Discrimination of Tomato Plants (Solanum lycopersicum) Grown under Anaerobic Baffled Reactor Effluent, Nitrified Urine Concentrates and Commercial Hydroponic Fertilizer Regimes Using Simulated Sensor Spectral Settings

Mbulisi Sibanda,Asanda Mditshwa,Onisimo Mutanga,Lembe S Magwaza,Alfred O Odindo,Timothy Dube,Paramu L Mafongoya,Shirly T Magwaza

doi:10.3390/agronomy9070373

Mbulisi Sibanda, Asanda Mditshwa + Show 6 more

Open Access

https://doi.org/10.3390/agronomy9070373

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Abstract

We assess the discriminative strength of three different satellite spectral settings (HyspIRI, the forthcoming Landsat 9 and Sentinel 2-MSI), in mapping tomato (Solanum lycopersicum Linnaeus) plants grown under hydroponic system, using human-excreta derived materials (HEDM), namely, anaerobic baffled reactor (ABR) effluent and nitrified urine concentrate (NUC) and commercial hydroponic fertilizer mix (CHFM) as main sources of nutrients. Simulated spectral settings of HyspIRI, Landsat 9 and Sentinel 2-MSI were resampled from spectrometric proximally sensed data. Discriminant analysis (DA) was applied in discriminating tomatoes grown under these different nutrient sources. Results showed that the simulated spectral settings of HyspIRI sensor better discriminate tomatoes grown under different fertilizer regimes when compared to Landsat 9 OLI and Sentinel-2 MSI spectral configurations. Using the DA algorithm, HyspIRI exhibited high overall accuracy (OA) of 0.99 and a kappa statistic of 0.99 whereas Landsat OLI and Sentinel-2 MSI exhibited OA of 0.94 and 0.95 and 0.79 and 0.85 kappa statistics, respectively. Simulated HyspIRI wavebands 710, 720, 690, 840, 1370 and 2110 nm, Sentinel 2-MSI bands 7 (783 nm), 6 (740 nm), 5 (705 nm) and 8a (865 nm) as well as Landsat bands 5 (865 nm), 6 (1610 nm), 7 (2200 nm) and 8 (590 nm), in order of importance, were selected as the most suitable bands for discriminating tomatoes grown under different fertilizer regimes. Overall, the performance of simulated HyspIRI, Landsat 9 OLI-2 and Sentinel-2 MSI spectral bands seem to bring new opportunities for crop monitoring.

Highlights

Food insecurity is a large and growing challenge in sub-Saharan Africa [1,2]
Considering the fact that literature states that hyperspectral data is generally characterized by high collinearity and that there is no specific algorithm that is suitable for discriminating vegetation characteristics at different places and times, this study examined the performance of discriminant analysis (DA) and partial least squares discriminant analysis (PLS-Discriminant analysis (DA)) in discriminating tomato plants grown under different fertilizer regimes
Analysis of variance tests results showed significant differences (α = 0.05) between tomato plants treated with different fertilizer combinations based on the spectral settings of Hyperspectral Infrared Imager (HyspIRI), Sentinel-2MSI

Summary

Introduction

Food insecurity is a large and growing challenge in sub-Saharan Africa [1,2]. It is estimated that at least one out of four people are hungry and undernourished in sub-Saharan Africa. Bank estimates that in 2030, nearly 9 in 10 extremely poor people will be living in Sub-Saharan. This is exacerbated by droughts and soil nutrients deficiencies resulting from limited. Agronomy 2019, 9, 373 fertilizer applications [1,2]. This is in turn associated with high fertilizer and food prices, amongst other factors. According to FAO [4] the annual food inflation increased from 5% in 2014 to 6%

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