Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar

Kensuke Kawamura,Naoki Moritsuka,Andry Andriamananjara,Tomohiro Nishigaki,Tantely Razafimbelo,Michel Rabenarivo,Hobimiarantsoa Rakotonindrina,Yasuhiro Tsujimoto

doi:10.3390/rs13081519

Abstract

As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR) spectroscopy is a promising tool for the quantitative estimation of soil properties. However, there remain challenges for predicting soil phosphorus (P) content and availability, which requires a reliable model applicable for different land-use systems to upscale. Recently, a one-dimensional convolutional neural network (1D-CNN) corresponding to the spectral information of soil was developed to considerably improve the accuracy of soil property predictions. The present study investigated the predictive ability of a 1D-CNN model to estimate soil available P (oxalate-extractable P; Pox) content in soils by comparing it with partial least squares (PLS) and random forest (RF) regressions using soil samples (n = 318) collected from natural (forest and non-forest) and cultivated (upland and flooded rice fields) systems in Madagascar. Overall, the 1D-CNN model showed the best predictive accuracy (R2 = 0.878) with a highly accurate prediction ability (ratio of performance to the interquartile range = 2.492). Compared to the PLS model, the RF and 1D-CNN models indicated 4.37% and 23.77% relative improvement in root mean squared error values, respectively. Based on a sensitivity analysis, the important wavebands for predicting soil Pox were associated with iron (Fe) oxide, organic matter (OM), and water absorption, which were previously known wavelength regions for estimating P in soil. These results suggest that 1D-CNN corresponding spectral signatures can be expected to significantly improve the predictive ability for estimating soil available P (Pox) from Vis-NIR spectral data. Rapid and accurate estimation of available P content in soils using our results can be expected to contribute to effective fertilizer management in agriculture and the sustainable management of ecosystems. However, the 1D-CNN model will require a large dataset to extend its applicability to other regions of Madagascar. Thus, further updates should be tested in future studies using larger datasets from a wide range of ecosystems in the tropics.

Highlights

The 1D-convolutional neural networks (CNNs) required a lot of learning time to develop the model, but it showed much better predictive accuracy (R2 = 0.989, root mean squared error (RMSE) = 35.636) than the random forest (RF) (R2 = 0.842, RMSE = 108.820) and partial least squares (PLS) (R2 = 0.827, RMSE = 114.854) models
These results indicated that a comprehensive model to predict Pox in soils with high accuracy could be developed irrespective of land use systems using a deep learning approach with a 1D-CNN model rather than PLS and RF
The present study investigated the performance of a 1D-CNN model through comparison with two conventional methods, PLS and RF, for estimating soil Pox content with

Summary

Introduction

Phosphorus (P) deficiency is a major constraint for crop production in low-input agricultural systems in the tropics [1], and stems from the predominance of strongly weathered soils in which the availability of P is lowered by strong sorption to aluminum (Al) and iron (Fe) (hydr)oxides [2,3]. Even in natural ecosystems, limited soil available. 2021, 13, 1519 biomass production in agricultural and natural ecosystems and developing sustainable land management. Among many extraction methods for evaluating available P, the method using acid ammonium oxalate solution is known to be suited to tropical weathered soils because it can solubilize the active reductant-soluble P, which is the dominant P pool for P cycling in tropical ecosystems [5,6]

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