This PDF file contains the front matter associated with SPIE Proceedings Volume 12879, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Hyperspectral cameras are a key enabling technology in precision agriculture, biodiversity monitoring, and ecological research. Consequently, these applications are fueling a growing demand for devices that are suited to widespread deployment in such environments. Current hyperspectral cameras, however, require significant investment in post-processing, and rarely allow for live capture assessments. Here, we introduce a novel hyperspectral camera that combines live spectral data and high-resolution imagery. This camera is suitable for integration with robotics and automated monitoring systems. We explore the utility of this camera for applications including chlorophyll detection and live display of spectral indices relating to plant health. We discuss the performance of this novel technology and associated hyperspectral analysis methods to support an ecological study of grassland habitats at Wytham Woods, UK.

High production output stands against impact on the environment through intense use of fertilizer and pesticides. Manual work comes along with high cost for employees. Automation contributes to finding a reasonable compromise. Machine vision applied under outdoor conditions in daily changing surroundings is the key to successful operation. System with extended spectral ranges – typically named hyper spectral imaging systems – have contributed to progress in recent time. Unfortunately, the system cost has to be considered to shift from scientific research to field application. Here new approaches using Artificial Intelligence (AI) can enable reliable operation for reasonable effort. In the example presented here, imaging takes place by means of standard camera equipment. The image content is evaluated applying AI algorithms. In the next step the spectrally resolved measurements are performed on selected spots by means of robot control or additional MEMS based deflection systems. In turn fast accurate and reliable results are achieved to initiate the relevant action most efficient and maximize the user benefit.

Optical Coherence Tomography (OCT), a non-contact, non-destructive imaging technique, is becoming a popular tool in phytophotonics, helping to address research questions in plant biology and horticulture. However, the stationary nature of typical OCT systems compromises its non-destructive advantage since plants often need to be dissected for an analysis with a laboratory OCT system. Here we present a portable, low-cost OCT system that enables in-situ measurements of plants. We outline technical challenges encountered during the development and showcase initial measurements of different plant tissues.

The increasing demand for alternatives to herbicides in weed management requires innovative approaches to integrated weed management, particularly due to the EU Soil Strategy which aims to reduce the use of pesticides in agriculture by 50% by 2030. Laser-based weed control provides an alternative to chemical methods allowing precise single plant weeding in the direct vicinity of crop plants. However, laser weeding must become more efficient and effective to compete with conventional methods. Optimizing the use of laser entails a wide array of parameters to analyze, such as laser wavelength, beam diameter, angle of irradiation and treatment point. Each parameter needs to be analyzed individually to gain knowledge of its effect on the laser weeding process. In this article, we show plant experiments to determine if the treatment point of the plant has an effect on the laser weeding process and which treatment point yields the highest success rate in damaging the plant. In a first set of experiments, we evaluated two potential treatment points of the plant: the meristem and the stem of the plant. We irradiated test plants with a 1940nm laser system, as this laser wavelength is well absorbed by the water within the plant, promising a high damage potential. We irradiated plants at a young growth stadium of up to four leaves and assessed each plant individually based on their damage level after a period of three weeks. To further improve our understanding of the laser weeding process, we recorded some experiments with a thermal imaging camera to visualize the heat distribution within the plant, as laser weeding is a thermal process, where heat is generated where the laser beam hits the plant.

Along food supply chains, several critical steps can lead to inconsumable food. Especially food of animal origin undergoes rapid aging, when stored inadequately. Quality assessment of packaged food products faces serious problems ranging from the loss of integrity of the package to damage of the food and it is applied only to a low number of samples per batch. As a result, food products are either wasted or not analyzed, which results in a significant decrease in food safety. As a part of an intelligent packaging system, we designed a sensor foil that can detect amines, produced during the food aging process. Change of the fluorescence of the sensor foil can be assessed with spectroscopy or color change from green to red can be detected optically with a camera, e.g. by smartphone. The foil can be incorporated inside the single packaging units and noninvasively measured routinely by the store or consumer. The readout of the foils was performed with steady-state tabletop spectrometers, which were then compared to the results for readouts with different inexpensive handheld devices that could be used during real-life applications, e.g., at any step in a food supply chain. Ideally, the single food product is linked to a single foil at the primary producer, measuring the first spectrum and connecting the data to the specific product, e.g. via distributed ledger. For a transparent process chain, QR-codes could be utilized to allow access to the freshness data along the shelf life of a single package.

Portable Shifted Excitation Raman Difference Spectroscopy (SERDS) using two excitation wavelengths around 785 nm is applied for selected applications in the field of agri-photonics. In the presence of daylight and laser-induced fluorescence, SERDS effectively separates Raman signals of green apple leaves and soil substances with more than 10-fold improved signal-to-background-noise ratios. Major ingredients of bovine milk are clearly detected and identified. A quantitative determination of the fat content in milk is performed and shows a limit-of-detection of 0.1 g / 100 mL. These results show a great potential of portable SERDS for real-world applications, e.g., for precision agriculture and food monitoring.

The detection of Clostridium in milk poses a significant challenge for the dairy industry since traditional methods are time-consuming and lack specificity towards these bacteria. Conversely, microbiological techniques are costly and demand skilled personnel. Clostridium in the form of spores can survive pasteurization and revert to their vegetative form during cheese aging. The gas-producing metabolism of Clostridium, characterized by the production of carbon dioxide and hydrogen, leads to the formation of cracks in the cheese and off-flavors. However, the analysis of gases produced in the headspace can be exploited to determine the presence of Clostridium in milk. This study aims present a Raman spectroscopy-based instrument that enables rapid and cost-effective identification of Clostridium in milk. The methodology aligns with the widely adopted most probable number (MPN) method, as established by Brändle et al. (2016), where vials are considered positive for growth after incubation. However, our innovation lies in the integration of an actual multigas sensing instrument to determine vial positivity, thereby enhancing accuracy. Notably, we emphasize the meticulous selection of vials and the optimization of headspace volume, crucial factors contributing to the heightened performance of the proposed instrument.

The cuticle is a natural polymeric membrane that covers the surface of aerial organs (including fruit) of terrestrial plants. The cuticle membrane mainly consists of cutins, waxes, and polysaccharides and serves as a protective barrier against water movement, adverse environmental conditions and the invasion of pathogens. Fulfilling its barrier function requires an intact cuticle. During fruit development, the cuticle is stretched and heavily stressed as the fruits grow exponentially in phases. In apple fruit, cutin and wax are synthesized constitutively throughout development. The newly synthesized cutin is deposited on the cuticle’s inner side. This results in a gradient of age and strain in the cuticle from outside (older, more strained) to inside (younger, less strained). Wax is deposited mainly within the cutin network and fixes the elastic strain of the cutin network. Studies indicated that wax concentration among the different layers of the cutin also varies with more wax in the outer cuticular proper and less in the inner cuticle layer. As a result, the cuticle of mature apple fruits exhibits a complex micromechanical structure. Characterizing this structure poses challenges that cannot be overcome using conventional tensile testing methods. In this study, we employed a Brillouin scattering setup to investigate the micromechanical structure of the cuticle.

Cyanobacteria, also known as blue-green algae, can produce cyanotoxins which can be harmful to animals and humans and can affect the ecosystem as well as the water quality. In marine or fresh water the cyanobacteria can grow to dense blooms with a high concentration of cells within a few days. Consequently, a fast and ideally real-time observation and analysis of cyanobacterial blooms is very important to ensure safety. We present a Raman spectroscopic approach to investigate and differentiate toxic and non-toxic cyanobacteria. For this, features of the acquired Raman spectra are highlighted to identify harmful cyanobacteria.

Plant in vitro culture techniques are fundamental for research, propagation, and breeding. Automated phenotyping of in vitro cultures can revolutionize trait evaluation by transitioning to continuous and objective quantification, as well as by enhancing accuracy, speed, and efficiency. Limited research exists on automated sensor usage in plant tissue culture, mainly focusing on "plant-to-sensor" approaches. While reflection-based imaging techniques have dominated research to date, fluorescence-based imaging could offer advantages for the application of phenotyping in commercial in vitro propagation and plant research. We developed a new detector head for our “Phenomenon” plant phenotyping system to investigate the potential of fluorescence-based in situ monitoring of plant in vitro culture. In this study, we demonstrate the acquisition of fluorescence image data from plant in vitro cultures as an advanced imaging technique for phenotyping approaches. Over time and qualitatively, we were able to document the development of hairy roots in N. tabacum after transformation with Rhizobium rhizogenes carrying the recently developed reporter gene eYGFPuv.

This project aims to develop a hyperspectral remote sensing approach to detect potato virus Y (pathogenic virus of the family Potyviridae, PVY), from an Unpiloted Aerial Vehicle (UAV). The hyperspectral camera is mounted on the UAV to capture the reflectance of the pixels of the leaves and identify the subtle changes in the color as an indicator of the PVY. The PVY-infected plants tend to have visible mosaic patterns on the leaves, leading to a potential signal for optical detection. Managing the PVY is one of the priorities for the Montana Seed Potato Growers, necessitating the development of a rapid-detection system for PVY. We aim to evaluate if we can detect PVY from a UAV with a radiometrically calibrated hyperspectral sensor to measure upwelling radiance and a calibrated spectrometer to measure downwelling irradiance. We aimed to start with publicly available data from Wageningen University, Netherlands, to build a baseline for our model under controlled lighting. However, we encountered difficulty working with this data, and hope to revisit this portion of the effort in the future.

Polyphenols are important compounds of the plant secondary metabolism, being involved in the plant response to abiotic and biotic stresses and conferring quality properties to fruits and vegetables. Their determination provides valuable information to be used in precision agriculture and in plant physiology studies. Non-destructive evaluation of polyphenols, present in leaf epidermises and fruit skins, can be determined indirectly by multispectral measurements of the chlorophyll fluorescence from the underlying cell layers. This technique, called the chlorophyll fluorescence excitation screening (ChlFES) method, is based on the spectral attenuation by superficial compounds of the incoming radiation impinging on chlorophyll molecules. Examples of the non-destructive monitoring of plant flavonoids response to different UV radiation regimes are reported. Application of the ChlFES to apples, olives, kiwifruits, plums and mainly wine grape to detect flavonoids and anthocyanins was aimed to select fruits with the highest nutraceutical value. In field appliance of the technique by portable fluorescence sensors allowed the control of the wine grape phenolic maturity during the season and the mapping of the grape phenolic content in the vineyard to perform selective harvest and then the production of top-quality wines. Furthermore, the estimate of the plant nutrient status as proximal sensing of the leaf nitrogen content was proved on different crops and proposed to drive precision fertilization.

The purity analysis of oilseed rape (Brassica napus L.) is currently a labor-intensive and manual process, requiring significant human effort for accurate assessment. In this context, the KIRa-Sorter system presents an innovative solution that leverages hyperspectral imaging technology for automating the comprehensive classification of various contaminants present in rapeseed samples. The initial phase of the KIRa-Sorter system involves the efficient capture of hyperspectral and RGB image data from rapeseed samples as input for classification. From up to 200 different types of foreign objects typically found in these samples, a reduced coreset has been defined that the system is able to automatically singulate, classify and physically sort.

Currently, ultraviolet-C (UV-C) has been used in food decontamination, representing an alternative to traditional strategies that are potentially harmful to the physical-chemical aspects of the food and the environment. This study evaluated the effectiveness of UV-C at 222 nm in the decontamination of tomatoes inoculated with Escherichia coli, and verified whether there is an extension of their shelf life, after treatments with light doses of 0; 0.90 ± 0.02; 1.70 ± 0.02; 3.40 ± 0.02; 7.0 ± 0.1; 14.0 ± 0.3; 27.5 ± 0.4 mJ/cm². After applying UV-C light, a microbiological evaluation was done by counting viable colonies. In addition, physical analyses were performed. For this, the irradiated samples were kept in an incubator at 25 °C for 14 days; then, the colorimetric parameters, pH, and weight were quantified daily immediately after the irradiation cycle and at the end of the 14 days of observation. The microbiological results reaffirmed the effectiveness of UV-C 222 nm in surface decontamination of food. From the results of the microbiological analyzes, good decontamination values were obtained using the light dose of 3.40 ± 0.04 mJ/cm² , with a reduction greater than four log. However, physical analyses did not identify a significant extension in shelf life, which also infers the absence of damage or physical impairment to the fruit development. In conclusion, UV-C at 222 nm effectively inactivates E. coli present on the surface of tomatoes without altering their physical characteristics.

Hydroponic farming is considered as a more sustainable solution in comparison to conventional farming. Most of the hydroponic farms rely on manual visual inspection for crop monitoring, which can be subjective, time-consuming, and tedious, especially in the case of large area farms. Hyperspectral Imaging (HSI) is a promising technique for automated sensing and monitoring. Though several automated systems based on HSI have been developed recently for crop monitoring, these tend to be computationally complex and demand significant processing power and time, especially when handling extensive data from large farms. In this context, we explore an approach using spectral ratios for crop growth monitoring and the detection of early-stage nutrient stress. The early detection of the nutrient stress can enable effective crop, resource, and time management in large hydroponic farms. A sensitive nutrient deficiency index, named Normalized Nutrient Deficiency Index (NNDI), has been formulated for the early-stage detection of nutrient deficiencies. Evaluating these indices is computationally simple and quick. A methodology for crop growth monitoring and nutrient deficiency stress using these indices is demonstrated on Lactuca sativa L. crops. It is envisaged that the proposed quick, non-destructive imaging technique can enable future automation possibilities and serve as an invaluable tool in indoor hydroponic farms.

Wine is a widely diffused beverage in the world and its production and quality are greatly influenced by the health conditions of the vine plants. The aim of the study was to investigate the possibility to monitor by Hyperspectral Imaging (HSI) the macronutrients (i.e., Ca, K, etc.) and micronutrients (i.e., Mn, Cu, Zn, etc.) variation in leaves sampled from different areas of a vineyard. The proposed approach is based on the acquisition by HSI in the Short-Wave Infrared Range (SWIR: 1000-2500 nm), of dried and milled vine leaves, followed by the implementation of a classification model based on Partial Least Square (PLS). Micro-X-Ray Fluorescence (micro-XRF) analyses were carried out on the same samples to correlate the SWIR spectral signatures with the detected chemical elements. Furthermore, HSI-based prediction maps, representative of the chemical elements distribution in samples were obtained. The achieved results are very promising, especially with reference to the possibility to adopt a fast strategy to monitor the macro- and micronutrients variation in the leaves directly in field, allowing to treat in real-time any nutritional deficiencies.

The color change of oil palm Fresh Fruit Bunches (FFB) is one of the important parameters for harvesting decisions. However, human visual identification is prone to errors due to uncontrolled ambient lighting and the far distance of fruit bunches on tall palm trees. These errors can lead to inaccurate harvesting and significant revenue loss. This study introduces a laser remote sensor for non-destructive FFB ripeness assessment. Based on the unique spectral reflection curves of the FFB at different ripeness, laser modules at three different wavelengths (visible- NIR region) have been employed for the measurement. The photodetector and laser sources are configured in a coaxial manner to enable long work distances up to 9 m. This portable laser remote sensor has undergone successful on-plant testing in oil palm estates, with measurements validated against oil content by conventional bunch analysis. It is a potential tool for precision harvesting and oil yield prediction.

Urban greenery is a vital component of city landscapes. With the increasing influence of extreme weather events and global warming, coupled with the pollution associated with urban activities, innovative solutions are needed to plan and manage green urban infrastructure for a healthy city ecosystem. This study focuses on utilizing portable photonic devices, to assess the physiological responses of Mediterranean selected plant species (Quercus Ilex L., Nerium Oleander L., and Prunus Cerasifera Ehrh) to variations in environmental conditions and pollution levels in representative areas of both the central and peripheral districts of Cagliari. During the post-summer period of 2023, a measurement campaign was executed, encompassing fundamental parameters like Leaf Area, Leaf Fresh and Dry Weight, and the plant canopy through the Normalized Difference Vegetation Index (NDVI). Portable sensors using fluorescence and Raman techniques were then employed to non-invasively monitor leaf component levels, correlating them with plant species and urban areas. Historical environmental and pollution data from 2019 to 2023 were gathered from monitoring stations in the Sardinia Region. Observations revealed a similar response of the plant species between the two zones, that is an imbalance in chlorophyll indexes, accompanied by opposite variations in polyphenol indexes. A characteristic feature was a greater index variability observed in Quercus Ilex L. and Nerium Oleander L. compared to Prunus Cerasifera Ehrh. This preliminary study initiated the design of a supporting protocol to select site-specific resilient plant species for urban greenery.

In this article, we report our work on the development of a non- invasive, rapid, robust, and high-fidelity technique that can be used to discriminate between genetic variants. Our study focused on terahertz (THz) spectroscopy and imaging to distinguish between genetic variants of the Allium genus rapidly and accurately. This was done by measuring the cellular water dynamics of the samples by measuring their evaporation profiles using Laser Feedback Interferometry (LFI) with THz Quantum Cascade Lasers (QCL). The evaporation profiles of the samples were then processed to create trajectories in the amplitude-phase domain, which correlated with cell age, cell type, and the amount of water bound to biomolecules. This technique can differentiate between the members of the Allium genus. The presence of outliers was also studied to determine the effectiveness of the technique for different samples and to negate external influence. This was done to discern the extent of influence of cell biomechanics and biochemistry between genetic variants. We found that within a genus, different species would have different degree of interaction between cellular water and cell biochemistry, which could be clearly mapped out using THz-QCL-based LFI. Based on our observations, we propose that this method could be appropriate for observing minute alterations in cellular water dynamics in real-time, and in the future, has the potential to be employed for rapid and effective genetic discrimination in agricultural and genome conservation applications.