Abstract
Given the facilitation of efficient soil data acquisition, light diffraction in both field and laboratory settings allows for applying infrared spectroscopy. This leads to the development of soil spectral library at regional and international levels owing to the extensive interest in the mid-infrared spectroscopy (MIR) domain. Spectroscopy practices meritoriously evaluate various soil constituents such as total nitrogen (TN), organic carbon (OC), potassium (K), and phosphorus (P) within the mid-infrared range, utilizing direct spectral responses and advanced modeling, mostly while analyzing fresh soil samples (undisturbed, wet). Machine and deep learning approaches potentially revolutionize soil spectral data modeling, demonstrating their transformative impact in various fields of study. A novel technique called DrSeq-ANN (dropout sequential artificial neural network), which falls under DL algorithms for predicting soil properties based on raw soil spectra, is proposed and evaluated in this investigation. The National Soil Survey Center-Kellogg Soil Survey Laboratory of the United States Department of Agriculture (USDA) database, comprising nearly 860 topsoil measurements from Kansas State having biological and physicochemical parameters, was employed. DrSeq-ANN outperformed other algorithms when fed to the pre-processed data with the help of techniques such as initial derivative, inverse derivative, logarithmic transformation with a base of 10 (Log10x), and logarithmic derivative. Specifically, while forecasting soil organic carbon, the DrSeq-ANN algorithm achieved R2 value of 0.79 and RMSE value of 0.03 with the logarithmic pre-processed method. With the competencies of the ANN model, DrSeq-ANN proved to be more accurate in prediction. The study confirmed that DrSeq-ANN can be trained in a multi-task setting to forecast the 4 soil factors (TN, OC, P, and K).
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Data Availability
The USDA National Soil Survey Centre-Kellogg Soil Survey Laboratory offers all data necessary to create prediction models on request.
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Acknowledgements
We appreciate the technical assistance provided by Richard Ferguson and Scarlett Bailey at the USDA NRCS National Soil Survey Centre in accessing the spectral library.
Funding
This research is supported through Phase-II of the Visvesvaraya PhD Scheme for Electronics and IT, MeitY (Ministry of Electronics and IT), Government of India (Unique Awardee Number: MEITY-PHD-3080).
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Jakkan, D.A., Ghare, P. & Sakode, C. Multi-parameter Soil Property Prediction Incorporating Mid-infrared Spectroscopy and Dropout Sequential Artificial Neural Network. Water Air Soil Pollut 234, 694 (2023). https://doi.org/10.1007/s11270-023-06726-6
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DOI: https://doi.org/10.1007/s11270-023-06726-6