Soil texture prediction through stratification of a regional soil spectral library

Pedosphere ◽  
2022 ◽  
Vol 32 (2) ◽  
pp. 294-306
José Janderson Ferreira COSTA ◽  
Élvio GIASSON ◽  
Elisângela Benedet DA SILVA ◽  
Tales TIECHER ◽  
Antonny Francisco Sampaio DE SENA ◽  
2021 ◽  
Liang Zhong ◽  
Xi Guo ◽  
Zhe Xu ◽  
Meng Ding

<p>Soil, as a non-renewable resource, should be monitored continuously to prevent its degradation and promote sustainable agricultural management. Soil spectroscopy in the visible-near infrared range is a fast and cost-effective analytical technique to predict soil properties. The use of large soil spectral libraries can reduce the work needed to reliably estimate soil properties and obtain robust models capable of widespread applicability. Deep learning is apt for big data analysis, and this approach could herald a profound change in the way we model soil spectral data generally. Accordingly, we explored the modeling potential of deep convolutional neural networks (DCNNs) for soil properties based on a large soil spectral library. The European topsoil dataset provided by the Land Use/Cover Area frame Survey (LUCAS) was used without any pre-processing of spectra or covariates added. Two 16-layer DCNN models (ResNet-16 and VGGNet-16) were successfully used to make regression predictions of seven soil properties and classification predictions of soil texture into four groups and 12 levels. Our results showed that the ResNet-16 and VGGNet-16 models produced highly accurate predictions for most soil properties, being superior to either a shallow convolutional neural network and traditional machine learning approaches. Soil organic carbon content, nitrogen content, cation exchange capacity, pH, and calcium carbonate content were well predicted, having a ratio of performance to deviation (RPD) > 2.0. Soil potassium content was adequately predicted (1.4 ≤ RPD ≤ 2.0) and phosphorous content was poorly predicted (RPD < 1.4). The overall classification accuracy of soil texture was 0.749 (four groups) and 0.566 (12 levels). The position of feature wavelengths differed among the soil properties, for which multiple characteristic peaks were common. This study fully demonstrates the modeling potential of deep learning with soil hyperspectral data, which could bring us closer to achieving precision agriculture.</p>

2015 ◽  
Vol 2 (2) ◽  
pp. 148-158

Spodosol soil of Typic Placorthod sub-group of East Barito District is one of the problem soils with the presence of hardpan layer, low fertility, low water holding capacity, acid reaction and it is not suitable for oil palm cultivation without any properly specific management of land preparation and implemented best agronomic practices. A study was carried out to evaluate the soil characteristic of a big hole (A profile) and no big hole (B profile) system and comparative oil palm productivity among two planting systems. This study was conducted in Spodosol soil at oil palm plantation (coordinate X = 0281843 and Y = 9764116), East Barito District, Central Kalimantan Province on February 2014, by surveying of placic and ortstein depth and observing soil texture and chemical properties of 2 (two) oil palm's soil profiles that have been planted in five years. Big hole system of commercial oil palm field planting on the Spodosol soil area was designed for the specific purpose of minimizing the potential of a negative effect of shallow effective planting depth for oil palms growing due to the hardpan layer (placic and ortstein) presence as deep as 0.25 - 0.50 m. The big hole system is a planting hole type which was vertical-sided with 2.00 m x 1.50 m on top and bottom side and 3.00 m depth meanwhile the 2:1 drain was vertical-sided also with 1.50 m depth and 300 m length. Oil palm production was recorded from the year 2012 up to 2014. Results indicated that the fractions both big hole profile (A profile) and no big hole profile (B profile) were dominated by sands ranged from 60% to 92% and the highest sands content of non-big hole soil profile were found in A and E horizons (92%). Better distribution of sand and clay fractions content in between layers of big hole soil profiles of A profile sample is more uniform compared to the B profile sample. The mechanical holing and material mixing of soil materials of A soil profile among the upper and lower horizons i.e. A, E, B and C horizons before planting that resulted a better distribution of both soil texture (sands and clay) and chemical properties such as acidity value (pH), C-organic, N, C/N ratio, CEC, P-available and Exchangeable Bases. Investigation showed that exchangeable cations (Ca, Mg, K), were very low in soil layers (A profile) and horizons (B profile) investigated. The low exchangeable cations due to highly leached of bases to the lower layers and horizons. Besides, the palm which was planted on the big hole system showed good adaptation and response positively by growing well of tertiary and quaternary roots that the roots were penetrable into deeper rooting zone as much as >1.00 m depth. The roots can grow well and penetrate much deeper in A profile compared to the undisturbed hardpan layer (B profile). The FFB (fresh fruit bunches) production of the non-big hole block was higher than the big hole block for the first three years of production. This might be due to the high variation of monthly rainfall in-between years of observation from 2009 to 2014. Therefore, the hardness of placic and ortstein as unpenetrable agents by roots and water to prevent water loss and retain the water in the rhizosphere especially in the drier weather. In the high rainfall condition, the 2:1 drain to prevent water saturation in the oil palm rhizosphere by moving some water into the drain. Meanwhile, the disturbed soil horizon (big hole area) was drier than un disturbance immediately due to water removal to deeper layers. We concluded that both big hole and 2:1 drain are a suitable technology for Spodosol soil land especially in preparing palms planting to minimize the negative effect of the hardpan layer for oil palm growth.

2016 ◽  
Vol 5 (1) ◽  
pp. 1787-1794
Ramdas D. Gore ◽  
Reena H. Chaudhari ◽  
Bharti W. Gawali ◽  

Data Series ◽  
10.3133/ds231 ◽  
2007 ◽  
Roger N. Clark ◽  
Gregg A. Swayze ◽  
Richard A. Wise ◽  
K. Eric Livo ◽  
Todd M. Hoefen ◽  

2014 ◽  
Vol 22 (2) ◽  
pp. 217-224
Houlong JIANG ◽  
Shuduan LIU ◽  
Anding XU ◽  

2017 ◽  
Vol 68 (11) ◽  
pp. 2704-2707
Delia Nica Badea ◽  
Codrina Levai

The paper evaluates the presence of methyl xanthine compounds: caffeine, theophylline, theobromine used as ingredients in carbonated soft drinks or as color and flavor ingredients in alcoholic beverages. The active components extracted from the selected products (coffee, tea, drinks) was separated and identified chromatographically using plates with silica nano -Sil NH2 / UV-254, mobile phase ethanol - water (50: 1, 50: 3, 50: 5; 50: 7; v / v) and 60 F254 plates, mobile phase acetone-toluene-chloroform (40:30:30 v / v). Separated caffeine and identified by TLC was analyzed using a HelWet Packard 5890 Gas Chromatograph equipped with MS 5972 mass detector and spectral library to confirm identification. This simple and rapid TLC, GC / MS instrumental method is useful in controlling traces of methyl xanthine compounds in food as a food safety useful in controlling traces compound of food products containing methylxanthines as a food safety measure.

2007 ◽  
Vol 7 (5) ◽  
pp. 655-667 ◽  
Henry Lam ◽  
Eric W. Deutsch ◽  
James S. Eddes ◽  
Jimmy K. Eng ◽  
Nichole King ◽  

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