Applications of Photonics in Agriculture Sector: A Review

Laser-Induced Fluorescence for Monitoring Environmental Contamination and Stress in the Moss Thuidium plicatile

The ability to detect, measure, and locate the source of contaminants, especially heavy metals and radionuclides, is of ongoing interest. A common tool for contaminant identification and bioremediation is vegetation that can accumulate and indicate recent and historic pollution. However, large-scale sampling can be costly and labor-intensive. Hence, non-invasive in-situ techniques such as laser-induced fluorescence (LIF) are becoming useful and effective ways to observe the health of plants through the excitation of organic molecules, e.g., chlorophyll. The technique presented utilizes images collected of LIF in moss to identify different metals and environmental stressors. Analysis through image processing of LIF response was key to identifying Cu, Zn, Pb, and a mixture of the metals at nmol/cm2 levels. Specifically, the RGB values from each image were used to create density histograms of each color channel's relative pixel abundance at each decimal code value. These histograms were then used to compare color shifts linked to the successful identification of contaminated moss samples. Photoperiod and extraneous environmental stressors had minimal impact on the histogram color shift compared to metals and presented with a response that differentiated them from metal contamination.

Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy

Green Chemistry is a vital and crucial instrument in achieving pollution control, and it plays an important role in helping society reach the Sustainable Development Goals (SDGs). NIR (near-infrared spectroscopy) has been utilized as an alternate technique for molecular identification, making the process faster and less expensive. Near-infrared diffuse reflectance spectroscopy and Machine Learning (ML) algorithms were utilized in this study to construct identification and classification models of bacteria such as Escherichia coli, Salmonella enteritidis, Enterococcus faecalis and Listeria monocytogenes. Furthermore, divide these bacteria into Gram-negative and Gram-positive groups. The green and quick approach was created by combining NIR spectroscopy with a diffuse reflectance accessory. Using infrared spectral data and ML techniques such as principal component analysis (PCA), hierarchical cluster analysis (HCA) and K-Nearest Neighbor (KNN), It was feasible to accomplish the identification and classification of four bacteria and classify these bacteria into two groups: Gram-positive and Gram-negative, with 100% accuracy. We may conclude that our study has a high potential for bacterial identification and classification, as well as being consistent with global policies of sustainable development and green analytical chemistry.

Global trends and research hotspots on HACCP and modern quality management systems in the food industry

HACCP (Hazard Analysis and Critical Control Points) and modern quality management systems have a significant impact on public health in the food industry. These systems ensure that food products are safe for consumption by identifying and managing potential hazards at every stage of the production process. To stimulate ongoing studies in both developing and underexplored areas of inquiry, this research synthesizes and organizes the contributions made in this field. It examines more than 40 years of studies from Scopus data base on HACCP and modern quality management systems in the food industry using the VOSviewer software version 1.6.18 (Leiden University, The Netherlands) and bibliometrix R-package. This represents, to the authors' knowledge, the first bibliometric analysis undergone in this direction. The graphical framework demonstrates the highest developments in research and the literature review investigates barriers and opportunities of implementing HACCP in food industry organizations. Findings indicate that until the beginning of the 1990s, there was not a large number of scientific production in the field of HACCP and modern quality management systems in the food industry. The USA were the most prolific affiliation terms of scientific production until 2012, when studies from Italy, the United Kingdom, China and Greece intensified. Currently, the most prolific country in terms of publications is Italy. In terms of global cooperation, the United Kingdom, The United States and The Netherlands represent most active nations on this topic Motor themes that reflect the main interest of the researchers include food diseases, quality control, hazards or food supply. The study also provides future research directions regarding food quality and safety management. These should be focused on improving the safety, quality, and sustainability of food products, while also adapting to changing consumer demands, emerging risks, and regulatory requirements.

Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging

Chlorophyll drives plant photosynthesis. Under stress conditions, leaf chlorophyll content changes dramatically, which could provide insight into plant photosynthesis and drought resistance. Compared to traditional methods of evaluating chlorophyll content, hyperspectral imaging is more efficient and accurate and benefits from being a nondestructive technique. However, the relationships between chlorophyll content and hyperspectral characteristics of wheat leaves with wide genetic diversity and different treatments have rarely been reported. In this study, using 335 wheat varieties, we analyzed the hyperspectral characteristics of flag leaves and the relationships thereof with SPAD values at the grain-filling stage under control and drought stress. The hyperspectral information of wheat flag leaves significantly differed between control and drought stress conditions in the 550-700 nm region. Hyperspectral reflectance at 549 nm (r = -0.64) and the first derivative at 735 nm (r = 0.68) exhibited the strongest correlations with SPAD values. Hyperspectral reflectance at 536, 596, and 674 nm, and the first derivatives bands at 756 and 778 nm, were useful for estimating SPAD values. The combination of spectrum and image characteristics (L*, a*, and b*) can improve the estimation accuracy of SPAD values (optimal performance of RFR, relative error, 7.35%; root mean square error, 4.439; R², 0.61). The models established in this study are efficient for evaluating chlorophyll content and provide insight into photosynthesis and drought resistance. This study can provide a reference for high-throughput phenotypic analysis and genetic breeding of wheat and other crops.

Comparison of Physical and System Factors Impacting Hydration Sensing in Leaves Using Terahertz Time-Domain and Quantum Cascade Laser Feedback Interferometry Imaging

To reduce the water footprint in agriculture, the recent push toward precision irrigation management has initiated a sharp rise in photonics-based hydration sensing in plants in a non-contact, non-invasive manner. Here, this aspect of sensing was employed in the terahertz (THz) range for mapping liquid water in the plucked leaves of Bambusa vulgaris and Celtis sinensis. Two complementary techniques, broadband THz time-domain spectroscopic imaging and THz quantum cascade laser-based imaging, were utilized. The resulting hydration maps capture the spatial variations within the leaves as well as the hydration dynamics in various time scales. Although both techniques employed raster scanning to acquire the THz image, the results provide very distinct and different information. Terahertz time-domain spectroscopy provides rich spectral and phase information detailing the dehydration effects on the leaf structure, while THz quantum cascade laser-based laser feedback interferometry gives insight into the fast dynamic variation in dehydration patterns.

A Novel Hyperspectral Method to Detect Moldy Core in Apple Fruits

An innovative low-cost device based on hyperspectral spectroscopy in the near infrared (NIR) spectral region is proposed for the non-invasive detection of moldy core (MC) in apples. The system, based on light collection by an integrating sphere, was tested on 70 apples cultivar (cv) Golden Delicious infected by Alternaria alternata, one of the main pathogens responsible for MC disease. Apples were sampled in vertical and horizontal positions during five measurement rounds in 13 days’ time, and 700 spectral signatures were collected. Spectral correlation together with transmittance temporal patterns and ANOVA showed that the spectral region from 863.38 to 877.69 nm was most linked to MC presence. Then, two binary classification models based on Artificial Neural Network Pattern Recognition (ANN-AP) and Bagging Classifier (BC) with decision trees were developed, revealing a better detection capability by ANN-AP, especially in the early stage of infection, where the predictive accuracy was 100% at round 1 and 97.15% at round 2. In subsequent rounds, the classification results were similar in ANN-AP and BC models. The system proposed surpassed previous MC detection methods, needing only one measurement per fruit, while further research is needed to extend it to different cultivars or fruits.

Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.

Detecting Food Fraud in Extra Virgin Olive Oil Using a Prototype Portable Hyphenated Photonics Sensor

Background

Current developments in portable photonic devices for fast authentication of extra virgin olive oil (EVOO) or EVOO with non-EVOO additions steer towards hyphenation of different optic technologies. The multiple spectra or so-called “fingerprints” of samples are then analyzed with multivariate statistics. For EVOO authentication, one-class classification (OCC) to identify “out-of-class” EVOO samples in combination with data-fusion is applicable.

Objective

Prospecting the application of a prototype photonic device (“PhasmaFood”) which hyphenates visible, fluorescence, and near-infrared spectroscopy in combination with OCC modelling to classify EVOOs and discriminate them from other edible oils and adulterated EVOOs.

Method

EVOOs were adulterated by mixing in 10–50% (v/v) of refined and virgin olive oils, olive-pomace olive oils, and other common edible oils. Samples were analyzed by the hyphenated sensor. OCC, data-fusion, and decision thresholds were applied and optimized for two different scenarios.

Results

By high-level data-fusion of the classification results from the three spectral databases and several multivariate model vectors, a 100% correct classification of all pure edible oils using OCC in the first scenario was found. Reducing samples being falsely classified as EVOOs in a second scenario, 97% of EVOOs adulterated with non-EVOO olive oils were correctly identified and ones with other edible oils correctly classified at score of 91%.

Conclusions

Photonic sensor hyphenation in combination with high-level data fusion, OCC, and tuned decision thresholds delivers significantly better screening results for EVOO compared to individual sensor results.

Highlights

Hyphenated photonics and its data handling solutions applied to extra virgin olive oil authenticity testing was found to be promising.