Principles, developments and applications of laser-induced breakdown spectroscopy in agriculture: A review

Yu, Ke; Ren, Jie; Zhao, Yanru

doi:10.1016/j.aiia.2020.07.001

Cited by 24 publications

(13 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Reduce the costs of analyses and enable higher throughput Baldock et al, 2014;Wijewardane et al, 2018Wijewardane et al, , 2020Dangal et al, 2019;Yu et al, 2020 Improves baselining and reduces model uncertainty to generate high-value credits New metrics for soil health Earlier identification of practices that have an impact on soil carbon sequestration Conant et al, 2011;Cotrufo et al, 2015;Vos et al, 2018;Lavallee et al, 2020 inorganic carbon amendments such as biochar to enhance carbon sequestration, provided that the carbon remains in the project area. Properly quantifying and verifying the inorganic carbon stocks, however, is challenging as it is not currently estimated by biogeochemical models.…”

Section: Improved Lab Analytical Methodsmentioning

confidence: 99%

“…In-field soil property measurements through probes or scanners still require in-person visit fields but would reduce sample shipping and tracking logistical needs (Viscarra Rossel and Bouma, 2016). Finally, improvements in lab analyses could decrease costs and already show promise in the near-term based on published data on spectroscopic techniques and existing spectral databases (Baldock et al, 2014;Yu et al, 2020) (further detail in Supplementary Material).…”

Section: Innovations In Soil Analyses Can Improve Scale and Throughputmentioning

confidence: 99%

See 1 more Smart Citation

Implementing the Soil Enrichment Protocol at Scale: Opportunities for an Agricultural Carbon Market

et al. 2021

View full text Add to dashboard Cite

High-quality agricultural carbon credits that incentivize regenerative practices can help address climate change through greenhouse gas (GHG) abatement and CO2 sequestration. Generating large volumes of such credits requires rigorous crediting methodologies. The Soil Enrichment Protocol (SEP) by the Climate Action Reserve (CAR) aims to unlock this type of crediting potential. The SEP includes new expert-driven standards for validating the use of soil biogeochemical modeling to generate credits. Technical experts at Indigo Ag participated in the SEP working group and are supporting implementation of the first project, CAR 1459_RP1, on hundreds of thousands of acres in the US. The authors share their thoughts on new approaches enabled by the SEP as both contributors to the theory behind and practitioners of these approaches. The SEP enables scalable, high-quality credits through four main advances: (1) allowing flexibility in the use of biogeochemical models that meet explicit performance requirements, (2) enabling a new approach to field-level, modeled baselines, (3) supporting a hybrid approach of credit generation using both soil measurement and modeling, and (4) requiring a new type of credit uncertainty quantification that accounts for multiple sources of uncertainty. Together these advances support agricultural credit quantification that enables payments to offset transitional costs for growers, at large enough scales to create a robust market, with a level of rigor that ensures any credited emission reductions have real climate impact. Innovations in soil analyses, advances in research, and improvements in data collection could further improve the potential for agricultural carbon credits to scale.

show abstract

Section: Improved Lab Analytical Methodsmentioning

confidence: 99%

Section: Innovations In Soil Analyses Can Improve Scale and Throughputmentioning

confidence: 99%

Implementing the Soil Enrichment Protocol at Scale: Opportunities for an Agricultural Carbon Market

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Special emphasis is given in the determination of the most important spectral features in terms of their contribution in the discrimination procedure, aiming to connect the different spectral features exhibited in the plasma emission with the degree of successful classification by machine learning. The above issues are studied here aiming to show and validate the high potential of machine learning aided LIBS for olive oil classification in terms of its geographical origins [38][39][40] .…”

Section: Laser-induced Breakdown Spectroscopy Coupled With Machine Lementioning

confidence: 99%

Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Gyftokostas

Stefas

Kokkinos

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Olive oil is a basic element of the Mediterranean diet and a key product for the economies of the Mediterranean countries. Thus, there is an added incentive in the olive oil business for fraud through practices like adulteration and mislabeling. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) assisted by machine learning is used for the classification of 139 virgin olive oils in terms of their geographical origin. The LIBS spectra of these olive oil samples were used to train different machine learning algorithms, namely LDA, ERTC, RFC, XGBoost, and to assess their classification performance. In addition, the variable importance of the spectral features was calculated, for the identification of the most important ones for the classification performance and to reduce their number for the algorithmic training. The algorithmic training was evaluated and tested by means of classification reports, confusion matrices and by external validation procedure as well. The present results demonstrate that machine learning aided LIBS can be a powerful and efficient tool for the rapid authentication of the geographic origin of virgin olive oil.

show abstract

“…With appropriate calibration, the LIBS analysis can provide quantitative measurement for most elements in soil including carbon, nitrogen, potassium, sulfur, and phosphorus 21,22 . There have been some applications of standalone LIBS systems in precision agriculture [21][22][23][24] , however there has been no detailed research of LIBS application in combination with ML and drones. Some studies have found it challenging to measure nitrogen using LIBS due to environmental factors; Earth's atmosphere is almost 80% nitrogen which will interfere with the sample measurement result since soil is less than 1% nitrogen.…”

Section: Introductionmentioning

confidence: 99%

Total Nitrogen Estimation in Agricultural Soils via Aerial Multispectral Imaging and Libs

Hossen

Diwakar

Ragi

2021

Preprint

View full text Add to dashboard Cite

Measuring soil health indicators is an important and challenging task that affects farmers’ decisions on timing, placement, and quantity of fertilizers applied in the farms. Most existing methods to measure soil health indicators (SHIs) are in-lab wet chemistry or spectroscopy-based methods, which require significant human input and effort, time-consuming, costly, and are low-throughput in nature. To address this challenge, we develop an artificial intelligence (AI)-driven near real-time unmanned aerial vehicle (UAV)-based multispectral sensing (UMS) solution to estimate total nitrogen (TN) of the soil, an important macro-nutrient or SHI that directly affects the crop health. Accurate prediction of soil TN can significantly increase crop yield through informed decision making on the timing of seed planting, and fertilizer quantity and timing. We train two machine learning models including multi-layer perceptron and support vector machine to predict the soil nitrogen using a suite of data classes including multispectral characteristics of the soil and crops in red, near-infrared, and green spectral bands, computed vegetation indices, and environmental variables including air temperature and relative humidity. To generate the ground-truth data or the training data for the machine learning models, we measure the total nitrogen of the soil samples (collected from a farm) using laser-induced breakdown spectroscopy (LIBS).

show abstract

Principles, developments and applications of laser-induced breakdown spectroscopy in agriculture: A review

Cited by 24 publications

References 92 publications

Implementing the Soil Enrichment Protocol at Scale: Opportunities for an Agricultural Carbon Market

Implementing the Soil Enrichment Protocol at Scale: Opportunities for an Agricultural Carbon Market

Laser-induced breakdown spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination

Total Nitrogen Estimation in Agricultural Soils via Aerial Multispectral Imaging and Libs

Contact Info

Product

Resources

About