Advancing Agriculture for Future Generations: The Impact of Spectroscopy on an Important Field

Welcome to our “Advancing Agriculture for Future Generations” content series!

Below is a compilation of news stories, Q&As, and technical articles that spotlight the current and ongoing spectroscopic research in the field of agriculture.

Click an article below to begin your journey!

Spectroscopy in Agriculture: An Interview with Dmitry Kurouski

Dmitry Kurouski of Texas A&M University speaks to Spectroscopy Editor Patrick Lavery about Raman spectroscopy's role in determining crop yield of key food items as the world population continues to increase.

Author: Patrick Lavery

Link: https://www.spectroscopyonline.com/view/spectroscopy-agriculture-interview-dmitry-kurouski

Reviewing the Impact of Raman Spectroscopy on Crop Quality Assessment: An Interview with Miri Park

Miri Park of the Fraunhofer Institute for Environmental, Safety, and Energy Technologies is examining how Raman spectroscopy could aid non-destructive sensing in agricultural science. Recently, Park sat down with Spectroscopy to discuss micro-Raman spectroscopy's role in assessing crop quality, particularly secondary metabolites, across different contexts (in vitro, in vivo, and in situ), while suggesting future research for broader application possibilities.

Author: Will Wetzel

Link: https://www.spectroscopyonline.com/view/reviewing-the-impact-of-raman-spectroscopy-on-crop-quality-assessment-an-interview-with-miri-park

Monitoring Soil Quality Using MIR and NIR Spectral Models: An Interview with Felipe Bachion de Santana

Felipe Bachion de Santana of Teagasc in Wexford, Ireland, is exploring new ways to monitor soil quality through using spectroscopic techniques. Spectroscopy spoke to him about his team’s work in monitoring the quality of soil to improve agricultural efficiency.

Author: Will Wetzel

Link: https://www.spectroscopyonline.com/view/monitoring-soil-quality-using-mir-and-nir-spectral-models-an-interview-with-felipe-bachion-de-santana 

Q&A: Portable FT-IR Empowers On-Site Food Quality Assurance

Exploring the transformative capabilities of handheld FT-IR spectrometers, a review from Yıldız Technical University and The Ohio State University emphasizes their pivotal role in ensuring food integrity and safety across the entire supply chain. Read the Q&A about this review article here.

Author: Patrick Lavery

Link:https://www.spectroscopyonline.com/view/transformative-solutions-portable-ftir-on-site-food-quality-assurance

Revolutionizing Orchard Management: Deep Learning Yields Precise Fruit Tree Segmentation

A recent study from Jeonbuk National University introduces a novel technique for orchard management: tackling intertwined fruit trees' precise segmentation using deep learning models.

Author: Spectroscopy Staff

Link: https://www.spectroscopyonline.com/view/revolutionizing-orchard-management-deep-learning-yields-precise-fruit-tree-segmentation

Revolutionizing Agriculture: Machine Learning Unveils Optimal Microbial Strains for Drought Mitigation

Researchers from the University of Szczecin and other Polish institutions have applied the power of machine learning, employing various models, to forecast optimal microbial strains for mitigating drought impacts on crops, marking a leap toward sustainable agriculture to ensure global food security.

Author: Spectroscopy Staff

Link: https://www.spectroscopyonline.com/view/revolutionizing-agriculture-machine-learning-unveils-optimal-microbial-strains-for-drought-mitigation

Cutting-Edge Technology Safeguards Apple Quality: Hyperspectral Imaging and Machine Learning to Combat Codling Moth Infestation

Researchers at the University of Kentucky employ non-destructive hyperspectral imaging and machine learning to predict and manage the physicochemical quality attributes of apples during storage, addressing the impact of codling moth infestation and revolutionizing apple quality assurance.

Author: Spectroscopy Staff

Link: https://www.spectroscopyonline.com/view/cutting-edge-technology-safeguards-apple-quality-hyperspectral-imaging-and-machine-learning-to-combat-codling-moth-infestation

Revolutionizing Lettuce Farming: Artificial Intelligence and Spectroscopy for Precise Pigment Phenotyping

Researchers in Brazil leverage artificial intelligence algorithms and Vis-NIR-SWIR hyperspectroscopy to achieve precise pigment phenotyping and classification of eleven lettuce varieties, showcasing the potential of integrating advanced technologies in agriculture.

Author: Spectroscopy Staff

Link: https://www.spectroscopyonline.com/view/revolutionizing-lettuce-farming-artificial-intelligence-and-spectroscopy-for-precise-pigment-phenotyping

Transfer Learning-Assisted LIBS Enhances Crop Traceability in Sample-Limited Conditions

Researchers have developed a transfer learning-assisted laser-induced breakdown spectroscopy (LIBS) method to identify geographical origins of crops with an impressive accuracy by incorporating deep adaptation networks.

Author: Spectroscopy Staff

Link: https://www.spectroscopyonline.com/view/transfer-learning-assisted-libs-enhances-crop-traceability-in-sample-limited-conditions 

Hyperspectral Images of Fuji Apples Used as Predictive Data for Fruit Bruise Area

While this technology has been frequently deployed in recent years to evaluate fruit quality, relatively few studies have examined such parameters as variation, damage time, and damage degree.

Author: Patrick Lavery

Link: https://www.spectroscopyonline.com/view/hyperspectral-images-fuji-apples-predictive-data-fruit-bruise-area

New Time Series Prediction Model Tested on Measuring Soil Moisture

Scientists from Zhejiang A&F University and Huzhou University in China recently created a new time series prediction model that combines linear and nonlinear prediction methods.

Author: Aaron Acevedo

Link: https://www.spectroscopyonline.com/view/new-time-series-prediction-model-tested-on-measuring-soil-moisture

Persimmon Leaves’ Contents Determined Using Hyperspectral Imaging

Using visible and near-infrared (Vis/NIR) hyperspectral imaging (HSI), scientists were able to determine macro- and micronutrient contents rapidly and non-destructively in persimmon leaves.

Author: Aaron Acevedo

Link: https://www.spectroscopyonline.com/view/persimmon-leaves-contents-determined-using-hyperspectral-imaging

Study on Estimating Total Nitrogen Content in Sugar Beet Leaves Under Drip Irrigation Based on Vis-NIR Hyperspectral Data and Chlorophyll Content

This article explores the relationship between the leaf nitrogen content (LNC) and hyperspectral remote sensing imagery (HYP) to construct an estimation model of the LNC of drip-irrigated sugar beets.

Authors: Zong-fei Li, Bing Chen, Hua Fan, Cong Fei, Ji-xia Su, Yang-yang Li, Ning-ning Liu, Hong-liang Zhou, Li-juan Zhang, Kai-yong Wang

Link: https://www.spectroscopyonline.com/view/study-on-estimating-total-nitrogen-content-in-sugar-beet-leaves-under-drip-irrigation-based-on-vis-nir-hyperspectral-data-and-chlorophyll-content 

Detection of the Early Fungal Infection of Citrus by Fourier Transform Near-Infrared Spectra

The results in this study indicate that FT-NIR spectroscopy, combined with chemometric methods, is able to distinguish early fungal infections in citrus.

Authors: Maopeng Li, Yande Liu, Jun Hu, Chengtao Su, Zhen Xu, Huizhen Cui

Link: https://www.spectroscopyonline.com/view/detection-of-the-early-fungal-infection-of-citrus-by-fourier-transform-near-infrared-spectra 

Soil Organic Matter Estimation Modeling Using Fractal Feature of Soil for vis-NIR Hyperspectral Imaging

A novel intelligent inversion model integrating multiscale fractal analysis, PCA, and machine learning techniques (RF and SVM) was devised to accurately estimate soil organic matter (SOM) using hyperspectral data.

Author: Shaofang He, Qing Zhou, Fang Wang, Luming Shen, Jing Yang

Link: https://www.spectroscopyonline.com/view/soil-organic-matter-estimation-modeling-fractal-feature-vis-nir-hyperspectral-imaging