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Early Estimation of Tomato Yield by Decision Tree Ensembles

Author

Listed:
  • Mario Lillo-Saavedra

    (Facultad de Ingeniería Agrícola, Universidad de Concepción, Chillán 3812120, Chile)

  • Alberto Espinoza-Salgado

    (Facultad de Ingeniería Agrícola, Universidad de Concepción, Chillán 3812120, Chile)

  • Angel García-Pedrero

    (Department of Computer Architecture and Technology, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Spain)

  • Camilo Souto

    (Facultad de Ingeniería Agrícola, Universidad de Concepción, Chillán 3812120, Chile
    Department of Horticulture, Oregon State University, 4017 Agricultural and Life Sciences Building, Corvallis, OR 97331, USA)

  • Eduardo Holzapfel

    (Facultad de Ingeniería Agrícola, Universidad de Concepción, Chillán 3812120, Chile)

  • Consuelo Gonzalo-Martín

    (Department of Computer Architecture and Technology, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Spain)

  • Marcelo Somos-Valenzuela

    (Department of Forest Sciences, Faculty of Agriculture and Environmental Sciencies, Universidad de La Frontera, Av. Francisco Salazar 01145, Temuco 4780000, Chile)

  • Diego Rivera

    (Centro de Sustentabilidad y Gestión Estratégica de Recursos (CiSGER), Facultad de Ingeniería, Universidad del Desarrollo, Las Condes, Santiago 7610658, Chile)

Abstract

Crop yield forecasting allows farmers to make decisions in advance to improve farm management and logistics during and after harvest. In this sense, crop yield potential maps are an asset for farmers making decisions about farm management and planning. Although scientific efforts have been made to determine crop yields from in situ information and through remote sensing, most studies are limited to evaluating data from a single date just before harvest. This has a direct negative impact on the quality and predictability of these estimates, especially for logistics. This study proposes a methodology for the early prediction of tomato yield using decision tree ensembles, vegetation spectral indices, and shape factors from images captured by multispectral sensors on board an unmanned aerial vehicle (UAV) during different phenological stages of crop development. With the predictive model developed and based on the collection of training characteristics for 6 weeks before harvest, the tomato yield was estimated for a 0.4 ha plot, obtaining an error rate of 9.28 %.

Suggested Citation

  • Mario Lillo-Saavedra & Alberto Espinoza-Salgado & Angel García-Pedrero & Camilo Souto & Eduardo Holzapfel & Consuelo Gonzalo-Martín & Marcelo Somos-Valenzuela & Diego Rivera, 2022. "Early Estimation of Tomato Yield by Decision Tree Ensembles," Agriculture, MDPI, vol. 12(10), pages 1-13, October.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1655-:d:937816
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    References listed on IDEAS

    as
    1. Parthasarathy Velusamy & Santhosh Rajendran & Rakesh Kumar Mahendran & Salman Naseer & Muhammad Shafiq & Jin-Ghoo Choi, 2021. "Unmanned Aerial Vehicles (UAV) in Precision Agriculture: Applications and Challenges," Energies, MDPI, vol. 15(1), pages 1-19, December.
    2. Fukuda, Shinji & Spreer, Wolfram & Yasunaga, Eriko & Yuge, Kozue & Sardsud, Vicha & Müller, Joachim, 2013. "Random Forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes," Agricultural Water Management, Elsevier, vol. 116(C), pages 142-150.
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