Drone remote sensing of wheat N using hyperspectral sensor and machine learning

Sahoo, Rabi N.; Rejith, R. G.; Gakhar, Shalini; Ranjan, Rajeev; Meena, Mahesh C.; Dey, Abir; Mukherjee, Joydeep; Dhakar, Rajkumar; Meena, Abhishek; Daas, Anchal; Babu, Subhash; Upadhyay, Pravin K.; Sekhawat, Kapila; Kumar, Sudhir; Kumar, Mahesh; Chinnusamy, Viswanathan; Khanna, Manoj

doi:10.1007/s11119-023-10089-7

Cited by 11 publications

(3 citation statements)

References 83 publications

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“…The increase in mapping and geospatial diagnosis levels through remote sensing techniques varies widely and provides effective support for constant applications in worldwide crop growth research [8,9]. The combination of agronomic data with orbital sensors and UAV data enhances the level of information and improves the reliability of geospatial and field data, aiming to optimize crop management [3].…”

Section: Introductionmentioning

confidence: 99%

Geotechnologies in Biophysical Analysis through the Applicability of the UAV and Sentinel-2A/MSI in Irrigated Area of Common Beans: Accuracy and Spatial Dynamics

de Oliveira,

de Castro,

Sousa

et al. 2024

Remote Sensing

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The applicability of remote sensing enables the prediction of nutritional value, phytosanitary conditions, and productivity of crops in a non-destructive manner, with greater efficiency than conventional techniques. By identifying problems early and providing specific management recommendations in bean cultivation, farmers can reduce crop losses, provide more accurate and adequate diagnoses, and increase the efficiency of agricultural resources. The aim was to analyze the efficiency of vegetation indices using remote sensing techniques from UAV multispectral images and Sentinel-2A/MSI to evaluate the spectral response of common bean (Phaseolus vulgaris L.) cultivation in different phenological stages (V4 = 32 DAS; R5 = 47 DAS; R6 = 60 DAS; R8 = 74 DAS; and R9 = 89 DAS, in 99 days after sowing—DAS) with the application of doses of magnesium (0, 250, 500, and 1000 g ha−1). The field characteristics analyzed were mainly chlorophyll content, productivity, and plant height in an experimental area by central pivot in the midwest region of Brazil. Data from UAV vegetation indices served as variables for the treatments implemented in the field and were statistically correlated with the crop’s biophysical parameters. The spectral response of the bean crop was also detected through spectral indices (NDVI, NDMI_GAO, and NDWI_GAO) from Sentinel-2A/MSI, with spectral resolutions of 10 and 20 m. The quantitative values of NDVI from UAV and Sentinel-2A/MSI were evaluated by multivariate statistical analysis, such as principal components (PC), and cophenetic correlation coefficient (CCC), in the different phenological stages. The NDVI and MCARI vegetation indices stood out for productivity prediction, with r = 0.82 and RMSE of 330 and 329 kg ha−1, respectively. The TGI had the best performance in terms of plant height (r = 0.73 and RMSE = 7.4 cm). The best index for detecting the relative chlorophyll SPAD content was MCARI (r = 0.81; R2 = 0.66 and RMSE = 10.14 SPAD), followed by NDVI (r = 0.81; R2 = 0.65 and RMSE = 10.19 SPAD). The phenological stage with the highest accuracy in estimating productive variables was R9 (Physiological maturation). GNDVI in stages R6 and R9 and VARI in stage R9 were significant at 5% for magnesium doses, with quadratic regression adjustments and a maximum point at 500 g ha−1. Vegetation indices based on multispectral bands of Sentinel-2A/MSI exhibited a spectral dynamic capable of aiding in the management of bean crops throughout their cycle. PCA (PC1 = 48.83% and PC2 = 39.25%) of the satellite multiple regression model from UAV vs. Sentinel-2A/MSI presented a good coefficient of determination (R2 = 0.667) and low RMSE = 0.12. UAV data for the NDVI showed that the Sentinel-2A/MSI samples were more homogeneous, while the UAV samples detected a more heterogeneous quantitative pattern, depending on the development of the crop and the application of doses of magnesium. Results shown denote the potential of using geotechnologies, especially the spectral response of vegetation indices in monitoring common bean crops. Although UAV and Sentinel-2A/MSI technologies are effective in evaluating standards of the common bean crop cycle, more studies are needed to better understand the relationship between field variables and spectral responses.

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Section: Introductionmentioning

confidence: 99%

Geotechnologies in Biophysical Analysis through the Applicability of the UAV and Sentinel-2A/MSI in Irrigated Area of Common Beans: Accuracy and Spatial Dynamics

de Oliveira,

de Castro,

Sousa

et al. 2024

Remote Sensing

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show abstract

“…To overcome these limitations, non-destructive spectral analyses have emerged as a valuable tool for crop monitoring and assessing plant N status without the need for intensive sampling (Verrelst et al, 2015;Elsayed et al, 2018;Prey and Schmidhalter, 2019;Sahoo et al, 2023a). Hyperspectral remote sensing enables timely monitoring and allows for the estimation of chlorophyll content on regional and global scales (Sahoo et al, 2023b). Consequently, remote sensing of canopy reflectance provides an efficient approach for improving the optimization of N application.…”

Section: Introductionmentioning

confidence: 99%

“…To address these limitations, spectral indices have emerged as a valuable solution. These indices have the ability to mitigate the impact of external factors, providing a relatively straightforward and dependable method to extract the N nutritional signal from the intricate reflection patterns of crop canopies (Hatfield et al, 2008;Viña et al, 2011;Inoue et al, 2016;Sahoo et al, 2023b). By utilizing spectral indices, the influence of canopy structure, growth stages, species, and the environment can be minimized, enabling a more accurate assessment of the N status of crops.…”

Section: Introductionmentioning

confidence: 99%

Assessing vegetation indices and productivity across nitrogen gradients: a comparative study under transplanted and direct-seeded rice

Chowdhury,

Khura,

Upadhyay

et al. 2024

Front. Sustain. Food Syst.

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Nitrogen responses vary under diverse agronomic management practices, influencing vegetation indices (VIs) and productivity across different ecological conditions. However, the proper quantification of these responses under various crop establishment methods with varied nitrogen levels is rarely studied. Therefore, a field experiment was conducted to investigate the impact of varying nitrogen levels on VIs, growth parameters, yield attributes, yield, and economic aspects of transplanted rice (TR) and direct-seeded rice (DSR). The experiment was conducted in the randomized block design consisted seven N levels, which included 0% recommended dose of nitrogen (RDN) or no nitrogen (N0), 33.33% RDN (N1), 66.66% RDN (N2), 100% RDN (N3), 133.33% RDN (N4), 166.66% RDN (N5) and 200% RDN (N6), and replicated thrice. The plots with higher N levels demonstrated increased values of VIs and treatment N3 (120 kg N ha−1), N4 (160 kg N ha−1), N5 (200 kg N ha−1), and N6 (240 kg N ha−1) showed no statistically significant differences in NDVI (normalized difference vegetation index), RVI (ratio vegetation index), NDRE (normalized difference red edge), and GNDVI (green normalized difference vegetation index) values across the various growth stages of rice. The application of treatment N4 resulted in the highest number of panicles m−2 (348.2 in TR, 376.8 in DSR), filled grains panicle−1 (74.55 in TR, 62.43 in DSR), and a 1,000-grain weight of 26.92 g in TR and 26.76 g in DSR. The maximum yield (4.89 t ha−1) was obtained in transplanted rice at treatment N4 and, 8.15% yield reduction was noted in DSR for the same treatment, which was statistically equivalent to N3, but significantly superior to other N levels. Conversely, in DSR with RDN (120 kg N ha−1), the cost–benefit ratio surpassed that of TR by 16.96%, signifying DSR’s adaptability for more profitable rice cultivation in the region. This research provides valuable insights into optimizing nitrogen management practices for TR and DSR, thereby enhancing rice crop performance and economic returns.

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3D-listless block cube set-partitioning coding for resource constraint hyperspectral image sensors

Bajpai

2024

SIViP

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Drone remote sensing of wheat N using hyperspectral sensor and machine learning

Cited by 11 publications

References 83 publications

Geotechnologies in Biophysical Analysis through the Applicability of the UAV and Sentinel-2A/MSI in Irrigated Area of Common Beans: Accuracy and Spatial Dynamics

Geotechnologies in Biophysical Analysis through the Applicability of the UAV and Sentinel-2A/MSI in Irrigated Area of Common Beans: Accuracy and Spatial Dynamics

Assessing vegetation indices and productivity across nitrogen gradients: a comparative study under transplanted and direct-seeded rice

3D-listless block cube set-partitioning coding for resource constraint hyperspectral image sensors

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