Development of a Remote Crop Quality Sensor: Advancing Carotenoid Assessment with Raman Spectroscopy

The accurate evaluation of crop quality is vital for sustainable agriculture and optimized production. Raman spectroscopy, renowned for its insensitivity to water interference and its ability to deliver molecular-specific information, presents significant potential as a remote sensing technology. This study explores the feasibility of adapting advanced Raman spectroscopy as a remote crop quality sensor for the precise assessment of carotenoids. Carotenoids were chosen due to their dual role as key stress indicators in crops and their well-established antioxidant benefits for human health.

To explore carotenoid variability, Arabidopsis thaliana and Spinacia oleracea were analyzed. Raman spectroscopy measurements were performed on two leaves per plant using a 785 nm laser. For the carotenoid quantification, Linear Discriminant Analysis (LDA) was adapted. The spectra were processed through smoothing, background removal, and normalization, followed by modification with an amplifying factor. This study evaluated the impact of these processing methods, particularly the application of the amplifying factor, on the accuracy of the model. High-Performance Liquid Chromatography (HPLC) was employed as the reference method for validation. Three-quarters of the samples were used to construct the model, while the remaining one-quarter was reserved for validation. As a result, the model utilizing spectra modified with the amplifying factor in most cases achieved higher validation accuracy compared to models based on unmodified spectra.

This study introduces a novel Raman spectroscopy-based remote sensing approach for crop quality assessment, establishing an enhanced model for interpreting spectral data. By enabling precise detection of stress-induced changes in plant chemical composition, including carotenoids, this technique paves the way for scalable, real-time monitoring through Raman-equipped machinery or drones, advancing sustainable agriculture practices.