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Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques

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  • Mohamad M. Awad

    (National Council for Scientific Research, Remote, Sensing Center, P.O. Box 11-8281, Beirut 11072260, Lebanon)

Abstract

Many crop yield estimation techniques are being used, however the most effective one is based on using geospatial data and technologies such as remote sensing. However, the remote sensing data which are needed to estimate crop yield are insufficient most of the time due to many problems such as climate conditions (% of clouds), and low temporal resolution. There have been many attempts to solve the lack of data problem using very high temporal and very low spatial resolution images such as Modis. Although this type of image can compensate for the lack of data due to climate problems, they are only suitable for very large homogeneous crop fields. To compensate for the lack of high spatial resolution remote sensing images due to climate conditions, a new optimization model was created. Crop yield estimation is improved and its precision is increased based on the new model that includes the use of the energy balance equation. To verify the results of the crop yield estimation based on the new model, information from local farmers about their potato crop yields for the same year were collected. The comparison between the estimated crop yields and the actual production in different fields proves the efficiency of the new optimization model.

Suggested Citation

  • Mohamad M. Awad, 2019. "Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques," Agriculture, MDPI, vol. 9(3), pages 1-13, March.
    • Handle: RePEc:gam:jagris:v:9:y:2019:i:3:p:54-:d:213605
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    References listed on IDEAS

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    1. Evangelos Anastasiou & Athanasios Balafoutis & Nikoleta Darra & Vasileios Psiroukis & Aikaterini Biniari & George Xanthopoulos & Spyros Fountas, 2018. "Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes," Agriculture, MDPI, vol. 8(7), pages 1-17, June.
    2. Yuei-An Liou & Sanjib Kumar Kar, 2014. "Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review," Energies, MDPI, vol. 7(5), pages 1-29, April.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
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    Cited by:

    1. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2023. "Prediction of Pea ( Pisum sativum L.) Seeds Yield Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(3), pages 1-19, March.
    2. Lili Zhou & Chenwei Nie & Tao Su & Xiaobin Xu & Yang Song & Dameng Yin & Shuaibing Liu & Yadong Liu & Yi Bai & Xiao Jia & Xiuliang Jin, 2023. "Evaluating the Canopy Chlorophyll Density of Maize at the Whole Growth Stage Based on Multi-Scale UAV Image Feature Fusion and Machine Learning Methods," Agriculture, MDPI, vol. 13(4), pages 1-22, April.
    3. Niwat Bhumiphan & Jurawan Nontapon & Siwa Kaewplang & Neti Srihanu & Werapong Koedsin & Alfredo Huete, 2023. "Estimation of Rubber Yield Using Sentinel-2 Satellite Data," Sustainability, MDPI, vol. 15(9), pages 1-15, April.
    4. Safi, Abdur Rahim & Karimi, Poolad & Mul, Marloes & Chukalla, Abebe & de Fraiture, Charlotte, 2022. "Translating open-source remote sensing data to crop water productivity improvement actions," Agricultural Water Management, Elsevier, vol. 261(C).

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