Robust Vis-NIRS models for rapid assessment of soil organic carbon and nitrogen in Feralsols Haplic soils from different tillage management practices

Sithole, Nkanyiso J.; Ncama, Khayelihle; Magwaza, Lembe Samukelo

doi:10.1016/j.compag.2018.08.036

Cited by 29 publications

(12 citation statements)

References 26 publications

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“…In a similar manner, CNN is a neural network that includes one or more convolutional layers in its architecture, and has the ability to exploit local correlations to make them more attractive than the fully-connected neural networks [54]. Despite the superiority shown in these techniques, there is a lack of consistency in the results; hence, PLSR is still utilized, as it provides sufficient accuracy [45]. Variations in the study areas and in the selection of calibration data and their size, numerous combinations of preprocessing, and multivariate methods are some of the reasons that no calibration method has achieved universal acceptance.…”

Section: Discussionmentioning

confidence: 99%

“…For robust VNIR-SWIR soil spectral measurements, it is important to construct a database that is representative of soil variability for each studied area [44]. For the generation of the required data variability, Sithole et al [45] acquired samples from various depths and different tillage systems that produced a robust PLSR predictive model for SOC with R 2 = 0.993, RMSEP = 0.157, and RPD = 2.55, i.e., with very high accuracy in terms of its ability to predict SOC. The modified PLSR was utilized by Heinze et al [46], as it was considered more accurate than PLSR.…”

Section: Multivariate Calibrationsmentioning

confidence: 99%

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From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

et al. 2020

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Rapid and cost-effective soil properties estimations are considered imperative for the monitoring and recording of agricultural soil condition for the implementation of site-specific management practices. Conventional laboratory measurements are costly and time-consuming, and, therefore, cannot be considered appropriate for large datasets. This article reviews laboratory and proximal sensing spectroscopy in the visible and near infrared (VNIR)–short wave infrared (SWIR) wavelength region for soil organic carbon and soil organic matter estimation as an alternative to analytical chemistry measurements. The aim of this work is to report the progress made in the last decade on data preprocessing, calibration approaches, and system configurations used for VNIR-SWIR spectroscopy of soil organic carbon and soil organic matter estimation. We present and compare the results of over fifty selective studies and discuss the factors that affect the accuracy of spectroscopic measurements for both laboratory and in situ applications.

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Section: Discussionmentioning

confidence: 99%

Section: Multivariate Calibrationsmentioning

confidence: 99%

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

et al. 2020

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“…Furthermore, a soil sensor-driven PCA model developed by Sithole et al [112] can detect soil organic nitrogen after different stages of N-fertilizer treatments. Unwanted foreign objects in the wheat field, such as barley, maize, deer, and rabbit dropping, are identified by a smart sensor system developed by Ravikanth et al [113] The system uses NIR measurements captured by hyperspectral image camera to classify the foreign object found in the wheat farm.…”

Section: Precision Agriculture and Food Sciencementioning

confidence: 99%

Machine Learning‐Enabled Smart Sensor Systems

Ren

et al. 2020

Advanced Intelligent Systems

109

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Recent advancements and major breakthroughs in machine learning (ML) technologies in the past decade have made it possible to collect, analyze, and interpret an unprecedented amount of sensory information. A new era for “smart” sensor systems is emerging that changes the way that conventional sensor systems are used to understand the world. Smart sensor systems have taken advantage of classic and emerging ML algorithms and modern computer hardware to create sophisticated “smart” models that are tailored specifically for sensing applications and fusing diverse sensing modalities to gain a more holistic appreciation of the system being monitored. Herein, a review of the recent sensing applications, which harness ML enabled smart sensor systems, is presented. First well‐known ML algorithms implemented in smart sensor systems for practical sensing applications are discussed. Subsequent sections summarize the practical sensing applications under two major categories: physical and chemical sensing and visual imaging sensing describing how the sensor technologies are coupled with ML “smart” models and how these systems achieve practical benefits. Finally, an outlook on the current trajectory and challenges that will be faced by future smart sensing systems and the opportunities that may be unlocked is provided.

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“…Currently, research in soils has contemplated its classifications and analysis, especially the determination of nitrogen and total organic carbon. [22][23][24] However, the spectra obtained in the NIR region is not always easy to understand, as the bands indicate vibrational modes of chemical bonds of the molecules that overlap, creating combination bands and overtones, revealing information about the biochemical composition of the sample. 23,25 In soil samples, which are more complex, the main constituents that influence spectral behavior are organic matter, clay minerals and iron oxides, in addition to particle size distribution and moisture.…”

Section: Introductionmentioning

confidence: 99%

“…NIR spectroscopy allows us to quickly evaluate soil samples whose chemical composition is modified by different management practices and land use. 18,22,23,34 Therefore, our hypothesis is that these differences in soil constituents can be identified by applying NIR spectroscopy and chemometrics analysis, and this will allow us to discriminate soils according to the cultivation system only from their spectral response. The aim of this study was to conduct discrimination and classification analyses on soils managed with different sources of fertilization and plant species grown in organic and conventional farming systems.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of soils managed with different sources of fertilization and plant species in organic and conventional farming through near‐infrared spectroscopy and chemometrics

et al. 2021

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BACKGROUND: The combination of near-infrared (NIR) spectroscopy and chemometrics can be used to group or discriminate soils based on spectral response. In this study, we conducted discrimination and classification analyses on soils managed with different sources of fertilization and plant species grown in organic and conventional farming systems. RESULTS: Principal component analysis explained 96% (PC1) and 3% (PC2) of the data variability and separated the soil samples of organic and conventional management systems. The wavenumbers that contributed most to the separation of the management systems were in the range of 3600 and 7300 cm −1 , especially the absorption peaks of 3700 and 4600 cm −1 (characteristic of C H and N H combinations), and 5200 and 7000 cm −1 (typical of O H combinations). Machine learning analysis using k-nearest neighbor and random forest algorithms was efficient in classifying soil samples according to management system with an accuracy of 97.8% and can therefore be used for future classification studies.CONCLUSION: Based on the results, we strongly recommend the use of NIR spectroscopy associated with chemometrics for discriminating soils grown with Malus domestica, Musa spp., Oryza sativa and Solanum tuberosum L. under organic and conventional management systems through spectral response.

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Robust Vis-NIRS models for rapid assessment of soil organic carbon and nitrogen in Feralsols Haplic soils from different tillage management practices

Cited by 29 publications

References 26 publications

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

Machine Learning‐Enabled Smart Sensor Systems

Discrimination of soils managed with different sources of fertilization and plant species in organic and conventional farming through near‐infrared spectroscopy and chemometrics

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