Early Estimation of Tomato Yield by Decision Tree Ensembles

Mario Lillo-Saavedra (), Alberto Espinoza-Salgado, Angel García-Pedrero, Camilo Souto, Eduardo Holzapfel, Consuelo Gonzalo-Martín, Marcelo Somos-Valenzuela and Diego Rivera
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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
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

Agriculture, 2022, vol. 12, issue 10, 1-13

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 %.

Keywords: decision tree ensemble; crop yield; UAVs (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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