FAO. FAO Statistical Yearbook 2023. Food and Agriculture Organization of the United Nation. 2023.
Helios W, Serafin-Andrzejewska M, Kozak M, Lewandowska S. Impact of nitrogen fertilisation and inoculation on soybean nodulation, nitrogen status, and yield in a central European climate. Agriculture (Basel). 2025;15(15):1654.
Google Scholar
Luo D, Chen Y, Lin H. Agronomic Optimization of Fertilization and Irrigation Regimes for High-Yield Soybean Cultivation. Field Crop. 2025;8(4):176–86.
Luo K, Yuan X, Zhang K, Fu Z, Lin P, Li Y, Yong T, et al. Soybean Variety Improves Canopy Architecture and Light Distribution to Promote Yield Formation in Maize–Soybean Strip Intercropping. Plant Cell Environ. 2025.
Ali MF, Ma L, Sohail S, Zulfiqar U, Hussain T, Lin X, et al. Zinc biofortification in cereal crops: overview and prospects. J Soil Sci Plant Nutr. 2025;25:4260–94. https://doi.org/10.1007/s42729-025-02396-x.
Google Scholar
Brahma B, Hammermeister A, Lynch D, Smith P, Nath AJ. Significance of land management practices under haskap orchards to mitigate the degradations of soil organic carbon stocks and soil health because of land use changes from forest and grassland. Soil Use Manage. 2025;41(1):e70037.
Google Scholar
Aarif M, Alam A, Hotak Y. Smart sensor technologies shaping the future of precision agriculture: Recent advances and future outlooks. J Sensors. 2025;2025:2460098.
Lhotáková Z, Neuwirthová E, Potůčková M, Červená L, Hunt L, Kupková L, et al. Mind the leaf anatomy while taking ground truth with portable chlorophyll meters. Sci Rep. 2025;15(1):1855.
Google Scholar
Tsaniklidis G, Makraki T, Papadimitriou D, Nikoloudakis N, Taheri-Garavand A, Fanourakis D. Non-destructive estimation of area and greenness in leaf and seedling scales: a case study in cucumber. Agronomy. 2025;15(10):2294.
Google Scholar
Bulacio Fischer PT, Carella A, Massenti R, Fadhilah R, Lo Bianco R. Advances in monitoring crop and soil nutrient status: proximal and remote sensing techniques. Horticulturae. 2025;11(2):182. https://doi.org/10.3390/horticulturae11020182.
Google Scholar
Paul NC, Ponnaganti N, Gaikwad BB, Sammi Reddy K, Nangare DD. Optimized soil adjusted vegetation index mapping of Pune district using Google Earth Engine. Remote Sens Lett. 2025;16(7):728–36.
Google Scholar
Yan K, Gao S, Yan G, Ma X, Chen X, Zhu P, et al. A global systematic review of the remote sensing vegetation indices. Int J Appl Earth Obs Geoinf. 2025;139:104560.
Berry A, Vivier MA, Poblete-Echeverría C. Evaluation of canopy fraction-based vegetation indices, derived from multispectral UAV imagery, to map water status variability in a commercial vineyard. Irrig Sci. 2025;43(1):135–53.
Google Scholar
Anand SL, Visakh R, Nalishma R, Sah RP, Beena R. High throughput phenomics in elucidating drought stress responses in rice (Oryza sativa L.). J Plant Biochem Biotechnol. 2025;34(1):119–32. https://doi.org/10.1007/s13562-024-00949-2.
Maimaitijiang M, Sagan V, Sidike P, Hartling S, Esposito F, Fritschi FB. Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sens Environ. 2020;237:111599.
Google Scholar
Xie C, Yang C. A review on plant high-throughput phenotyping traits using UAV-based sensors. Comput Electron Agric. 2020;178:105731.
Google Scholar
Nugroho AP, Wiratmoko A, Nugraha D, Markumningsih S, Sutiarso L, Falah MAF, Okayasu T. Development of a low-cost thermal imaging system for water stress monitoring in indoor farming. Smart Agric Technol 2025;11:101048. https://doi.org/10.1016/j.atech.2025.101048.
Yang CY, Zhang YC, Hou YL. Assessing water status in rice plants in water-deficient environments using thermal imaging. Bot Stud (Taipei). 2025;66(1):6.
Google Scholar
Sharma H, Sidhu H, Bhowmik A. Remote sensing using unmanned aerial vehicles for water stress detection: a review focusing on specialty crops. Drones. 2025;9(4):241.
Google Scholar
Zhai W, Cheng Q, Duan F, Huang X, Chen Z. Remote sensing-based analysis of yield and water-fertilizer use efficiency in winter wheat management. Agric Water Manage. 2025;311:109390.
Google Scholar
Denre M, Shyamrao ID, Kumar A. Study on zinc as plant nutrient: a review. J Sci Res Rep. 2025;31(6):972–99.
Google Scholar
Madaan I, Sharma P, Singh AD, Dhiman S, Kour J, Kumar P, et al. Zinc and plant hormones: an updated review. Zinc in Plants; 2025. p. 193–223. ISBN: 978-0-323-91314-0.
Pelagio-Flores R, Ravelo-Ortega G, García-Pineda E, López-Bucio J. A century of Azospirillum: plant growth promotion and agricultural promise. Plant Signal Behav. 2025;20(1):2551609.
Google Scholar
Egli DB, Bruening WP. Temporal profiles of pod production and pod set in soybean. Eur J Agron. 2006;24(1):11–8.
Google Scholar
Uddling J, Gelang-Alfredsson J, Piikki K, Pleijel H. Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynth Res. 2007;91(1):37–46.
Google Scholar
Broadley MR, White PJ, Hammond JP, Zelko I, Lux A. Zinc in plants. New Phytol. 2007;173(4):677–702.
Google Scholar
Díaz-Rodríguez AM, Parra Cota FI, Cira Chávez LA, García Ortega LF, Estrada Alvarado MI, Santoyo G, et al. Microbial inoculants in sustainable agriculture: advancements, challenges, and future directions. Plants. 2025;14(2):191.
Google Scholar
Hungria M, Campo RJ, Mendes IC, Graham PH. Contribution of biological nitrogen fixation to the N nutrition of grain crops in the tropics: the success of soybean (Glycine max L. Merr.) in South America. In: Singh RP, Shankar N, Jaiwal PK, editors. Nitrogen nutrition and sustainable plant productivity. Houston: Studium Press; 2006. p. 43–93. ISBN 1-933699-00-0.
Lee H, Kang Y, Kim J. Remote sensing-based assessment of soybean growth and yield prediction using integrated spectral and thermal indices. Front Plant Sci. 2023;14:1182314. https://doi.org/10.3389/fpls.2023.1182314.
Google Scholar
Ma BL, Dwyer LM, Costa C, Cober ER, Morrison MJ. Early prediction of soybean yield from canopy reflectance measurements. Agron J. 2001;93(6):1227–34.
Google Scholar
Pineda M, Perez-Bueno ML, Barón M, Calderón R. Assessment of crop performance under stress conditions by remote sensing: A case study in soybean. Agric For Meteorol. 2021;311:108663. https://doi.org/10.1016/j.agrformet.2021.108663.
Google Scholar
Richardson AD, Duigan SP, Berlyn GP. An evaluation of noninvasive methods to estimate foliar chlorophyll content. New Phytol. 2002;153(1):185–94.
Google Scholar
Gitelson AA. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J Plant Physiol. 2004;161(2):165–73.
Google Scholar
Hansen PM, Schjoerring JK. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens Environ. 2003;86(4):542–53.
Google Scholar
Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices. Remote Sens Environ. 1996;55(2):95–107.
Google Scholar
Kumagai E, Aoki N, Masuya Y, Shimono H. Phenotypic plasticity conditions the response of soybean seed yield to elevated atmospheric CO2 concentration. Plant Physiol. 2015;169(3):2021–9.
Google Scholar
Turnage, G. Sampling Submersed Aquatic Plant Biomass: Fresh vs. Dry Weight. GeoSystems Research Institute Report, 5093. Mississippi State University. 2022.
Xu Z, Zhou G. Responses of photosynthetic capacity to soil moisture gradient in perennial rhizome grass and perennial bunchgrass. BMC Plant Biol. 2011;11(1):21.
Google Scholar
Yan S, Weng B, Jing L, Bi W. Effects of drought stress on water content and biomass distribution in summer maize (Zea mays L.). Front Plant Sci. 2023;14:1118131.
Google Scholar
Gomez, K.A., and A. A. Gomez. Statistical procedures for agricultural research. 2 st Ed. John wiley and sons; New York (U.S.A.). 1984. https://pdf.usaid.gov/pdf_docs/PNAAR208.pdf
McKinney W. Data structures for statistical computing in Python. Scipy. 2010;445(1):51–6.
Hunter JD. Matplotlib: a 2D graphics environment. Comput Sci Eng. 2007;9(03):90–5.
Google Scholar
Waskom ML. Seaborn: statistical data visualization. J Open Source Softw. 2021;6(60):3021.
Google Scholar
Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):261–72.
Google Scholar
Tunc M, Ipekesen S, Basdemir F, Akinci C, Bicer BT. Effect of Organic and Inorganic Fertilizer Doses on Yield and Yield Components of Common Beans. J Anim Plant Sci. 2023;33:1333–45. https://doi.org/10.36899/JAPS.2023.6.0673.
Hungria M, de O Chueire LM, Coca RG, Megı́as M. Preliminary characterization of fast growing rhizobial strains isolated from soyabean nodules in Brazil. Soil Biol Biochem. 2001;33(10):1349–61.
Google Scholar
Fritschi FB, Ray JD. Soybean leaf nitrogen, chlorophyll content, and chlorophyll a/b ratio. Photosynthetica. 2007;45(1):92–8.
Google Scholar
Zhao C, Liu B, Xiao L, Hoogenboom G, Boote KJ, Kassie BT, et al. A SIMPLE crop model. Eur J Agronomy. 2018;100:138–53. https://doi.org/10.1016/j.eja.2018.01.002.
Google Scholar
Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ. 2004;90(3):337–52.
Google Scholar
Peng S, Chen A, Xu L, Cao C, Fang J, Myneni RB, et al. Recent change of vegetation growth trend in China. Environ Res Lett. 2011;6(4):044027.
Google Scholar
Moges SM, Raun WR, Mullen RW, Freeman KW, Johnson GV, Solie JB. Evaluation of mid-season spectral reflectance indices for predicting grain yield and grain protein in winter wheat. J Plant Nutr. 2005;27(6):1061–80. https://doi.org/10.1081/PLN-120038544.
Google Scholar
Rufaioğlu SB, Bilgili AV, Savaşlı E, Özberk İ, Aydemir S, Ismael AM, et al. Sensor-based yield prediction in durum wheat under semi-arid conditions using machine learning across Zadoks growth stages. Remote Sens. 2025;17(14):2416.
Google Scholar
Jones HG. Irrigation scheduling: advantages and pitfalls of plant-based methods. J Exp Bot. 2004;55(407):2427–36.
Google Scholar
Costa JM, Grant OM, Chaves MM. Thermography to explore plant–environment interactions. J Exp Bot. 2013;64(13):3937–49.
Google Scholar
Prasad B, Carver BF, Stone ML, Babar MA, Raun WR, Klatt AR. Genetic analysis of indirect selection for winter wheat grain yield using spectral reflectance indices in wheat breeding. Field Crop Res. 2017;200:1–13. https://doi.org/10.1016/j.fcr.2016.10.001.
Google Scholar
González-Dugo V, Zarco-Tejada PJ, Fereres E. Applicability and limitations of using the crop water stress index as an indicator of water deficits in citrus orchards. Agric For Meteorol. 2014;198:94–104.
Google Scholar