S18 - Session P1 - Image Based Diagnosis - A new method for extracting plant stress responses by short-distance remote sensing using hyperspectral imagery

Authors: Tetsu Ogawa *, Maro Tamaki, Take Usui, Kouki Hikosaka

Simple methods to assess the environmental stress of crops in agriculture are important for improving crop yields and quality, optimizing resource use such as irrigation, and stable and sustainable production under the global climate change. However, leaf contact devices such as the photosynthetic gas exchange measurement system are not suitable for agricultural fields due to time and cost. Therefore, remote sensing is attracting attention for wider range of observation in a relatively short time at a low cost. Photosynthesis has a mechanism for avoiding irreparable damage to environmental stresses, releasing excess energy out as heat dissipation. The photochemical reflectance index (PRI) is an index using a reflectance of 531nm, which reflects the pigment conversion associated with heat dissipation. At the individual leaf level, PRI can estimate environmental stress. However, at the canopy level, the diversity of leaf characteristics and light environments interferes with stable measurement. Conventional studies have focused on how to handle such interfered information using radiation transfer models, but uncertainties are still included. Here we propose an alternative method to detect plant stress responses using hyperspectral camera (HSC) images. We extract spectral information from leaf surfaces that are exposed to a common environment using several remote sensing indices, which enables us to accurately assess plant stress status. We raised tomato and okra in a glasshouse at different watering availabilities. We obtained HSC images as well as gas exchange rates, water potential and chlorophyll fluorescence. Our method correctly extracted leaves that were exposed to direct sunlight. Use of such leaves enabled us to better predict stress status of plants compared with use of the whole plants. This method will be useful to predict photosynthetic rates and environmental stress status in various plant canopies.