Soil analysis using visible and near infrared spectroscopy

Visible-near infrared diffuse reflectance (vis-NIR) spectroscopy is a fast, non-destructive technique well suited for analyses of some of the essential constituents of the soil. These constituents, mainly clay minerals, organic matter and soil water strongly affect conditions for plant growth and influence plant nutrition. Here we describe the process by which vis–NIR spectroscopy can be used to collect soil spectra in the laboratory. Because it is an indirect technique, the succeeding model calibrations and validations that are necessary to obtain reliable predictions about the soil properties of interest, are also described in the chapter

Visible and near infrared spectroscopy in soil science

This chapter provides a review on the state of soil visible–near infrared (vis–NIR) spectroscopy. Our intention is for the review to serve as a source of up-to date information on the past and current role of vis–NIR spectroscopy in soil science. It should also provide critical discussion on issues surrounding the use of vis–NIR for soil analysis and on future directions. To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations. A review of the past and current role of vis–NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals. We then discuss the performance and generalization capacity of vis–NIR calibrations, with particular attention on sample pre-tratments, co-variations in data sets, and mathematical data preprocessing. Field analyses and strategies for the practical use of vis–NIR are considered. We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content. Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned. For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function. To do this, research in soil spectroscopy needs to be more collaborative and strategic. The development of the Global Soil Spectral Library might be a step in the right direction

Estimating Macronutrient Content of Paddy Soil Based on Near-Infrared Spectroscopy Technology Using Multiple Linear Regression

This study investigates the feasibility of employing near-infrared (NIR) spectroscopy with multiple linear regression (MLR) to estimate macronutrients in paddy soil compared with partial least squares (PLS) and principal component regression (PCR). Seventy-nine soil samples from West Java Province, Indonesia, are subject to conventional nutrient analysis and NIR spectroscopy (1000-2500 nm). The reflectance data undergoes various pretreatment techniques, and MLR models are calibrated using the forward method to achieve correlations exceeding 0.90. The best model calibrations are selected based on high correlation coefficients, determination coefficients, RPD, and low RMSE values. Meanwhile, the comparison of performance MLR is made with the PLS and PCR models. Results indicate that simple MLR models perform less than PLS for all nutrients, better than PCR for nitrogen, and below PCR for phosphorus and potassium. However, MLR reliably estimates soil nitrogen, phosphorus, and potassium content with ratio of performance to deviation (RPD) exceeding 2.0. This study demonstrates the potential of MLR for precise macronutrient estimation in paddy soil

Naive Bayes Method Monitoring Macro Nutrition and Soil Moisture Using Naive Bayes Method based on Internet of Things

The different land quality causes farmers not to know the exact quality of their agricultural land. Improper processing of agricultural land can result in a decrease in the quality of a land. The content of soil macronutrients consists of nitrogen (N), phospor (P), and potassium (K), these contents that usually affect plant growth. The development of an optical transducer for which is used to measure the wavelength of an object in everyday life can use led sensors as a light source and photo diodes as light detection using the TCRT5000 sensor as an infrared wave transmitter module. The use of the internet of things at this time is very useful to facilitate monitoring. The use of naïve bayes to determine the resulting probability. the soil moisture content obtained averaged 20.63% and nitrogen content values with an average of 590, Phospor 513 and Potassium 670. The sending of data to ThingSpeak can be determined as desired. It is hoped that this research can be developed by refining the use of sensors and methods used to make it easier to apply in life

Identify the opportunities provided by developments in earth observation and remote sensing for national scale monitoring of soil quality

Defra wish to establish to what extent national-scale soil monitoring (both state and change) of a series of soil indicators might be undertaken by the application of remote sensing methods. Current soil monitoring activities rely on the field-based collection and laboratory analysis of soil samples from across the landscape according to different sampling designs. The use of remote sensing offers the potential to encompass a larger proportion of the landscape, but the signal detected by the remote sensor has to be converted into a meaningful soil measurement which may have considerable uncertainty associated with it. The eleven soil indicators which were considered in this report are pH, organic carbon, bulk density, phosphorus (Olsen P), nitrogen (total N), magnesium (extractable), potassium (extractable), copper (aqua regia extractable), cadmium (aqua regia extractable), zinc (aqua regia extractable) and nickel (aqua regia extractable). However, we also comment on the potential use of remote sensing for monitoring of soil depth and (in particular) peat depth, plus soil erosion and compaction. In assessing the potential of remote sensing methods for soil monitoring of state and change, we addressed the following questions: 1. When will these be ready for use and what level of further development is required? 2. Could remote sensing of any of these indicators replace and/or complement traditional field based national scale soil monitoring? 3. Can meaningful measures of change be derived? 4. How could remote soil monitoring of individual indicators be incorporated into national scale soil monitoring schemes? To address these questions, we undertook a comprehensive literature and internet search and also wrote to a range of international experts in remote sensing. It is important to note that the monitoring of the status of soil indicators, and the monitoring of their change, are two quite different challenges; they are different variables and their variability is likely to differ. There are particular challenges to the application of remote sensing of soil in northern temperate regions (such as England and Wales), including the presence of year-round vegetation cover which means that soil spectral reflectance cannot be captured by airborne or satellite observations, and long-periods of cloud cover which limits the application of satellite-based spectroscopy. We summarise the potential for each of the indicators, grouped where appropriate. Unless otherwise stated, the remote sensing methods would need to be combined with ground-based sampling and analysis to make a contribution to detection of state or change in soil indicators. Soil metals (copper (Cu), cadmium (Cd), zinc (Zn), nickel (Ni)): there is no technical basis for applying current remote sensing approaches to monitor either state or change of these indicators and there are no published studies which have shown how this might be achieved. Soil nutrients: the most promising remote sensing technique to improve estimates of the status of extractable potassium (K) is the collection and application of airborne radiometric survey (detection of gamma radiation by low-flying aircraft) but this should be investigated further. This is unlikely to assist in monitoring change. Based on published literature, it may be possible to enhance mapping the state of extractable magnesium (Mg), but not to monitor change, using hyperspectral (satellite or airborne) remote sensing in cultivated areas. This needs to be investigated further. There are no current remote sensing methods for detecting state or change of Olsen (extractable) phosphorus (P). Organic carbon and total nitrogen: Based on published literature, it may be possible to enhance mapping the state of organic carbon and total nitrogen (but not to monitor change), using hyperspectral (satellite or airborne) remote sensing in cultivated areas only. In applying this approach the satellite data are applied using a statistical model which is trained using ground-based sampling and analysis of soil

Towards a methodical framework for comprehensively assessing forest multifunctionality

Funded by Deutsche Forschungsgemeinschaft. Grant Number: DFG FOR 891/1-3 National Natural Science Foundation of China. Grant Numbers: 30710103907, 30930005, 31170457, 31210103910 Swiss National Science Foundation (SNSF) Sino-German Centre for Research Promotion in Beijing. Grant Number: GZ 986Peer reviewedPublisher PD

Spectroscopic Sensor Data Fusion to Improve the Prediction of Soil Nutrient Contents

This study aims to advance the understanding and application of spectroscopic sensor data fusion for improving soil nutrient content predictions. In addition to presenting an extensive review of studies on the spectroscopic sensor data fusion, a research investigation was conducted to assess the effectiveness of five fusion algorithms in predicting three primary nutrients (Nitrogen, Phosphorous, and Potassium) and two secondary nutrients (Calcium and Magnesium) in soil using Visible and Near-Infrared, Mid-Infrared, and X-ray Fluorescence data. Among the five fusion algorithms, one was a low-level fusion involving data concatenation. Two were mid-level fusions, incorporating feature extraction by applying (i) Principal Component reduction and (ii) Partial Least Squares reduction. The other two were high-level fusions, namely (i) Simple Averaging and (ii) Granger Ramanathan Averaging. The results indicate that Low-Level Fusion may not be suitable for inherently incompatible data. Mid-level fusion improved the R² by 0.1-18%, RMSE by 0-8%, and RPIQ by 0-11.5%, while high-level fusion enhanced the R² by 0-12.5%, RMSE by 0-12%, and RPIQ by 2.3-13.4%, depending on the nutrients and fusion algorithms. Despite these improvements, predictions were only satisfactory for primary nutrients, and none of the algorithms could notably enhance predictions for Phosphorous. The study also finds that fusion algorithms do not significantly improve bias. The study provided evidence of improvement in prediction accuracy with data fusion which can aid in delineating management zones for precision agriculture. It also encourages further research on novel approaches of sensor fusion and algorithms that can effectively handle non-linearity introduced due to the fusion of data. Advisor: Yufeng G

Towards In-situ Based Printed Sensor Systems for Real-Time Soil-Root Nutrient Monitoring and Prediction with Polynomial Regression

This dissertation explores how to increase sensor density in the agricultural framework using low-cost sensors, while also managing major bottlenecks preventing their full commercial adoption for agriculture, accuracy and drift. It also investigated whether low-cost biodegradable printed sensor sheets can result in improved stability, accuracy or drift for use in precision agriculture. In this dissertation, multiple electrode systems were investigated with much of the work focused on printed carbon graphene electrodes (with and without nanoparticles). The sensors were used in two configurations: 1) in varying soil to understand sensor degradation and the effect of environment on sensors, and 2) in plant pod systems to understand growth. It was established that 3) the sensor drift can be controlled and predicted 2) the fabricated low-cost sensors work as well as commercial sensors, and 3) these sensors were then successfully validated in the pod platform. A standardized testing system was developed to investigate soil physicochemical effects on the modified nutrient sensors through a series of controlled experiments. The construct was theoretically modeled and the sensor data was matched to the models. Supervised machine learning algorithms were used to predict sensor responses. Further models produced actionable insight which allowed us to identify a) the minimal amounts of irrigation required and b) optimal time after applying irrigation or rainfall event before achieving accurate sensor readings, both with respect to sensor depth placement within the soil matrix. The pore-scale behavior of solute transport through different depths within the sandy soil matrix was further simulated using COMSOL Multi-physics. This work leads to promising disposable printed systems for precision agriculture