The soil plant analysis development (SPAD) value is used as an indicator for nitrogen fertilizer management in Japan because of its relationship with leaf chlorophyll content. However, a SPAD meter is a time-consuming and labor-intensive tool for use on a large scale. To overcome this shortcoming of the SPAD meter, we investigated the potential of using RGB images of a naked barley canopy taken by a UAV, which has a high temporal and spatial resolution for a large area, to estimate SPAD values in the field. First, linear regression models for estimating SPAD values were constructed using RGB values measured by a chroma meter with a stable internal light source at the leaf level. Second, the potential of using these models in the field was evaluated using RGB values estimated from images at the canopy level taken under sunlight by a UAV at different growth stages of the naked barley. Our results suggested that SPAD values could be estimated from UAV canopy images using regression models for estimating SPAD values that were constructed for the leaf level. The SPAD (g, b) multivariate linear regression model could be regarded as the optimal model for estimating the SPAD values of naked barley leaves at both the leaf and canopy levels in our study. Our results indicated that it was possible to estimate SPAD values of naked barley leaves from canopy RGB images in the field on a large scale using the regression model constructed for the leaf level.

The plant growth monitoring based environmental control in greenhouse, which is defined as the speaking plant approach (SPA) concept, has been recognized as a key to achieve the efficient agricultural production in highly sophisticated greenhouses. Photosynthetic function is one of the most important features that should be monitored for SPA and the representative photosynthetic pigments such as chlorophyll (Chl) a and Chl b in leaves are measured to evaluate the status of photosynthesis at single leaf level, and furthermore, total Chl content indicates the abundance of nutrients and Chl a/b ratio represents the light condition of the leaf surface. The spectrometer or high-performance liquid chromatography (HPLC) is generally used as the device for the Chl pigment measurement. However, there is no previous study to clarify whether the quantitative intercomparison between the datasets of Chl contents measured by spectrometer and HPLC is possible or not. In this study, we investigated the differences in Chl contents of the same leaves of tomato, paprika, and strawberry grown under greenhouse conditions measured by using spectrometer and HPLC and discussed the device dependent potential bias. As a result, strong linear correlations between Chl a-, Chl b-, total Chl- contents and Chl a/b ratios measured by the spectrometer and HPLC were confirmed in tomato, paprika, and strawberry leaves. This finding implies that the conversion of Chl contents measured by spectrometer into those by HPLC can be done with the linear regression formulas derived in the same way shown in this study.