Optimization of soil background removal to improve the prediction of wheat traits with UAV imagery

Almeida-Ñauñay, Andrés F.; Tarquis, A. M.; López-Herrera, Juan; Pérez-Martín, Enrique; Pancorbo, J.L.; Raya-Sereno, María D.; Quemada, Miguel

doi:10.1016/j.compag.2022.107559

Cited by 8 publications

(3 citation statements)

References 75 publications

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“…This problem has apparently not been observed in the early stages (NDSI and MSAVI index data). Although, research data (Almeida-Ñauñay et al, 2023) indicate that NDVI, MSAVI and NDRE indices were able to predict signs of yield problems in wheat crops. Studies conducted on corn for silage (Herbei et al, 2022) also confirmed the possibility of using the MSAVI index for yield prediction.…”

Section: Figure 2 Ndsi Soil Salinity Index Maps (March 2021) Msavi Ad...mentioning

confidence: 90%

Comparison of NDVI, NDRE, MSAVI and NDSI Indices for Early Diagnosis of Crop Problems

Voitik

Кравченко

Pushka

et al. 2023

Agricultural Engineering

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In precision agriculture, it is possible to use satellite monitoring of fields. Satellite monitoring systems allow you to get free images with a resolution of up to 10 m per pixel, which is sufficient to determine the state of vegetation of plants on such indicators as the normalized vegetation index NDVI. However, the NDVI indicator already indicates the existing problems of correction which will not help to restore the lost yield of crops, but only helps to prevent further losses. Using the NDSI soil salinity index, it is possible to determine the difference in its properties from spectral images. Also, you can study the vegetation of plants in the early stages of their development, in fact immediately after germination. Soil-adjusted vegetation index, such as MSAVI, is used for this purpose. Studies indicate the possibility of using NDSI and MSAVI indicators for early diagnosis of confirmed crops NDVI and NDRE (indicating chlorophyll activity in plants) at later stages of their development. Studies conducted on soybean, spring barley and maize crops sown in the spring of 2021 indicate a correspondence between raster field maps show-ing the above indices made from March to July. Statistical analysis of raster images of field maps using specialized software showed a correlation between NDSI and MSAVI in March and May, respectively, with NDVI and NDRE indexes in June and July. Therefore, it is possible to judge the expediency of using NDSI and MSAVI indicators for early diagnosis of possible problems with plant vegetation, as well as for the creation of maps of differential fertilization.

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Section: Figure 2 Ndsi Soil Salinity Index Maps (March 2021) Msavi Ad...mentioning

confidence: 90%

Comparison of NDVI, NDRE, MSAVI and NDSI Indices for Early Diagnosis of Crop Problems

Voitik

Кравченко

Pushka

et al. 2023

Agricultural Engineering

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“…Several studies have been conducted on winter wheat, using both linear and non-linear equations to estimate expected yields [63][64][65][66]. In both sensors (Table 4), the linear and exponential equations differed significantly from the quadratic equation across different treatments.…”

Section: Using Yield Prediction Equationsmentioning

confidence: 99%

“…By comparison and equation modification, the linear equation achieved the most reliable results, so this equation was chosen over exponential and quadratic. Several researchers have examined the relationship between wheat yield and NDVI using linear regression [4,40,64], while Raun et al [66] opted for an exponential equation. As discussed earlier, the linear model achieved the most reliable results.…”

Section: Using Yield Prediction Equationsmentioning

confidence: 99%

Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

Zsebő,

Bede,

Kukorelli

et al. 2024

Drones

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This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving a yield prediction model with cumulative growing degree days (CGDD) and days from sowing (DFS) data. The study area was located in Mosonmagyaróvár, Hungary. A small-scale field trial in winter wheat was constructed as a randomized block design including Environmental: N-135.3, P2O5-77.5, K2O-0; Balance: N-135.1, P2O5-91, K2O-0; Genezis: N-135, P2O5-75, K2O-45; and Control: N, P, K 0 kg/ha. The crop growth was monitored every second week between April and June 2022 and 2023, respectively. NDVI measurements recorded by GreenSeeker were taken at three pre-defined GPS points for each plot; NDVI values based on the MicaSense camera Red and NIR bands were calculated for the same points. Results showed a significant difference (p ≤ 0.05) between the Control and treated areas by GreenSeeker measurements and Micasense-based calculated NDVI values throughout the growing season, except for the heading stage. At the heading stage, significant differences could be measured by GreenSeeker. However, remotely sensed images did not show significant differences between the treated and Control parcels. Nevertheless, both sensors were found suitable for yield prediction, and 226 DAS was the most appropriate date for predicting winter wheat’s yield in treated plots based on NDVI values and meteorological data.

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Enhancing precision of root-zone soil moisture content prediction in a kiwifruit orchard using UAV multi-spectral image features and ensemble learning

Zhu,

Cui,

Guo

et al. 2024

Computers and Electronics in Agriculture

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Optimization of soil background removal to improve the prediction of wheat traits with UAV imagery

Cited by 8 publications

References 75 publications

Comparison of NDVI, NDRE, MSAVI and NDSI Indices for Early Diagnosis of Crop Problems

Comparison of NDVI, NDRE, MSAVI and NDSI Indices for Early Diagnosis of Crop Problems

Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

Enhancing precision of root-zone soil moisture content prediction in a kiwifruit orchard using UAV multi-spectral image features and ensemble learning

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