Chlorophyll content has been used as an indicator for assessing photosynthetic ability, health and defense against a variety of degenerative diseases. Hyperspectral remote sensing offers some non-destructive methods and has played an important role in evaluating vegetation characteristics. However, the prices of traditional field portable spectroradiometers, such as Ocean Optics Hyperspectral Vis-NIR spectroradiometers and Analytical Spectral Devices FieldSpec series, have not yet decreased to consumer levels, which prevents much practical use. Recently, fingertip-sized spectrometers have been developed and then they could be powerful tools for evaluating vegetation characteristics. In the present study, a compact spectrometer (C12880MA-10, Hamamatsu Photonics) was used to evaluate chlorophyll content in tea leaves. Incorporating pre-processing techniques was effective for obtaining estimated values with high accuracy and then de-trending was the best pre-processing technique in this study, achieving an RPD of 2.03 and an RMSE of 3.07 μg cm－2. The proposed method is cost effective, practical for consumers to apply and will enable effective crop management.
Landslide map is a thematic map used for disaster management. In recent years, there have been attempts to create landslide maps using artificial intelligence-based approaches. This study aimed to clarify the effectiveness of two normalization methods, derived from the spatial non-uniformity and continuity of landslide topography, for the deep generative model of landslide moving mass. We propose a normalization method for the supervised data to correct the spatial non-uniformity of landslides. The resulting supervised data, normalized by landslide area occupancy, improved the learning efficiency of the deep generative model. We also propose a normalization method for the inferenced results using the spatial continuity of landslides. The inferenced results, post-processed by employing our normalization method, showed reasonable distribution in comparison to the ground truth.