Estimation of Productivity and Above-Ground Biomass for Corn ( Zea mays ) via Vegetation Indices in Madeira Island

Fabrício Lopes Macedo (), Humberto Nóbrega, José G. R. de Freitas, Carla Ragonezi, Lino Pinto, Joana Rosa and Miguel A. A. Pinheiro de Carvalho
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Fabrício Lopes Macedo: ISOPlexis Centre of Sustainable Agriculture and Food Technology, Campus da Penteada, University of Madeira, 9020-105 Funchal, Portugal
Humberto Nóbrega: ISOPlexis Centre of Sustainable Agriculture and Food Technology, Campus da Penteada, University of Madeira, 9020-105 Funchal, Portugal
José G. R. de Freitas: ISOPlexis Centre of Sustainable Agriculture and Food Technology, Campus da Penteada, University of Madeira, 9020-105 Funchal, Portugal
Carla Ragonezi: ISOPlexis Centre of Sustainable Agriculture and Food Technology, Campus da Penteada, University of Madeira, 9020-105 Funchal, Portugal
Lino Pinto: ISOPlexis Centre of Sustainable Agriculture and Food Technology, Campus da Penteada, University of Madeira, 9020-105 Funchal, Portugal
Joana Rosa: ISOPlexis Centre of Sustainable Agriculture and Food Technology, Campus da Penteada, University of Madeira, 9020-105 Funchal, Portugal
Miguel A. A. Pinheiro de Carvalho: ISOPlexis Centre of Sustainable Agriculture and Food Technology, Campus da Penteada, University of Madeira, 9020-105 Funchal, Portugal

Agriculture, 2023, vol. 13, issue 6, 1-14

Abstract: The advancement of technology associated with the field, especially the use of unmanned aerial vehicles (UAV) coupled with multispectral cameras, allows us to monitor the condition of crops in real time and contribute to the field of machine learning. The objective of this study was to estimate both productivity and above-ground biomass (AGB) for the corn crop by applying different vegetation indices (VIs) via high-resolution aerial imagery. Among the indices tested, strong correlations were obtained between productivity and the normalized difference vegetation index (NDVI) with a significance level of p < 0.05 (0.719), as well as for the normalized difference red edge (NDRE), or green normalized difference vegetation index (GNDVI) with crop productivity ( p < 0.01), respectively 0.809 and 0.859. The AGB results align with those obtained previously; GNDVI and NDRE showed high correlations, but now with a significance level of p < 0.05 (0.758 and 0.695). Both GNDVI and NDRE indices showed coefficients of determination for productivity and AGB estimation with 0.738 and 0.654, and 0.701 and 0.632, respectively. The use of the GNDVI and NDRE indices shows excellent results for estimating productivity as well as AGB for the corn crop, both at the spatial and numerical levels. The possibility of predicting crop productivity is an essential tool for producers, since it allows them to make timely decisions to correct any deficit present in their agricultural plots, and further contributes to AI integration for drone digital optimization.

Keywords: precision agriculture; NDRE; NDVI; GNDVI; modeling training; machine learning; multispectral images; artificial intelligence (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: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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