A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions

Abstract Hyperspectral imaging is a technology that can be used to monitor plant responses to stress. Hyperspectral images have a full spectrum for each pixel in the image, 400–2500 nm in this case, giving detailed information about the spectral reflectance of the plant. Although this technology has been used in laboratory-based controlled lighting conditions for early detection of plant disease, the transfer of such technology to imaging plants in field conditions presents a number of challenges. These include problems caused by varying light levels and difficulties of separating the target plant from its background. Here we present an automated method that has been developed to segment raspberry plants from the background using a selected spectral ratio combined with edge detection. Graph theory was used to minimise a cost function to detect the continuous boundary between uninteresting plants and the area of interest. The method includes automatic detection of a known reflectance tile which was kept constantly within the field of view for all image scans. A method to split images containing rows of multiple raspberry plants into individual plants was also developed. Validation was carried out by comparison of plant height and density measurements with manually scored values. A reasonable correlation was found between these manual scores and measurements taken from the images (r2 = 0.75 for plant height). These preliminary steps are an essential requirement before detailed spectral analysis of the plants can be achieved

Raspberry plant stress detection using hyperspectral imaging.

Monitoring plant responses to stress is an ongoing challenge for crop breeders, growers, and agronomists. The measurement of below-ground stress is particularly challenging as plants do not always show visible signs of stress in the above-ground organs, particularly at early stages. Hyperspectral imaging is a technique that could be used to overcome this challenge if associations between plant spectral data and specific stresses can be determined. In this study, three genotypes of red raspberry plants grown under controlled conditions in a glasshouse were subjected to below-ground biotic stresses (root pathogen Phytophthora rubi and root herbivore Otiorhynchus sulcatus) or abiotic stress (soil water availability) and regularly imaged using hyperspectral cameras over this period. Significant differences were observed in plant biophysical traits (canopy height and leaf dry mass) and canopy reflectance spectrum between the three genotypes and the imposed stress treatments. The ratio of reflectance at 469 and 523 nm showed a significant genotype-by-treatment interaction driven by differential genotypic responses to the P. rubi treatment. This indicates that spectral imaging can be used to identify variable plant stress responses in raspberry plants

Seeing the wood for the trees: hyperspectral imaging for high throughput QTL detection in raspberry, a perennial crop species

Physiological and physical traits are excellent indicators of many crop characteristics, but precise phenotyping of these traits is time consuming and, therefore, limits progress in crop breeding and the speed of crop monitoring. Hyperspectral imaging offers an opportunity to overcome these barriers as a technique for high throughput field measurements. Using a recently developed hyperspectral imaging platform devised for plantations of the perennial crop raspberry, this study aimed to further develop the tool and test its capacity as an innovative approach for high throughput field phenotyping, data collection and analysis. Hyperspectral imaging and visual crop assessments were carried out over two growing seasons in a field-grown raspberry mapping population, and data were subject to Quantitative Trait Loci (QTL) analysis. The findings show that reflectance intensity at multiple wavelengths can be linked to known genetic markers in raspberry, and many of these 'spectral traits' are expressed consistently through the growing season and between years, for example spectral ratio 719 nm / 691 nm shows up consistently as a QTL on LG4. Spectral traits were identified that co-located with previously mapped physical traits, such as 719 nm / 691 nm and cane density. The study indicates that hyperspectral imaging can be used as an innovative approach for high throughput field phenotyping of raspberry and could be transferred readily to other perennial crops. Our approach provides a pipeline for automated field data collection and analysis that can be used for rapid QTL detection of spectral traits