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Al-Gaadi, Khalid A.
- Prediction of Potato High-Yield Zones of a Field:Bivariate Frequency Ratio Technique
Abstract Views :190 |
PDF Views:67
Authors
Khalid A. Al-Gaadi
1,
Abdalhaleem A. Hassaballa
1,
Rangaswamy Madugundu
1,
El-Kamil Tola
1,
Ronnel B. Fulleros
2
Affiliations
1 Precision Agriculture Research Chair, King Saud University, Riyadh, SA
2 Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh, SA
1 Precision Agriculture Research Chair, King Saud University, Riyadh, SA
2 Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh, SA
Source
Current Science, Vol 119, No 6 (2020), Pagination: 992-1000Abstract
Bivariate frequency ratio (BFR) technique was employed to determine high-yield zones in a 30 ha potato (Solanum tuberosum L.) field located in Wadi-Ad-Dawasir, Saudi Arabia. BFR was performed by inputting selected yield tendency factors (YTFs) and potato actual yield (YA). The YTFs were NDVI-derived from Sentinel-2 images, soil electrical conductivity, nitrogen, pH and texture. The obtained yield tendency map (YP) was assessed against (YA) using the area under the curve metric. Although low accuracy (41-58 %) was observed with the individual YTFs, high-yield zones were determined with an accuracy of 90% using the cumulative response of YTFs.Keywords
Bivariate Frequency Ratio, Potato Field, Soil Parameters, Yield Prediction.Full Text
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- Sentinel-2 Images for Effective Mapping of Soil Salinity in Agricultural Fields
Abstract Views :223 |
PDF Views:73
Authors
Affiliations
1 Precision Agriculture Research Chair, Deanship of Scientific Research, King Saud University, Riyadh 11451, SA
2 Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, SA
1 Precision Agriculture Research Chair, Deanship of Scientific Research, King Saud University, Riyadh 11451, SA
2 Department of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, SA
Source
Current Science, Vol 121, No 3 (2021), Pagination: 384-390Abstract
Salinity is a critical feature for the management of agricultural soil, particularly in arid and semi-arid areas. The present study was conducted to develop an effective soil salinity prediction model using Sentinel-2A (S2) satellite data. Initially, the collected soil samples were analysed for soil salinity (ECe). Subsequently, multiple linear regression analysis was carried out between the obtained ECe values and S2 data, for the prediction of soil salinity models. The relationship between ECe and S2 data, including individual bands, band ratios and spectral indices showed moderate to highly significant correlations (R2 = 0.43–0.83). A combination of SWIR-1 band and the simplified brightness index was found to be the most appropriate (R2 = 0.65; P < 0.001) for prediction of soil salinity. The results of this study demonstrate the ability to obtain reliable estimates of EC using S2 data.Keywords
Agricultural Lands, Multiple Linear Regression, Satellite Data Simplified Brightness Index, Soil Salinity.Full Text
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