Monitoring flowering phenology of apple trees using remote sensing techniques
DOI:
https://doi.org/10.55779/ng42196Keywords:
apple tree, crop monitoring, drone, flowering phenology, satelliteAbstract
The apple orchards with large land extensions represent a challenge in monitoring crops. Remote sensing techniques can analyze and obtain information on the general state of crops using multispectral data and vegetation indexes. This study aimed to analyze the flowering phenological of three apple orchards using images from Sentinel-2 satellite and a Phantom 4 pro/pro+ drone in three apple-producing regions. The images were processed using ArcGIS software and the Normalized Difference Vegetation Index (NDVI). The results were statistically analyzed through discriminant to classify and identify significant differences between the flowering phenological stages. NDVI maps were obtained for the study areas, and the NDVI values ranged from 0.09 to 0.26 and from 0.22 to 0.35 for drone and satellite images, respectively. It was possible to differentiate between two groups of phenological stages in the apple orchards (pre-bloom and post-bloom). The information generated can be a complementary tool for monitoring the apple tree crop.
Metrics
References
Bennewitz-Alvarez E, Cazanga-Solar S, Carrasco-Benavides M (2018). Estudio de los estados fenológicos en cerezo (Prunus avium L.) utilizando observaciones de campo e índices de vegetación derivados de imágenes satelitales. Idesia (Arica) 36(1):65–72. http://dx.doi.org/10.4067/S0718-34292018000100065
Bocco M, Sayago S, Violini S, Willington EA (2015). Modelos simples para estimar rendimiento de cultivos agrícolas a partir de imágenes satelitales: una herramienta para la planificación. II Simposio Argentino sobre Tecnología y Sociedad (STS)-JAIIO 44 (Rosario, 2015), pp 26-35.
Bonnaire LB, Montoya F, Obando-Vidal (2021). Procesamiento de imágenes multiespectrales captadas con drones para evaluar el índice de vegetación de diferencia normalizada en plantaciones de café variedad Castillo. Ciencia y Tecnología Agropecuaria 22(1):e1578.
Chen Z, Su R, Wang Y, Chen G, Wang Z, Yin P, Wang J (2022). Automatic estimation of apple orchard blooming levels using the improved YOLOv5. Agronomy 12(10):2483. https://doi.org/10.3390/agronomy12102483
Chen Z, Yu L, Liu W, Zhang J, Wang N, Chen X (2021). Research progress of fruit color development in apple (Malus domestica Borkh.). Plant Physiology and Biochemistry 162:267-279. https://doi.org/10.1016/j.plaphy.2021.02.033
Cruz-Toribio JO, Gutierrez-Lazares JW (2019). Evaluación superficial de vías urbanas empleando vehículo aéreo no tripulado (VANT). Métodos y Materiales 8(1):23-32. https://doi.org/10.15517/mym.v8i1.34113
Delegido J, Tenjo C, Ruiz-Verdú A, Pereira-Sandoval M, Pasqualotto N, Gibaja G, Verrelst J, Peña R, Urrego EP, Borrás J, Sanchis-Muñoz J, Pezzola A, Mosquera Z, Quinto Z, Gómez JJ, Moreno J (2016). Aplicaciones de Sentinel-2 a estudios de vegetación y calidad de aguas continentales. XVII Simposio Internacional en Percepción Remota y Sistemas de Información Geográfica (SELPER). Puerto Iguazú, Argentina p 15.
Earth Observing System (2020). NDVI. Retrieved 2022 March 03 from https://eos.com/es/make-an-analysis/ndvi/
Eaton C, Shepherd A (2002). Agricultura por contrato, alianzas para el crecimiento. FAO, Boletín de Servicios Agrícolas de la FAO 145, 250 p.
Esri (2016). Función NDVI. Retrieved 2022 March 17 from https://desktop.arcgis.com/es/arcmap/10.3/manage-data/raster-and-images/ndvi-function.htm
Guan S, Fukami K, Matsunaka H, Okami M, Tanaka R, Nakano H, Sakai T, Nakano K, Ohdan H, Takahashi K (2019). Assessing correlation of high-resolution NDVI with fertilizer application level and yield of rice and wheat crops using small UAVs. Remote Sensing 11(2):112. https://doi.org/10.3390/rs11020112
InfoAgro (2021). Producción de manzana en México. Retrieved 2022 February 02 from https://mexico.infoagro.com/produccion-de-manzana-en-mexico/
Kharuf-Gutierrez S, Hernández-Santana L, Orozco-Morales R, Aday Díaz ODLC, Delgado Mora I (2018). Análisis de imágenes multiespectrales adquiridas con vehículos aéreos no tripulados. Ingeniería Electrónica, Automática y Comunicaciones 39(2):79-91.
Luna Toledo ES, Figuerola PI (2016). Monitoring olive phenology and phenometry through NDVI - MODIS dynamics, in Vichigasta, La Rioja. Rada 7:73-81.
Marini MF (2013). Discriminación de cultivos de distinto desarrollo utilizando imágenes satelitales MODIS. GeoFocus. Revista Internacional de Ciencia y Tecnología de la Información Geográfica 13(1):48-60.
Murray M (2020). Critical temperatures for frost damage on fruit trees. All Current Publications 644 p.
Pierattini C (2017). Evaluación de índices de vegetación para la estimación de la fenología en un huerto de manzano. Tesis de Ingeniería, Universidad Católica del Maule, Chile p 40.
Poblete-Echeverria C, Odi M, Ortega-Farías S (2013). Estimación de la evapotranspiración de un huerto de manzanos mediante el modelo de coeficiente dual FAO-56 asistido por imágenes satelitales. Anais XVI Simposio Brasileiro de Sensoriamento Remoto, 13-18 de abril de 2013, Brasil pp 135-142. http://dx.doi.org/10.13140/2.1.1431.8721
Rivero R, Sønsteby A, Heide OM, Måge F, Remberg SF (2017). Flowering phenology and the interrelations between phenological stages in apple trees (Malus domestica Borkh.) as influenced by the Nordic climate. Acta Agriculturae Scandinavica, Section B – Soil and Plant Science 67(4):292-302. https://doi.org/10.1080/09064710.2016.1267256
Rueda F, Peñaranda LA, Velásquez WL, Díaz SA (2015). Aplicación de una metodología de análisis de datos obtenidos por percepción remota orientados a la estimación de la productividad de caña para panela al cuantificar el NDVI (índice de vegetación de diferencia normalizada). Ciencia y Tecnología Agropecuaria 16(1):25-40.
Segarra J, Buchaillot ML, Araus JL, Kefauver SC (2020). Remote sensing for precision agriculture: Sentinel-2 improved features and applications. Agronomy 10(5):641. https://doi.org/10.3390/agronomy10050641
Seif El-Yazal MA, Seif El-Yazal SA, Rady MM (2014). Exogenous dormancy-breaking substances positively change endogenous phytohormones and amino acids during dormancy release in ‘Anna’ apple trees. Plant Growth Regulation 72:211-220. https://doi.org/10.1007/s10725-013-9852-1
Sevilla H, Erazo A (2019). Estudio multiespectral del cultivo de tuna para determinar los índices NDVI1, CWSI2 y SAVI3, a partir de imágenes Sentinel 2A, en el cantón Guano, provincia de Chimborazo, Ecuador. Enfoque UTE 10(3):55-56.
SIAP (2020). Panorama Agroalimentario. Retrieved 2020 January 25 from https://www.inforural.com.mx/wp-content/uploads/2020/11/Atlas-Agroalimentario-2020.pdf
Thomson SV, Gouk SC (2003). Influence of age of apple flowers on growth of Erwinia amylovora and biological control agents. Plant Disease 87(5):502-509. https://doi.org/10.1094/PDIS.2003.87.5.502
USDA (2020). Fresh apples, grapes, and pears: World markets and trade. Foreign Agricultural Service of the US Department of Agriculture. Official website. Retrieved 2024 January 05 from https://www.fas.usda.gov/data/
Wang XA, Tang J, Whitty M (2021). DeepPhenology: Estimation of apple flower phenology distributions based on deep learning. Computers and Electronics in Agriculture 185:106123. https://doi.org/10.1016/j.compag.2021.106123
Yang C (2020). Remote sensing and precision agriculture technologies for crop disease detection and management with a practical application example. Engineering 6(5):528-532. https://doi.org/10.1016/j.eng.2019.10.015
Yzarra W, López F (2011). Manual de observaciones fenológicas. SENAMHI-MINANG-DGCA, Lima, Perú p 99. Retrieved 2024 February 11 from https://www.senamhi.gob.pe/load/file/01401SENA-11.pdf
Zenteno GA, Palacios E, Tijerina L, Flores H (2017). Aplicación de tecnologías de percepción remota para la estimación del rendimiento en caña de azúcar. Revista Mexicana de Ciencias Agrícolas 8(7):1575-1586.
Zhao H, Yang C, Guo W, Zhang L, Zhang D (2020). Automatic estimation of crop disease severity levels based on vegetation index normalization. Remote Sensing 12(12):1930. https://doi.org/10.3390/rs12121930
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Johana M. CARMONA-GARCÍA, Alexandra ARREDONDO-BUSTILLOS , Nora A. SALAS-SALAZAR, Rafael A. PARRA-QUEZADA, María J. RODRÍGUEZ-ROQUE, María A. FLORES-CÓRDOVA, Damaris L. OJEDA-BARRIOS, Mayra C. SOTO-CABALLERO
This work is licensed under a Creative Commons Attribution 4.0 International License.
Distribution - Permissions - Copyright
Papers published in Nova Geodesia are Open-Access, distributed under the terms and conditions of the Creative Commons Attribution License.
© Articles by the authors; licensee SMTCT, Cluj-Napoca, Romania. The journal allows the author(s) to hold the copyright/to retain publishing rights without restriction.
License:
Open Access Journal - the journal offers free, immediate, and unrestricted access to peer-reviewed research and scholarly work, due to SMTCT support to increase the visibility, accessibility and reputation of the researchers, regardless of geography and their budgets. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author.
