The growing demand for innovative agricultural technologies drives our research on photonics-based solutions for non-destructive plant analysis. Our work introduces two techniques to overcome the limitations of conventional methods. The first part presents a portable VIS-NIR handheld spectrometer, showcasing strong correlations (0.84 and 0.77) for quantifying anthocyanin and chlorophyll content. In the second part, a customized fiber optic probe using ATR-FTIR enables measurements of plant parameters (water content, antioxidant activity, lignin, and cellulose). Statistical analysis of high-resolution spectra introduces the novel index NDMRI for effective phytochemical differentiation. These non-destructive, rapid, and objective methods promise to optimize agriculture and drive advancements in this field.

In this work, we evaluate the capabilities of a portable smartphone-based laser speckle imager for grading seeds based on the extent of biological activity related to germination inside seeds. The portable illumination device is attached to the rear side of a smartphone and externally controlled via USB port. Both RGB and laser speckle image sequences are simultaneously acquired through smartphone cameras for further processing. A custom image processing algorithm is used to generate the final spatio-temporal activity map. Our approach enables better grading of seeds prior to cultivation thereby potentially improving crop yields.

Reduction of pesticide use is one of the most pressing current issues in plant production. Laser weeding could play a significant role in the fundamental changes expected in agriculture. We present this laser process and discuss technical challenges in different agricultural application scenarios. A proper choice of laser technology depends on many factors such as the agricultural machinery it is mounted on as well as on the crop system it is designed for or the type and size of farm that is to use the device on their farmland.

Plant disease outbreaks pose serious threats to global food security. A rigid methodology that accounts for rapid identification of the earliest point of infection caused by plant viruses is necessary. Raman spectroscopy that generates spectral signatures of cellular-level dynamics resonates the virus induced alteration in plants through moderations in spectral features. Here, we present a model study to identify the earliest point of infection. Measured spectra from healthy and virus infected Arabidopsis thaliana plants are applied to principal component analysis. We found a separation as early as 8 days post inoculation between healthy and virus infected plants.

We present optical methods for contactless measurement of two plant parameters that are relevant for e.g. tomato growth: ‘head thickness’ (the thickness of the stem about 30 cm below the top), and leaf area index (LAI, the leaf area per unit ground area). For LAI a 3D camera is exploited. First results show that leaf area can be determined with an uncertainty <8% under laboratory conditions with would be sufficiently accurate. For head thickness an optical caliper based on a laser and a line array has been developed, showing an uncertainty well below the required 0.1 mm.

Greenhouse production systems are increasingly complex, necessitating a data-driven approach with robust, intelligent sensors. Although there is a clear advantage for growers to be able to monitor the physiological status of the crop, current practices mostly involve cumbersome, expensive, and slow laboratory measurements. It is shown that visible and near-infrared imaging spectroscopy allows for the rapid and non-destructive assessment of the concentrations of sugars, starch, pigments, various nutrients, and dry matter in the leaves and fruits of tomato plants. A tailored feature selection algorithm also shows the feasibility of using as few as 8 bands with spectral cameras. This study validates imaging spectroscopy as a rapid tool for assessing crop status and fruit quality in greenhouse horticulture.

Spectral imaging is widely applied in pest detection and yield estimation in agri- and horticulture. Illumination based spectral imaging (instead of selection on the detector side) could provide a more cost-effective solution, potentially enabling applications for which current spectral imaging technology is too costly. In this contribution we present the development of a low-cost approach for quantitative spectral imaging, with spectral selection using LED-based illumination at various wavelengths. We used a Printed Circuit Board (PCB) approach with a motherboard, which is implemented as a Pi-Hat and controls the LEDs. The motherboard is connected to a donut-shaped PCB with eight surface mounted LEDs with spectra from 400 - 940 nm to select wavelengths. We use a monochrome Si-camera sensitive to all wavelengths, a light diffuser and a 3D printed holder. Using our setup, we register a multispectral image by successively switching LED illumination and grabbing frames. We validated the spectral imaging capability using conventional reflection spectroscopy and color tiles. We are currently investigating various applications of this compact, low-cost solution for spectral imaging.

We investigated the viability of using a human laser hair removal system to remove/reduce sheep wool growth to prevent flystrike as an alternative to crutching and mulesing. A Duetto MT EVO laser from Quanta systems, which combines a 755 nm Alexandrite and a 1064 nm Nd:YAG laser was used to treat excised 1 year old lamb skin. Skin samples analysed with histology showed that white wool fibres remained undamaged beneath the epidermis but black wool sustained damage. These results are unsurprising given the complete absence of the laser’s target melanin in white wool and abundance of it in black.