation":[{"name":"Universidade Tecnol\u00f3gica Federal do Paran\u00e1, Campus Dois Vizinhos, Dois Vizinhos 85660-000, Paran\u00e1, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8280-9027","authenticated-orcid":false,"given":"Ant\u00f3nio C.","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"CIMO, LA SusTEC, Instituto Polit\u00e9cnico de Bragan\u00e7a, Campus de Santa Apol\u00f3nia, 5300-253 Bragan\u00e7a, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1023\/A:1008690409554","article-title":"On the origin of almond","volume":"46","author":"Ladizinsky","year":"1999","journal-title":"Genet. Resour. Crop Evol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1111\/mec.12129","article-title":"Evolutionary history of almond tree domestication in the Mediterranean basin","volume":"22","author":"Delplancke","year":"2013","journal-title":"Mol. Ecol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108558","DOI":"10.1016\/j.scienta.2019.108558","article-title":"Horticultural performance of \u2018Marinada\u2019 and \u2018Vairo\u2019 almond cultivars grown on a genetically diverse set of rootstocks","volume":"256","author":"Lordan","year":"2019","journal-title":"Sci. Hortic."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Guti\u00e9rrez-Gordillo, S., Dur\u00e1n Zuazo, V.H., Hern\u00e1ndez-Santana, V., Gil, F.F., Escalera, A.G., Amores-Ag\u00fcera, J.J., and Garc\u00eda-Tejero, I.F. (2020). Cultivar Dependent Impact on Yield and Its Components of Young Almond Trees under Sustained-Deficit Irrigation in Semi-Arid Environments. Agronomy, 10.","DOI":"10.3390\/agronomy10050733"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fernandes de Oliveira, A., Mameli, M.G., De Pau, L., and Satta, D. (2023). Almond Tree Adaptation to Water Stress: Differences in Physiological Performance and Yield Responses among Four Cultivar Grown in Mediterranean Environment. Plants, 12.","DOI":"10.3390\/plants12051131"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Freitas, T.R., Santos, J.A., Silva, A.P., and Fraga, H. (2023). Reviewing the Adverse Climate Change Impacts and Adaptation Measures on Almond Trees (Prunus dulcis). Agriculture, 13.","DOI":"10.3390\/agriculture13071423"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1080\/03650340.2018.1492113","article-title":"Fostering sustainable water use in almond (Prunus dulcis Mill.) orchards in a semiarid Mediterranean environment","volume":"65","author":"Souza","year":"2019","journal-title":"Arch. Agron. Soil Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lipan, L., Cano-Lamadrid, M., Hern\u00e1ndez, F., Sendra, E., Corell, M., V\u00e1zquez-Ara\u00fajo, L., Moriana, A., and Carbonell-Barrachina, \u00c1.A. (2020). Long-Term Correlation between Water Deficit and Quality Markers in HydroSOStainable Almonds. Agronomy, 10.","DOI":"10.3390\/agronomy10101470"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"251","DOI":"10.17660\/ActaHortic.2017.1150.35","article-title":"Deficit irrigation of almond trees did not decrease Yield","volume":"1150","author":"Monksa","year":"2017","journal-title":"Acta Hortic."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.agwat.2017.10.001","article-title":"Water use of irrigated almond trees when subjected to water deficits","volume":"195","author":"Espadador","year":"2018","journal-title":"Agric. Water Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"90","DOI":"10.3733\/ca.v065n02p90","article-title":"Regulated deficit irrigation reduces water use of almonds without affecting yield","volume":"65","author":"Stewart","year":"2011","journal-title":"Calif. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1007\/s00271-012-0369-6","article-title":"Financial feasibility of implementing regulated and sustained deficit irrigation in almond orchards","volume":"31","author":"Alcon","year":"2013","journal-title":"Irrig. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.scienta.2011.04.007","article-title":"The response of different almond genotypes to moderate and severe water stress in order to screen for drought tolerance","volume":"129","author":"Yadollahi","year":"2011","journal-title":"Sci. Hortic."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108208","DOI":"10.1016\/j.agwat.2023.108208","article-title":"Quantitative analysis of almond yield response to irrigation regimes in Mediterranean Spain","volume":"279","author":"Intrigliolo","year":"2023","journal-title":"Agric. Water Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108990","DOI":"10.1016\/j.scienta.2019.108990","article-title":"Physiological and biochemical performance of almond trees under deficit irrigation","volume":"261","author":"Prgomet","year":"2020","journal-title":"Sci. Hortic."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"108562","DOI":"10.1016\/j.agwat.2023.108562","article-title":"Deficit irrigation limits almond trees\u2019 photosynthetic productivity and compromises yields","volume":"289","author":"Sperling","year":"2023","journal-title":"Agric. Water Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.2134\/agronj2018.03.0183","article-title":"Yield and Water Use in Almond under Deficit Irrigation","volume":"111","author":"Collin","year":"2019","journal-title":"Agron. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.scienta.2019.02.070","article-title":"Kaolin, an emerging tool to alleviate the effects of abiotic stresses on crop performance","volume":"250","author":"Brito","year":"2019","journal-title":"Sci. Hortic."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1007\/s10340-015-0659-x","article-title":"Kaolin and potassium soap with thyme essential oil to control Monosteira unicostata and other phytophagous arthropods of almond trees in organic orchards","volume":"88","author":"Marcotegui","year":"2015","journal-title":"J. Pest Sci."},{"key":"ref_20","first-page":"2","article-title":"Compatibility of organic farming treatments against Monosteira unicostata with non-target arthropod fauna of almond trees canopy","volume":"15","author":"Marcotegui","year":"2017","journal-title":"Span. J. Agric. Res."},{"key":"ref_21","first-page":"59","article-title":"Review of Crop Yield Estimation Using Machine Learning and Deep Learning Techniques","volume":"23","author":"Modi","year":"2022","journal-title":"Scalable Comput."},{"key":"ref_22","first-page":"101418","article-title":"Advancements in UAV remote sensing for agricultural yield estimation: A systematic comprehensive review of platforms, sensors, and data analytics","volume":"37","author":"Gade","year":"2025","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Saravia, D., Valqui-Valqui, L., Salazar, W., Quille-Mamani, J., Barboza, E., Porras-Jorge, R., Injante, P., and Arbizu, C.I. (2023). Yield Prediction of Four Bean (Phaseolus vulgaris) Cultivars Using Vegetation Indices Based on Multispectral Images from UAV in an Arid Zone of Peru. Drones, 7.","DOI":"10.3390\/drones7050325"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107581","DOI":"10.1016\/j.compag.2022.107581","article-title":"UAS-based imaging for prediction of chickpea crop biophysical parameters and yield","volume":"205","author":"Avneri","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Saravia, D., Salazar, W., Valqui-Valqui, L., Quille-Mamani, J., Porras-Jorge, R., Corredor, F.A., Barboza, E., V\u00e1squez, H.V., Casas Diaz, A.V., and Arbizu, C.I. (2022). Yield Predictions of Four Hybrids of Maize (Zea mays) Using Multispectral Images Obtained from UAV in the Coast of Peru. Agronomy, 12.","DOI":"10.20944\/preprints202205.0231.v1"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Silveira, C., Almeida, A., and Ribeiro, A.C. (2022). Technological Innovation in the Traditional Olive Orchard Management: Advances and Opportunities to the Northeastern Region of Portugal. Water, 14.","DOI":"10.3390\/w14244081"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"673","DOI":"10.3390\/rs2030673","article-title":"Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques","volume":"2","author":"Panda","year":"2010","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105791","DOI":"10.1016\/j.compag.2020.105791","article-title":"A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices","volume":"178","author":"Ramos","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Killeen, P., Kiringa, I., Yeap, T., and Branco, P. (2024). Corn Grain Yield Prediction Using UAV-Based High Spatiotemporal Resolution Imagery, Machine Learning, and Spatial Cross-Validation. Remote Sens., 16.","DOI":"10.3390\/rs16040683"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Han, X., Wei, Z., Chen, H., Zhang, B., Li, Y., and Du, T. (2021). Inversion of Winter Wheat Growth Parameters and Yield Under Different Water Treatments Based on UAV Multispectral Remote Sensing. Front. Plant Sci., 12.","DOI":"10.3389\/fpls.2021.609876"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2017.05.003","article-title":"Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery","volume":"130","author":"Zhou","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"240","DOI":"10.3390\/agriengineering6010015","article-title":"Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction","volume":"6","author":"Fraga","year":"2024","journal-title":"AgriEngineering"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guti\u00e9rrez-Gordillo, S., de la Gala Gonz\u00e1lez-Santiago, J., Trigo-C\u00f3rdoba, E., Rubio-Casal, A.E., Garc\u00eda-Tejero, I.F., and Egea, G. (2021). Monitoring of Emerging Water Stress Situations by Thermal and Vegetation Indices in Different Almond Cultivars. Agronomy, 11.","DOI":"10.3390\/agronomy11071419"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42853-023-00209-6","article-title":"Ensemble of Machine Learning Algorithms for Rice Grain Yield Prediction Using UAV-Based Remote Sensing","volume":"49","author":"Sarkar","year":"2024","journal-title":"J. Biosyst. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Feng, L., Zhang, Z., Ma, Y., Du, Q., Williams, P., Drewry, J., and Luck, B. (2020). Alfalfa yield prediction using UAV-based hyperspectral imagery and ensemble learning. Remote Sens., 12.","DOI":"10.3390\/rs12122028"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fu, Z., Jiang, J., Gao, Y., Krienke, B., Wang, M., Zhong, K., Cao, Q., Tian, Y., Zhu, Y., and Cao, W. (2020). Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle. Remote Sens., 12.","DOI":"10.3390\/rs12030508"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Canata, T.F., Wei, M.C.F., Maldaner, L.F., and Molin, J.P. (2021). Sugarcane yield mapping using high-resolution imagery data and machine learning technique. Remote Sens., 13.","DOI":"10.3390\/rs13020232"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"888","DOI":"10.3390\/agriengineering4040057","article-title":"Coffee-Yield Estimation Using High-Resolution Time-Series Satellite Images and Machine Learning","volume":"4","author":"Martello","year":"2022","journal-title":"AgriEngineering"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Silveira, C., Almeida, A., and Ribeiro, A.C. (2023). How Can a Changing Climate Influence the Productivity of Traditional Olive Orchards? Regression Analysis Applied to a Local Case Study in Portugal. Climate, 11.","DOI":"10.3390\/cli11060123"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"127193","DOI":"10.1016\/j.eja.2024.127193","article-title":"Evaluating machine learning models and identifying key factors influencing spatial maize yield predictions in data intensive farm management","volume":"157","author":"Maseko","year":"2024","journal-title":"Eur. J. Agron."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Baio, F.H.R., Santana, D.C., Teodoro, L.P.R., de Oliveira, I.C., Gava, R., de Oliveira, J.L.G., da Silva Junior, C.A., Teodoro, P.E., and Shiratsuchi, L.S. (2023). Maize Yield Prediction with Machine Learning, Spectral Variables and Irrigation Management. Remote Sens., 15.","DOI":"10.3390\/rs15010079"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kpienbaareh, D., Mohammed, K., Luginaah, I., Wang, J., Bezner Kerr, R., Lupafya, E., and Dakishoni, L. (2022). Estimating Groundnut Yield in Smallholder Agriculture Systems Using PlanetScope Data. Land, 11.","DOI":"10.3390\/land11101752"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3998","DOI":"10.1016\/j.asr.2023.07.006","article-title":"Prediction of crop yield using satellite vegetation indices combined with machine learning approaches","volume":"72","author":"Jhajharia","year":"2023","journal-title":"Adv. Space Res."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Shammi, S.A., Huang, Y., Feng, G., Tewolde, H., Zhang, X., Jenkins, J., and Shankle, M. (2024). Application of UAV Multispectral Imaging to Monitor Soybean Growth with Yield Prediction through Machine Learning. Agronomy, 14.","DOI":"10.3390\/agronomy14040672"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"111599","DOI":"10.1016\/j.rse.2019.111599","article-title":"Soybean yield prediction from UAV using multimodal data fusion and deep learning","volume":"237","author":"Maimaitijiang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"109019","DOI":"10.1016\/j.compag.2024.109019","article-title":"Using spectral vegetation indices and machine learning models for predicting the yield of sugar beet (Beta vulgaris L.) under different irrigation treatments","volume":"221","author":"Ropelewska","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Elsayed, S., El-Hendawy, S., Dewir, Y.H., Schmidhalter, U., Ibrahim, H.H., Ibrahim, M.M., Elsherbiny, O., and Farouk, M. (2021). Estimating the leaf water status and grain yield of wheat under different irrigation regimes using optimized two-and three-band hyperspectral indices and multivariate regression models. Water, 13.","DOI":"10.3390\/w13192666"},{"key":"ref_48","first-page":"1","article-title":"Monitoring growth and predicting crop yield through UAV-mounted spectral camera analysis of the interplay between soil compaction and vegetation index","volume":"36","year":"2024","journal-title":"Emir. J. Food Agric."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112295","DOI":"10.1016\/j.ecolind.2024.112295","article-title":"Predicting grain yield of maize using a new multispectral-based canopy volumetric vegetation index","volume":"166","author":"Guo","year":"2024","journal-title":"Ecol. Indic."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Peralta, N.R., Assefa, Y., Du, J., Barden, C.J., and Ciampitti, I.A. (2016). Mid-season high-resolution satellite imagery for forecasting site-specific corn yield. Remote Sens., 8.","DOI":"10.3390\/rs8100848"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Du, M., and Noguchi, N. (2017). Monitoring of wheat growth status and mapping of wheat yield\u2019s within-field spatial variations using color images acquired from UAV-camera System. Remote Sens., 9.","DOI":"10.3390\/rs9030289"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Pang, A., Chang, M.W.L., and Chen, Y. (2022). Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia. Sensors, 22.","DOI":"10.3390\/s22030717"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"348","DOI":"10.54386\/jam.v24i4.1770","article-title":"Cotton yield prediction using drone derived LAI and chlorophyll content","volume":"24","author":"Shanmugapriya","year":"2022","journal-title":"J. Agrometeorol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.5194\/hess-11-1633-2007","article-title":"Updated world map of the K\u00f6ppen-Geiger climate classification","volume":"11","author":"Peel","year":"2007","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_55","unstructured":"(1991). Coba Agroconsultores Soil Map, Current Land Use Map and Land Suitability Map of the Northeast of Portugal."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"112262","DOI":"10.1016\/j.scienta.2023.112262","article-title":"Influence of sustained deficit irrigation and foliar kaolin application on almond kernel composition","volume":"321","author":"Barreales","year":"2023","journal-title":"Sci. Hortic."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"7227","DOI":"10.1002\/jsfa.12807","article-title":"Effects of regulated deficit irrigation and foliar kaolin application on quality parameters of almond [Prunus dulcis (Mill.) D.A. Webb]","volume":"103","author":"Barreales","year":"2023","journal-title":"J. Sci. Food Agric."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Barreales, D., Capit\u00e3o, S., Bento, A.A., Casquero, P.A., and Ribeiro, A.C. (2023). Adapting Almond Production to Climate Change through Deficit Irrigation and Foliar Kaolin Application in a Mediterranean Climate. Atmosphere, 14.","DOI":"10.3390\/atmos14101593"},{"key":"ref_59","unstructured":"Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. (1998). Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56, FAO."},{"key":"ref_60","unstructured":"(2025, January 30). DJI Support for Matrice 300 RTK. Available online: https:\/\/www.dji.com\/pt\/matrice-300."},{"key":"ref_61","unstructured":"(2025, January 30). MicaSense RedEdge-MX Integration Guide. Available online: https:\/\/support.micasense.com\/hc\/en-us\/articles\/360011389334-RedEdge-MX-Integration-Guide."},{"key":"ref_62","unstructured":"(2025, January 30). Agisoft Agisoft Metashape: Professional Edition. Available online: https:\/\/www.agisoft.com\/features\/professional-edition\/."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/07038992.1996.10855178","article-title":"Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications","volume":"22","author":"Chen","year":"1996","journal-title":"Can. J. Remote Sens."},{"key":"ref_64","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":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/S0273-1177(97)01133-2","article-title":"Remote sensing of chlorophyll concentration in higher plant leaves","volume":"22","author":"Gitelson","year":"1998","journal-title":"Adv. Space Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1080\/22797254.2019.1572459","article-title":"Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images","volume":"52","author":"Jorge","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S0034-4257(97)00114-4","article-title":"The Sensitivity of the OSAVI Vegetation Index to Observational Parameters","volume":"63","author":"Steven","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2537","DOI":"10.1080\/01431160110107806","article-title":"Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction","volume":"23","author":"Gitelson","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1007\/s00484-013-0713-4","article-title":"Ground and remote sensing-based measurements of leaf area index in a transitional forest and seasonal flooded forest in Brazil","volume":"58","author":"Biudes","year":"2014","journal-title":"Int. J. Biometeorol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_72","unstructured":"Lane, D.M. (2025, March 10). Introduction to Analysis of Variance. Available online: https:\/\/onlinestatbook.com\/2\/analysis_of_variance\/intro.html."},{"key":"ref_73","unstructured":"Scikit-Learn (2025, March 10). Recursive Feature Elimination with Cross-Validation to Select Features. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.feature_selection.RFECV.html."},{"key":"ref_74","unstructured":"Scikit-Learn (2025, March 10). Ordinary Least Squares Linear Regression. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.LinearRegression.html."},{"key":"ref_75","unstructured":"Scikit-Learn (2025, March 10). A Random Forest Regressor. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestRegressor.html."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1093\/aob\/mcl100","article-title":"Physiological Effects of Kaolin Applications in Well-irrigated and Water-stressed Walnut and Almond Trees","volume":"98","author":"Rosati","year":"2006","journal-title":"Ann. Bot."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1007\/s10341-022-00732-4","article-title":"Kaolin Spray Improves Growth, Physiological Functions, Yield, and Nut Quality of \u2018Tardy Nonpareil\u2019 Almond Under Deficit Irrigation Regimens","volume":"65","author":"Gharaghani","year":"2022","journal-title":"Erwerbs-Obstbau"},{"key":"ref_78","first-page":"1585","article-title":"Effect of Kaolin Application on Growth, Water Use Efficiency, and Leaf Epidermis Characteristics of Physalis peruviana L. Seedlings under Two Irrigation Regimes","volume":"17","year":"2015","journal-title":"J. Agric. Sci. Technol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"109795","DOI":"10.1016\/j.scienta.2020.109795","article-title":"Kaolin foliar spray improves olive tree performance and yield under sustained deficit irrigation","volume":"277","author":"Brito","year":"2021","journal-title":"Sci. Hortic."}],"container-title":["AgriEngineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2624-7402\/7\/11\/395\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T12:32:20Z","timestamp":1763641940000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2624-7402\/7\/11\/395"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,20]]},"references-count":79,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["agriengineering7110395"],"URL":"https:\/\/doi.org\/10.3390\/agriengineering7110395","relation":{},"ISSN":["2624-7402"],"issn-type":[{"value":"2624-7402","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,20]]}}}