IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v10y2020i10p451-d422330.html
   My bibliography  Save this article

Estimation of Total Nitrogen Content in Forage Maize ( Zea mays L.) Using Spectral Indices: Analysis by Random Forest

Author

Listed:
  • Magali J. López-Calderón

    (Facultad de Agricultura y Zootecnia (FAZ-UJED), Universidad Juárez del Estado de Durango, Ejido Venecia, Tlahualilo Km 35, Gómez Palacio 35111, Mexico)

  • Juan Estrada-Ávalos

    (INIFAP CENID-RASPA, Km 6.5 márgen derecho del canal del Sacramento, Gómez Palacio 35140, Mexico)

  • Víctor M. Rodríguez-Moreno

    (INIFAP Campo Experimental Pabellón, Km 32.5, Carretera Ags-Zac, Pabellón de Arteaga 20660, Mexico)

  • Jorge E. Mauricio-Ruvalcaba

    (INIFAP Campo Experimental Pabellón, Km 32.5, Carretera Ags-Zac, Pabellón de Arteaga 20660, Mexico)

  • Aldo R. Martínez-Sifuentes

    (INIFAP CENID-RASPA, Km 6.5 márgen derecho del canal del Sacramento, Gómez Palacio 35140, Mexico)

  • Gerardo Delgado-Ramírez

    (INIFAP CENID-RASPA, Km 6.5 márgen derecho del canal del Sacramento, Gómez Palacio 35140, Mexico)

  • Enrique Miguel-Valle

    (INIFAP CENID-RASPA, Km 6.5 márgen derecho del canal del Sacramento, Gómez Palacio 35140, Mexico)

Abstract

Knowing the total Nitrogen content (Nt) of forage maize ( Zea mays ) is important so that decisions can be made quickly and efficiently to adjust the timing and amount of both irrigation and fertilizer. In 2017 and 2018 during three growing cycles in two study plots, leaf samples were collected and the Dumas method was used to estimate Nt. During the same growing seasons and on the same sampling plots, a Parrot Sequoia camera mounted on an unmanned aerial vehicle (UAV) was used to collect high resolution images of forage maize study plots. Thirteen multispectral indices were generated and, from these, a Random Forest (RF) algorithm was used to estimate Nt. RF is a machine-learning technique and is designed to work with extremely large datasets. Overall analysis showed five of the 13 indices as the most important. One of these five, the Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index, was found to be the most important for estimation of Nt in forage maize (R 2 = 0.76). RF handled the complex dataset in a time-efficient manner and Nt did not differ significantly when compared between traditional methods of evaluating Nt at the canopy level and using UAVs and RF to estimate Nt in forage maize. This result is an opportunity to explore many new research options in precision farming and digital agriculture.

Suggested Citation

  • Magali J. López-Calderón & Juan Estrada-Ávalos & Víctor M. Rodríguez-Moreno & Jorge E. Mauricio-Ruvalcaba & Aldo R. Martínez-Sifuentes & Gerardo Delgado-Ramírez & Enrique Miguel-Valle, 2020. "Estimation of Total Nitrogen Content in Forage Maize ( Zea mays L.) Using Spectral Indices: Analysis by Random Forest," Agriculture, MDPI, vol. 10(10), pages 1-15, October.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:10:p:451-:d:422330
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/10/10/451/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/10/10/451/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Krishna, Gopal & Sahoo, Rabi N. & Singh, Prafull & Bajpai, Vaishangi & Patra, Himesh & Kumar, Sudhir & Dandapani, Raju & Gupta, Vinod K. & Viswanathan, C. & Ahmad, Tauqueer & Sahoo, Prachi M., 2019. "Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing," Agricultural Water Management, Elsevier, vol. 213(C), pages 231-244.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tazeem Haider & Muhammad Shahid Farid & Rashid Mahmood & Areeba Ilyas & Muhammad Hassan Khan & Sakeena Tul-Ain Haider & Muhammad Hamid Chaudhry & Mehreen Gul, 2021. "A Computer-Vision-Based Approach for Nitrogen Content Estimation in Plant Leaves," Agriculture, MDPI, vol. 11(8), pages 1-19, August.
    2. Zinhle Mashaba-Munghemezulu & George Johannes Chirima & Cilence Munghemezulu, 2021. "Modeling the Spatial Distribution of Soil Nitrogen Content at Smallholder Maize Farms Using Machine Learning Regression and Sentinel-2 Data," Sustainability, MDPI, vol. 13(21), pages 1-21, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peng, Zhigong & Lin, Shaozhe & Zhang, Baozhong & Wei, Zheng & Liu, Lu & Han, Nana & Cai, Jiabing & Chen, He, 2020. "Winter Wheat Canopy Water Content Monitoring Based on Spectral Transforms and “Three-edge” Parameters," Agricultural Water Management, Elsevier, vol. 240(C).
    2. Bretreger, David & Yeo, In-Young & Hancock, Greg, 2022. "Quantifying irrigation water use with remote sensing: Soil water deficit modelling with uncertain soil parameters," Agricultural Water Management, Elsevier, vol. 260(C).
    3. Wu, Yinshan & Jiang, Jie & Zhang, Xiufeng & Zhang, Jiayi & Cao, Qiang & Tian, Yongchao & Zhu, Yan & Cao, Weixing & Liu, Xiaojun, 2023. "Combining machine learning algorithm and multi-temporal temperature indices to estimate the water status of rice," Agricultural Water Management, Elsevier, vol. 289(C).
    4. Osama Elsherbiny & Yangyang Fan & Lei Zhou & Zhengjun Qiu, 2021. "Fusion of Feature Selection Methods and Regression Algorithms for Predicting the Canopy Water Content of Rice Based on Hyperspectral Data," Agriculture, MDPI, vol. 11(1), pages 1-21, January.
    5. Cheng, Minghan & Jiao, Xiyun & Liu, Yadong & Shao, Mingchao & Yu, Xun & Bai, Yi & Wang, Zixu & Wang, Siyu & Tuohuti, Nuremanguli & Liu, Shuaibing & Shi, Lei & Yin, Dameng & Huang, Xiao & Nie, Chenwei , 2022. "Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning," Agricultural Water Management, Elsevier, vol. 264(C).
    6. Mi Tian & Chao Wu & Xin Zhu & Qinghai Hu & Xueqiu Wang & Binbin Sun & Jian Zhou & Wei Wang & Qinghua Chi & Hanliang Liu & Yuheng Liu & Jiwu Yang & Xurong Li, 2024. "Spatial–Temporal Variations in Soil Organic Carbon and Driving Factors in Guangdong, China (2009–2023)," Land, MDPI, vol. 13(7), pages 1-18, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:10:y:2020:i:10:p:451-:d:422330. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.