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      Propuesta para el monitoreo del cultivo de maíz basado en sensores remotos Translated title: Proposal for monitoring maize cultivation based on remóte sensors

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          Abstract

          Resumen El monitoreo de cultivos basado en sensores remotos, particularmente a través de vehículos aéreos no tripulados (VANTs), permite a los agricultores estar actualizados sobre la salud de su cultivo y localizar qué áreas de la parcela requieren atención para mejorar su rendimiento. Por tal motivo, la presente propuesta metodológica planteada a partir de los resultados del estudio piloto para identificar las zonas donde existe una deficiencia de nutrientes o agua a través de imágenes obtenidas de un VANT, integrando información de las prácticas agronómicas expresadas por los agricultores y los hallazgos observados en campo. Se emplearon índices de vegetación para determinar la salud del cultivo y la etapa fenológica en la que se encuentra. Asimismo, se incentiva la participación de los pequeños agricultores con la finalidad de sensibilizarlos de la información que se puede obtener a través de este tipo de metodologías de la agricultura de precisión. Finalmente, los estudios previos en la zona de estudio permitieron establecer una guía para el monitoreo de la salud del cultivo de maíz a través de sensores remotos, particularmente de un VANT.

          Translated abstract

          Abstract Remote sensing-based crop monitoring, particularly through unmanned aerial vehicles (UAVs), allows farmers to stay up to date on the health of their crop and locate which areas of their plot require attention, thus implementing preventive and corrective measures to anticipate problems at an early stage and enhance crop yield. For this reason, the methodology proposes to identify areas where there is a nutrient or water deficiency through images obtained from an unmanned aerial vehicle, integrating information on the expressed agronomic practices by the farmers and the findings from the field. Vegetation indices were used to determine crop health. Participation of small farmers is encouraged to sensitize producers about the usefulness of the information that can be gathered through this type of precision agriculture methodologies. Finally, previous studies allowed establishing a guide for monitoring the maize health through remote sensors, particularly a UAV.

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          Most cited references42

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          Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service

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            Drones in agriculture: A review and bibliometric analysis

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              Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images

              Crop yield is related to national food security and economic performance, and it is therefore important to estimate this parameter quickly and accurately. In this work, we estimate the yield of winter wheat using the spectral indices (SIs), ground-measured plant height (H), and the plant height extracted from UAV-based hyperspectral images (HCSM) using three regression techniques, namely partial least squares regression (PLSR), an artificial neural network (ANN), and Random Forest (RF). The SIs, H, and HCSM were used as input values, and then the PLSR, ANN, and RF were trained using regression techniques. The three different regression techniques were used for modeling and verification to test the stability of the yield estimation. The results showed that: (1) HCSM is strongly correlated with H (R2 = 0.97); (2) of the regression techniques, the best yield prediction was obtained using PLSR, followed closely by ANN, while RF had the worst prediction performance; and (3) the best prediction results were obtained using PLSR and training using a combination of the SIs and HCSM as inputs (R2 = 0.77, RMSE = 648.90 kg/ha, NRMSE = 10.63%). Therefore, it can be concluded that PLSR allows the accurate estimation of crop yield from hyperspectral remote sensing data, and the combination of the SIs and HCSM allows the most accurate yield estimation. The results of this study indicate that the crop plant height extracted from UAV-based hyperspectral measurements can improve yield estimation, and that the comparative analysis of PLSR, ANN, and RF regression techniques can provide a reference for agricultural management.
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                Author and article information

                Journal
                era
                Ecosistemas y recursos agropecuarios
                Ecosistemas y recur. agropecuarios
                Universidad Juárez Autónoma de Tabasco, Dirección de Investigación y Posgrado (Villahermosa, Tabasco, Mexico )
                2007-9028
                2007-901X
                December 2023
                : 10
                : 3
                : e3810
                Affiliations
                [3] Atlacomulco Estado de México orgnameTienda El Inge San José del Rincón y Agroquímicos Siya Ajumu México
                [2] Ciudad Universitaria orgnameUniversidad Autónoma del Estado de México orgdiv1Facultad de Geografía orgdiv2Laboratorio de Observación de la Tierra Mexico
                [1] Ciudad Universitaria orgnameUniversidad Autónoma del Estado de México orgdiv1Facultad de Geografía orgdiv2Geotecnologías, ambiente y sociedades resilientes Mexico
                Article
                S2007-90282023000300022 S2007-9028(23)01000300022
                10.19136/era.a10n3.3810
                3717569f-c063-493a-80f9-5d9c26b5e39f

                This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

                History
                : 15 November 2023
                : 01 June 2023
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 43, Pages: 0
                Product

                SciELO Mexico

                Categories
                Notas científicas

                vehículo aéreo no tripulado,percepción remota,unmanned aerial vehicles,vegetation indices,healthy crop,Agricultura de precisión,remote sensing,Precision agriculture,salud del cultivo,índices de vegetación

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