article · Agronomy
Assessing crop growth and quality non-destructively helps optimise fertiliser application in precision agriculture. This research evaluated the use of gradient boosting regression models alongside published and novel two- and three-band spectral indices to estimate sugar beet performance across five nitrogen fertiliser levels. The measured traits included total chlorophyll, chlorophyll a and b, root yield, sugar yield, and sugar content. Higher nitrogen levels led to increases in chlorophyll and yields, but showed a strong negative relationship with sugar content. Several newly developed three-band spectral indices using visible, near-infrared, and red-edge wavelengths showed moderate to strong correlations with all parameters. Combining these spectral indices with gradient boosting regression models provided a reliable method to predict both production and quality traits under semiarid conditions.
Balancing fertiliser application is critical for maximising crop yield while preserving quality and minimising environmental harm. By demonstrating that optical sensors and machine learning can rapidly gauge sugar beet status and quality in semiarid environments, this approach supports the broader adoption of non-destructive monitoring tools in precision agriculture, helping growers apply nitrogen more effectively.
The method could be used by precision agriculture software providers, agronomists, and sensor developers to build non-destructive monitoring tools for sugar beet growers in semiarid regions. Because the machine learning models and spectral indices have been developed and tested on field data, the technology represents applied research that could be integrated into existing crop scouting or spectral imaging workflows.
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Accurately estimating crop performance under various fertilizer levels in a rapid and non-destructive manner has become a vital aspect of precision agriculture technology for both economic and environmental benefits. This study aimed to estimate different sugar beet parameters, such as total chlorophyll (Chlt), chlorophyll a (Chla), chlorophyll b (Chlb), root yield (RY), sugar yield (SY), and sugar content (SC) under five nitrogen (N) levels (0, 30, 60, 90, and 120 kg N ha−1). This was achieved by using a combination of the gradient boosting regression (GBR) model with published and newly developed two- and three-band spectral indices (2D- and 3D-SRIs). The results showed that the N levels had the highest proportion of variations (80.4–92.9%) for all parameters, except for SC, which had more variation (59.9%) according to year than the N levels (37.2%). All parameters, except SC, showed a significant increase with gradually increasing N levels. Additionally, the N levels displayed linear and strong positive relationships with the chlorophyll parameters, and linear and strong negative relationships with SC, while these relationships were quadratic and strong with RY and SY. Several published and novel 3D-SRIs exhibited moderate to strong relationships (R2 = 0.65–0.89) with all parameters. The newly developed 3D-SRIs, which involve wavelengths from the visible, near-infrared, and red-edge regions, such as NDI536, 538, 534, NDI738, 750, 542, and NDI448, 734, 398, were effective in accurately estimating all parameters. Combining 2D-SRIs, 3D-SRIs, and the aggregate of all spectral indices (ASRIs) with GBR models could be a robust strategy for estimating the six observed parameters with reasonable precision. The GBR-ASF-6 SRIs and the GBR-ASF-7 SRIs models performed better in predicting Chl content and SC with R2 values of 0.99 and 0.99 (RMSE = 0.073 and 1.568) for the training dataset and R2 values of 0.65 and 0.78 (RMSE = 0.354 and 6.294) for the testing datasets, respectively. The obtained results concluded that published and newly developed 3D-SRIs, GBR based on 2D-SRIs or 3D-SRIs, and the aggregate of all ASRIs can be used in practice to accurately estimate the Chl content, production, and quality of sugar beet across a wide range of N levels under semiarid conditions.
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DOI: 10.3390/agronomy13112743
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