Advancing Agriculture for Future Generations: A Preview

Welcome to the first part of our preview for our “Advancing Agriculture for Future Generations” content series, a joint series with LCGC International and Spectroscopy.

We are happy to share with you more than 15 articles, all of which showcase the latest spectroscopicresearch in agriculture. Our content is divided into three categories – news articles, interviews, and technical articles. Here’s a sneak peak of what to expect.

This series provides an overview of the most recent spectroscopy research in agriculture. Many studies explore the use of hyperspectral imaging (HSI) in this field. Our first piece, titled, “Hyperspectral Images of Fuji Apples Used as Predictive Data for Fruit Bruise Area,” explores a recent study out of Shangdon Agricultural University in Tai’an, China that uses HSI to obtain more information on the variation law of fruit bruising. Another study from the University of Kentucky, titled, “Cutting-Edge Technology Safeguards Apple Quality: Hyperspectral Imaging and Machine Learning to Combat Codling Moth Infestation,” employed HSI 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. A third study, titled, “Persimmon Leaves’ Contents Determined Using Hyperspectral Imaging,” explores how scientists can use HSI to determine macro- and micronutrient contents rapidly and non-destructively in persimmon leaves.

Machine learning and deep learning models are critical in the agriculture industry. “Revolutionizing Orchard Deep Learning Yields Precise Fruit Tree Segmentation,” and “Revolutionizing Agriculture: Machine Learning Unveils Optimal Microbial Strains for Drought Mitigation” explore using these techniques and applications to help improve crop yield and to improve sustainability practices in agriculture.

As we have observed over the past year, more researchers are exploring the use of artificial intelligence (AI), to improve their work and research. A news story, “Revolutionizing Lettuce Farming: Artificial Intelligence and Spectroscopy for Precise Pigment Phenotyping,” comes courtesy from researchers in Brazil, and explores how by leveraging AI algorithms and visible near-infrared shortwave infrared (Vis-NIR-SWIR) hyperspectroscopy, researchers can achieve precise pigment phenotyping and classification of eleven lettuce varieties, showcasing the potential of integrating advanced technologies in agriculture.

We also included a news story that uses laser-induced breakdown spectroscopy (LIBS) to identify the geographical origin of crops. The news article, titled, “Transfer Learning-Assisted LIBS Enhances Crop Traceability in Sample-Limited Conditions,” shows how LIBS, when combined with deep adaptation networks, can aid in this endeavor.

Soil is the life bed of all crops; good soil leads to enhanced crop yield, whereas bad soil can spoil crops and have a trickle-down negative impact on the economy and food security. The article “New Time Series Prediction Model Tested on Measuring Soil Moisture,” explores a new time series prediction model that combines linear and nonlinear prediction methods to monitor soil moisture.

Spectroscopic techniques play an integral role in advancing agriculture and we hope this series provides a snapshot of some of the research happening in this field.

To read the second part of our preview to the content series and learn more about the Q&As and peer-reviewed and featured articles that will be in our series, click here.