The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants’ disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.

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Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

Surface-enhanced Raman scattering/spectroscopy (SERS) is now being extensively employed as a non-destructive and highly sensitive analytical tool, especially in the detection of various trace chemicals. With the increasing demand for online food quality and safety inspection, SERS has also attracted more and more attention in agricultural applications, including quality assessment of agricultural products, monitoring of crop growth, screening of plant seeds, as well as safety control of varieties of food including fruit and vegetable. A comprehensive understanding of the recent progress in both research and application of SERS-based sensors in these aspects is highly required. This review focuses on diversified strategies for fabrication of effective SERS substrates for SERS sensors, including flexible SERS substrates, reusable SERS substrates, selective SERS substrates for specific targets, and microfluidic SERS-based sensors, and also effective solutions to practical tasks from different aspects, with the discussion of features of different SERS-based sensors and their applications in agriculture and related fields. SERS-based sensors provide extraordinary convenience in sample preparation and instrument operation, exhibiting special advantages over many traditional methods in terms of detection throughput, cost, efficiency, universality, automation, and portability. Currently, researchers are striving to develop advanced SERS sensing techniques in agriculture by making use of modern technology, including nanomaterial/nanostructure fabrication, automation, mobile communication, machine learning, and artificial intelligence. We expect that portable and smart/intelligent advanced SERS-based sensors specifically designed for various situations and scenarios of agricultural purposes will be more widely applied in the future. 

A review: Research progress of SERS-based sensors for agricultural applications

Highly sensitive multiplex detection of foodborne pathogens using a SERS immunosensor combined with novel covalent organic frameworks based biologic interference-free Raman tags.

Detection of infected kernels is important for Fusarium head blight (FHB) prevention and product quality assurance in wheat. In this study, Raman spectroscopy (RS) and deep learning networks were used for the determination of FHB-infected wheat kernels. First, the RS spectra of healthy, mild, and severe infection kernels were measured and spectral changes and band attribution were analyzed. Then, the Inception network was improved by residual and channel attention modules to develop the recognition models of FHB infection. The Inception–attention network produced the best determination with accuracies in training set, validation set, and prediction set of 97.13%, 91.49%, and 93.62%, among all models. The average feature map of the channel clarified the important information in feature extraction, itself required to clarify the decision-making strategy. Overall, RS and the Inception–attention network provide a noninvasive, rapid, and accurate determination of FHB-infected wheat kernels and are expected to be applied to other pathogens or diseases in various crops.

/pdf/raman-spectroscopy-and-improved-inception-network-for-39v7099j.pdf

Raman Spectroscopy and Improved Inception Network for Determination of FHB-Infected Wheat Kernels

Recently, Raman spectroscopy has been developed rapidly and employed for food quality assurance and safety monitoring due to its distinctive advantages of water-insensitive, real-time, field-deployable, label-free, and fingerprinting-identification features as compared with traditional large-scale laboratory-based analytical techniques. This review article discusses the detection/measurement methods of Raman spectroscopy in food safety and quality assurance, including food-sample pretreatment methods, detection-platform design, data analysis, chemometrics, and Raman readers. In addition, this article highlights applications and advantages of Raman technologies in food-production chain, including plant growth, food processing, transportation, storage, and marketing. 

Raman Spectroscopy for Food Quality Assurance and Safety Monitoring: A Review

Plant diseases result in 20-40% of agricultural loss every year worldwide. Timely detection of plant diseases can effectively prevent the development and spread of diseases and ensure the agricultural yield. High-throughput and rapid methods are in great demand. This review investigates the advanced application of Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) in the detection of plant diseases. The determination of bacterial diseases and stress-induced diseases, fungal diseases, viral diseases, pests in beans, and mycotoxins related to plant diseases using RS and SERS are discussed in detail. Then, biomarkers for RS and SERS detection are analyzed with regard to plant disease diagnosis. Finally, the advantages and challenges are further illustrated. Additionally, potential alternatives are proposed for the challenges. The review is expected to provide a reference and guidance for the use of RS and SERS in plant disease diagnostics.

Advanced Application of Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy in Plant Disease Diagnostics: A Review.

Surface-enhanced Raman spectroscopy with gold nanorods modified by sodium citrate and liquid–liquid interface self-extraction for detection of deoxynivalenol in Fusarium head blight-infected wheat kernels coupled with a fully convolution network

Hu Xujin

Papers

Advanced Application of Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy in Plant Disease Diagnostics: A Review.

Surface-enhanced Raman spectroscopy with gold nanorods modified by sodium citrate and liquid–liquid interface self-extraction for detection of deoxynivalenol in Fusarium head blight-infected wheat kernels coupled with a fully convolution network