IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i12p7125-d835744.html
   My bibliography  Save this article

Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models

Author

Listed:
  • Ricardo Gava

    (Department of Agronomy, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, Mato Grosso do Sul, Brazil)

  • Dthenifer Cordeiro Santana

    (Graduate Program in Plant Production, State University of São Paulo (UNESP), Ilha Solteira, São Paulo 15385-000, São Paulo, Brazil)

  • Mayara Favero Cotrim

    (Graduate Program in Plant Production, State University of São Paulo (UNESP), Ilha Solteira, São Paulo 15385-000, São Paulo, Brazil)

  • Fernando Saragosa Rossi

    (Graduate Program in Soil Science, State University of São Paulo (UNESP), Jaboticabal, São Paulo 14884-900, São Paulo, Brazil)

  • Larissa Pereira Ribeiro Teodoro

    (Department of Agronomy, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, Mato Grosso do Sul, Brazil)

  • Carlos Antonio da Silva Junior

    (Department of Geography, State University of Mato Grosso (UNEMAT), Sinop 78555-000, Mato Grosso, Brazil)

  • Paulo Eduardo Teodoro

    (Department of Agronomy, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, Mato Grosso do Sul, Brazil)

Abstract

Using remote sensing combined with machine learning (ML) techniques is a promising approach to classify soybean cultivars. Therefore, the objectives of this study are (i) to verify which input dataset configuration (using only spectral bands, only vegetation indices, or both) is more accurate in the identification of soybean cultivars, and (ii) to verify which ML technique is more accurate in the identification of soybean cultivars. Information was extracted from five central irrigation pivots in the same region and with the same sowing date in the 2015/2016 crop year, in which each pivot was cultivated with a different cultivar, in which the cultivars used were: CV1—P98y12 RR, CV2—Desafio RR, CV3—M6410 IPRO, CV4—M7110 IPRO, and CV5—NA5909 RR. A cloud-free orbital image of the site was acquired from the Google Earth Engine platform. In addition to the spectral bands alone, a total of 13 vegetation indices were calculated. The models tested were: artificial neural networks (ANN), radial basis function network (RBF), decision tree algorithms J48 (DT) and reduced error pruning tree (REP), random forest (RF), and support vector machine (SVM). The five soybean cultivars were classified by the six-machine learning (ML) models in stratified randomized cross-validation with k-fold = 10 and 10 repetitions (100 runs for each model). After obtaining the r and MAE statistics, analysis of variance was performed considering a 6 × 3 factorial scheme (models versus inputs) with 10 repetitions (folds). The means were grouped by the Scott–Knott test at 5% probability. The spectral bands were the most accurate among the tested inputs in the identification of soybean cultivars. ANN was the most accurate model in identifying soybean cultivars.

Suggested Citation

  • Ricardo Gava & Dthenifer Cordeiro Santana & Mayara Favero Cotrim & Fernando Saragosa Rossi & Larissa Pereira Ribeiro Teodoro & Carlos Antonio da Silva Junior & Paulo Eduardo Teodoro, 2022. "Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models," Sustainability, MDPI, vol. 14(12), pages 1-12, June.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7125-:d:835744
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/12/7125/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/12/7125/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Moradi, G.R. & Dehghani, S. & Khosravian, F. & Arjmandzadeh, A., 2013. "The optimized operational conditions for biodiesel production from soybean oil and application of artificial neural networks for estimation of the biodiesel yield," Renewable Energy, Elsevier, vol. 50(C), pages 915-920.
    Full references (including those not matched with items on IDEAS)

    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. Abhirup Khanna & Bhawna Yadav Lamba & Sapna Jain & Vadim Bolshev & Dmitry Budnikov & Vladimir Panchenko & Alexandr Smirnov, 2023. "Biodiesel Production from Jatropha: A Computational Approach by Means of Artificial Intelligence and Genetic Algorithm," Sustainability, MDPI, vol. 15(12), pages 1-33, June.
    2. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    3. Can, Özer & Baklacioglu, Tolga & Özturk, Erkan & Turan, Onder, 2022. "Artificial neural networks modeling of combustion parameters for a diesel engine fueled with biodiesel fuel," Energy, Elsevier, vol. 247(C).
    4. Vellaiyan, Suresh & Partheeban, C.M. Anand, 2020. "Combined effect of water emulsion and ZnO nanoparticle on emissions pattern of soybean biodiesel fuelled diesel engine," Renewable Energy, Elsevier, vol. 149(C), pages 1157-1166.
    5. Sina Faizollahzadeh Ardabili & Bahman Najafi & Meysam Alizamir & Amir Mosavi & Shahaboddin Shamshirband & Timon Rabczuk, 2018. "Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters," Energies, MDPI, vol. 11(11), pages 1-19, October.
    6. Soltani, Soroush & Roodbar Shojaei, Taha & Khanian, Nasrin & Shean Yaw Choong, Thomas & Asim, Nilofar & Zhao, Yue, 2022. "Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst," Renewable Energy, Elsevier, vol. 187(C), pages 760-773.
    7. Manieniyan, V. & Vinodhini, G. & Senthilkumar, R. & Sivaprakasam, S., 2016. "Wear element analysis using neural networks of a DI diesel engine using biodiesel with exhaust gas recirculation," Energy, Elsevier, vol. 114(C), pages 603-612.
    8. Sakthivel, G. & Sivaraja, C.M. & Ikua, Bernard W., 2019. "Prediction OF CI engine performance, emission and combustion parameters using fish oil as a biodiesel by fuzzy-GA," Energy, Elsevier, vol. 166(C), pages 287-306.
    9. Marina Corral Bobadilla & Roberto Fernández Martínez & Rubén Lostado Lorza & Fátima Somovilla Gómez & Eliseo P. Vergara González, 2018. "Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines," Energies, MDPI, vol. 11(11), pages 1-19, November.
    10. Aghbashlo, Mortaza & Hosseinpour, Soleiman & Tabatabaei, Meisam & Dadak, Ali, 2017. "Fuzzy modeling and optimization of the synthesis of biodiesel from waste cooking oil (WCO) by a low power, high frequency piezo-ultrasonic reactor," Energy, Elsevier, vol. 132(C), pages 65-78.
    11. Silitonga, A.S. & Shamsuddin, A.H. & Mahlia, T.M.I. & Milano, Jassinne & Kusumo, F. & Siswantoro, Joko & Dharma, S. & Sebayang, A.H. & Masjuki, H.H. & Ong, Hwai Chyuan, 2020. "Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization," Renewable Energy, Elsevier, vol. 146(C), pages 1278-1291.
    12. Shelare, Sagar D. & Belkhode, Pramod N. & Nikam, Keval Chandrakant & Jathar, Laxmikant D. & Shahapurkar, Kiran & Soudagar, Manzoore Elahi M. & Veza, Ibham & Khan, T.M. Yunus & Kalam, M.A. & Nizami, Ab, 2023. "Biofuels for a sustainable future: Examining the role of nano-additives, economics, policy, internet of things, artificial intelligence and machine learning technology in biodiesel production," Energy, Elsevier, vol. 282(C).
    13. Sakthivel, G. & Sivakumar, R. & Saravanan, N. & Ikua, Bernard W., 2017. "A decision support system to evaluate the optimum fuel blend in an IC engine to enhance the energy efficiency and energy management," Energy, Elsevier, vol. 140(P1), pages 566-583.
    14. Tamilselvan, P. & Nallusamy, N. & Rajkumar, S., 2017. "A comprehensive review on performance, combustion and emission characteristics of biodiesel fuelled diesel engines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1134-1159.
    15. Vardast, Neda & Haghighi, Mohammad & Dehghani, Sahar, 2019. "Sono-dispersion of calcium over Al-MCM-41used as a nanocatalyst for biodiesel production from sunflower oil: Influence of ultrasound irradiation and calcium content on catalytic properties and perform," Renewable Energy, Elsevier, vol. 132(C), pages 979-988.
    16. Dehghani, Sahar & Haghighi, Mohammad, 2020. "Sono-enhanced dispersion of CaO over Zr-Doped MCM-41 bifunctional nanocatalyst with various Si/Zr ratios for conversion of waste cooking oil to biodiesel," Renewable Energy, Elsevier, vol. 153(C), pages 801-812.

    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:jsusta:v:14:y:2022:i:12:p:7125-:d:835744. 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.