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Yield predict and physiological state evaluation of irrigated common bean cultivars with contrasting growth habits by learning algorithms using spectral indices

2022; Taylor & Francis; Volume: 37; Issue: 27 Linguagem: Inglês

10.1080/10106049.2022.2096700

1752-0762

Anderson Prates Coelho, Rogério Teixeira de Faria, Leandro Borges Lemos, David Luciano Rosalen, Alexandre Barcellos Dalri,

Genetics and Plant Breeding

AbstractAbstractThis study aimed to analyze and compare the accuracy of models to predict the grain yield (GY) of common bean cultivars with contrasting growth habits using spectral indices. The common bean cultivars used were IAC Imperador and IPR Campos Gerais, which have determinate and indeterminate growth habits, respectively. The plants were grown under five irrigation levels (54, 70, 77, 100, and 132% of the crop evapotranspiration) to generate variability. The normalized difference vegetation (NDVI) and leaf chlorophyll (LCI) indexes were measured at the following phenological stages: V4 (third trifoliate leaf), R5 (pre-flowering), R6 (full flowering), and R8 (grain filling). The spectral indices were used individually for each phenological stage and associated with simple and multiple regressions (SLR and MLR) and artificial neural networks (ANN) to predict GY. Then, stratified models by cultivar and general models were established using data from both cultivars. The accuracy of NDVI-based GY predictions for both models at R6 phenological stage (ANN and SLR average) was acceptable (R2 = 0.64; RMSE = 0.37 Mg ha−1; MBE = −0.14 Mg ha−1) but poor for LCI predictions. The highest accuracies were observed at reproductive phenological stages, mainly R6. The ANNs algorithm did not show superior GY prediction accuracy compared to SLR. NDVI-based remote sensing is feasible to predict and monitor common bean yield potential using cultivar-specific and general models.Keywords: Phaseolus vulgaris L.NDVIportable chlorophyll meterremote sensingartificial neural networks AcknowledgementTo The São Paulo Research Foundation (FAPESP), process number 2018/17363-2.Disclosure statementNo potential conflict of interest was reported by the authors.