Análisis del estrés hídrico en frijol utilizando imágenes multiespectrales y sensores de humedad del suelo

Autores/as

  • Nestor Magdaleno Montoya Pineda Universidad Autónoma Agraria Antonio Narro image/svg+xml
    • Martín Cadena Zapata Universidad Autónoma Agraria Antonio Narro image/svg+xml
      • Alejandro Zermeño Gonzáles Universidad Autónoma Agraria Antonio Narro image/svg+xml
        • Mario Alberto Méndez Dorado Universidad Autónoma Agraria Antonio Narro image/svg+xml
          • Andrés Cadena Díaz Universidad Autónoma Agraria Antonio Narro image/svg+xml
            • Ariel Méndez Cifuentes Universidad Autónoma Agraria Antonio Narro image/svg+xml

              DOI:

              https://doi.org/10.19136/era.a13n1.4279

              Palabras clave:

              Manejo del agua, sensores remotos, optimización del riego, Manejo del agua, sensores remotos, optimización del riego, sistemas de riego y tecnología agrícola

              Resumen

              El objetivo del estudio fue analizar tres índices y dos bandas de reflectancia obtenidas con imágenes multiespectrales capturadas con drones, para evaluar el grado de estrés de un cultivo de frijol (Phaseolus vulgaris L.), bajo tres niveles de tensión de la humedad del suelo y dos posiciones de la cinta de riego (superficie y enterrada). Los resultados del estudio mostraron que el índice de agua de diferencia normalizada (NDWI) y la reflectancia en la banda del borde del rojo (RE), tienen una mejor relación con el contenido de humedad obteniendo diferencias significativas de mayor contenido de humedad en los tratamientos de cintilla enterrada y los tratamientos de menor tensión de humedad. Con el índice de diferencia normalizada del borde rojo (NDRE) se detectó diferencias significativas de mayor vigor (mayor contenido de clorofila) y por ende menos estrés con las plantas regadas con la cinta enterrada, para los tratamientos de tensión de humedad un mayor índice NDRE representa mayor contenido de humedad. Con el indicie de verdor triangular (TGI) solo se detectó diferencia en el vigor de las plantas (en el contenido de clorofila) en las tensiones de humedad cuando se comparó 30 kPa y 50 kPa. Con la reflectancia en la banda del rojo (R) se detectó mayor estrés hídrico en las plantas regadas con la cinta en la superficie, pero no se detectaron diferencias en las plantas sometidas a las tensiones de 20 kPa y 50 kPa.

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              Referencias

              Bhushan M, Negi A (2023). Impact of UAVs in Agriculture. In Goel N, Yadav R (Eds.), Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries (pp. 258-268). IGI Global. https://doi.org/10.4018/978-1-6684-8785-3.ch013.

              Boiarskii B (2019) Comparison of NDVI and NDRE Indices to Detect Differences in Vegetation and Chlorophyll Content. Journal of Mechanics of Continua and Mathematical Sciences, spl1(4). https://doi.org/10.26782/jmcms.spl.4/2019.11.00003.

              Bwambale E, Abagale FK, Anornu GK (2023) Data-Driven Modelling of Soil Moisture Dynamics for Smart Irrigation Scheduling. Smart Agricultural Technology, 5. https://doi.org/10.1016/j.atech.2023.100251.

              Carter GA, Knapp AK (2001) Leaf Optical Properties in Higher Plants: Linking Spectral Characteristics to Stress and Chlorophyll Concentration. American Journal of Botany, 88(4). https://doi.org/10.2307/2657068.

              Easterday K, Kislik C, Dawson T E, Hogan S, Kelly M (2019) Remotely Sensed Water Limitation in Vegetation: Insights from an Experiment with Unmanned Aerial Vehicles (UAVs). Remote Sensing, 11(16). https://doi.org/10.3390/rs11161853.

              Enciso J, Cholula U, Masih A, Chavez J L, Solorzano J, Laredo A (2020) Evaluating the Use of True Color Unmanned Aerial System Images for Irrigation Scheduling in Citrus. 6th Decennial National Irrigation Symposium. https://doi.org/10.13031/irrig.2020-032.

              Feng S, Qiu J, Crow W T, Mo X, Liu S, Wang S, Gao L, Wang X, Chen S (2023) Improved Estimation of Vegetation Water Content and its Impact on L-band Soil Moisture Retrieval Over Cropland. Journal of Hydrology, 617. https://doi.org/10.1016/j.jhydrol.2022.129015.

              FIRA (2022) Panorama Agroalimentario Frijol 2022. https://sursureste.org.mx/estudios/panorama-agroalimentario-frijol-2022/. Fecha de consulta 8 de junio de 2024.

              Gerardo R, de Lima I P (2023) Applying RGB-Based Vegetation Indices Obtained from UAS Imagery for Monitoring the Rice Crop at the Field Scale: A Case Study in Portugal. Agriculture (Switzerland), 13(10). https://doi.org/10.3390/agriculture13101916.

              Hunt E R, Doraiswamy P C, McMurtrey JE, Daughtry C S T, Perry E M, Akhmedov B (2012) A Visible Band Index for Remote Sensing Leaf Chlorophyll Content at the Canopy Scale. International Journal of Applied Earth Observation and Geoinformation, 21(1). https://doi.org/10.1016/j.jag.2012.07.020.

              Javornik T, Carović-Stanko K, Gunjača J, Vidak M, Lazarević B (2023) Monitoring Drought Stress in Common Bean Using Chlorophyll Fluorescence and Multispectral Imaging. Plants, 12(6). https://doi.org/10.3390/plants12061386.

              Jorge J, Vallbé M, Soler JA (2019) Detection of Irrigation Inhomogeneities in an Olive Grove Using the NDRE Vegetation Index Obtained from UAV Images. European Journal of Remote Sensing, 52(1). https://doi.org/10.1080/22797254.2019.1572459.

              Kamate A, Soro PA, Zoro-Diama EG, Diomande KS, Adohi-Krou AV (2023) Water Stress Early Detection of Eggplant Plants by Hyperspectral Fluorescence Spectroscopy. Open Journal of Applied Sciences, 13(03). https://doi.org/10.4236/ojapps.2023.133028.

              Kirongo CA (2016) A Review of Image Processing Software Techniques for Early Detection of Plant Drought Stress. International Journal of Computer Applications Technology and Research, 5(6). https://doi.org/10.7753/ijcatr0506.1009.

              Lin S, Li J, Liu Q, Li L, Zhao J, Yu W (2019) Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity. Remote Sensing, 11(11). https://doi.org/10.3390/rs11111303.

              Lipovac A, Bezdan A, Moravćević D, Djurović N, Ćosić M, Benka P, Stričević R (2022) Correlation between Ground Measurements and Uav Sensed Vegetation Indices for Yield Prediction of Common Bean Grown Under Different Irrigation Treatments and Sowing Periods. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4162287.

              LIU Xiao-Lei, QIN Zhi-Hao (2007) Comparative Analysis between NDWI and NDVI Indicesin Regional Drought Monitoring[J]. Remote Sensing Technology and Application, 22(5): 608-612 https://doi.org/10.11873/j.issn.1004-0323.2007.5.608.

              McFeeters SK (1996) The use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features. International Journal of Remote Sensing, 17(7). https://doi.org/10.1080/01431169608948714.

              Nagy A, Kiss NÉ, Buday-Bódi E, Magyar T, Cavazza F, Gentile SL, Abdullah H, Tamás J, Fehér ZZ (2024) Precision Estimation of Crop Coefficient for Maize Cultivation Using High-Resolution Satellite Imagery to Enhance Evapotranspiration Assessment in Agriculture. Plants, 13(9). https://doi.org/10.3390/plants13091212.

              Nunes EC (2023) Employing Drones in Agriculture: An Exploration of Various Drone Types and Key Advantages. https://doi.org/10.48550/arXiv.2307.04037

              Ohya T, Yoshida S, Kawabata R, Okabe H, Kai S (2002) Biophoton Emission Due to Drought Injury in Red Beans: Possibility of Early Detection of Drought Injury. Japanese Journal of Applied Physics, Part 1: Regular Papers and Short Notes and Review Papers, 41(7 A). https://doi.org/10.1143/jjap.41.4766.

              Perna OF, de Lima JD, Rivadavea WR, Barbosa JM, Braganholi JH, Bazanella AP, Silva GJ (2022) Evaluation of Water Stress and Co-Inoculation of Azospirillum Brasilense and Rhizobium Tropici in Beans (Phaseolus vulgaris L.). Arquivos de Ciências Veterinárias e Zoologia da Unipar, 25(2conv). https://doi.org/10.25110/arqvet.v25i2conv.2022.8795

              Quemada C, Pérez-Escudero J M, Gonzalo R, Ederra I, Santesteban LG, Torres N, Iriarte JC (2021) Remote Sensing for Plant Water Content Monitoring: A Review. In Remote Sensing (Vol. 13, Issue 11). https://doi.org/10.3390/rs13112088.

              Rathod P D, Shinde GU (2023) Autonomous Aerial System (UAV) for Sustainable Agriculture: A Review. International Journal of Environment and Climate Change, 13(8). https://doi.org/10.9734/ijecc/2023/v13i82080.

              Rodríguez-Fernández P, Sánchez-Mora C (2021) Ecological Production of Beans (Phaseolus Vulgaris L.) in the Edaphoclimatic Conditions of the III Frente. https://www.redalyc.org/articulo.oa?id=181369731005.

              SADER (2022) Estima Agricultura Crecimiento de 11.4% de la Producción de Frijol en 2021; Mantiene Tendencia al Alza. https://www.gob.mx/agricultura/prensa/estima-agricultura-crecimiento-de-11-4-de-la-produccion-de-frijol-en-2021-mantiene-tendencia-al-alza#:~:text=La%20Secretar%C3%ADa%20de%20Agricultura%20y%20Desarrollo%20Rural%20inform%C3%B3%20que%20la,071%20toneladas%20cosechadas%20en%202020. Fecha de consulta 8 de junio de 2024.

              Saravia D, Valqui-Valqui L, Salazar W, Quille-Mamani J, Barboza E, Porras-Jorge R, Injante P, Arbizu CI (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(5). https://doi.org/10.3390/drones7050325.

              Shi H, Guo J, An J, Tang Z, Wang X, Li W, Zhao X, Jin L, Xiang Y, Li Z, Zhang F (2023) Estimation of Chlorophyll Content in Soybean Crop at Different Growth Stages Based on Optimal Spectral Index. Agronomy, 13(3). https://doi.org/10.3390/agronomy13030663.

              Verma B, Porwal M, Jha AK, Vyshnavi RG, Rajpoot A, Nagar AK (2023) Enhancing Precision Agriculture and Environmental Monitoring Using Proximal Remote Sensing. Journal of Experimental Agriculture International, 45(8). https://doi.org/10.9734/jeai/2023/v45i82168.

              Visitacion GJ, Saludes RB, Luyun RA, Pinca, YMM, Eusebio MFV (2022) Statistical Analysis of Crop Water Stress in Rainfed Rice (Oryza sativa L.) Using Spectral and Non-spectral Indices. Philippine Journal of Science 151(2). https://doi.org/10.56899/151.02.04.

              Xue J, Su B (2017) Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. In Journal of Sensors 2017. https://doi.org/10.1155/2017/1353691.

              Zhang C, Pattey E, Liu J, Cai H, Shang J, Dong T (2018) Retrieving Leaf and Canopy Water Content of Winter wheat Using Vegetation Water Indices. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(1). https://doi.org/10.1109/JSTARS.2017.2773625.

              Zhang Y, Yang Y, Zhang Q, Duan R, Liu J, Qin Y, Wang X (2023) Toward Multi-Stage Phenotyping of Soybean with Multimodal UAV Sensor Data: A Comparison of Machine Learning Approaches for Leaf Area Index Estimation. Remote Sensing, 15(1). https://doi.org/10.3390/rs15010007.

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              Publicado

              2026-02-25

              Número

              Sección

              ARTÍCULOS CIENTÍFICOS

              Cómo citar

              Montoya Pineda, N. M., Cadena Zapata, M., Zermeño Gonzáles, A., Méndez Dorado, M. A., Cadena Díaz, A., & Méndez Cifuentes, A. (2026). Análisis del estrés hídrico en frijol utilizando imágenes multiespectrales y sensores de humedad del suelo. Ecosistemas Y Recursos Agropecuarios, 13(1). https://doi.org/10.19136/era.a13n1.4279

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