The Speaking Plant Approach (SPA) is regarded as a desirable concept which defines that the environmental factors should be adjusted to the crop’s physiological status. The first and most important step in the SPA concept is to obtain physiological information from a living plant. Especially for environmental control in highly sophisticated greenhouse, daily measurement of plant growth must be indispensable in SPA concept. In our previous study, a robotized chlorophyll fluorescence (CF) imaging system that evaluates daily changes in photosynthetic function of tomato canopy has been developed and came onto the market in 2015. In this study, we applied the robotized CF imaging system to measure the daily stem elongation of tomato plants grown in greenhouse. CF images of shoot apex of tomato plants were taken by using the robotized CF imaging system and detected the shoot apex manually by using commercially available image analysis software to obtain the average plant height. Then, the daily stem elongation was calculated by subtracting the average plant height of one day before from that of the day. The performance verification test proved that the spatial resolution of CF image was 1.6 mm pixel^-1 and the reproducibility of average height measurement was within 0.01 %. And, a long-term stem elongation monitoring for consecutive 70 days, which was done with the conventional weekly stem elongation monitoring by using a measuring tape, suggested that daily stem elongation is able to be monitored with the robotized CF imaging system.

Process-based ecosystem models simulate carbon, nitrogen and water cycles and they are applicable to assess ecosystem functions and services. Meteorological conditions as key driving forces of ecosystem processes are variable both spatially and temporally, therefore require appropriate downscaling from the coarser resolution data in use with ecosystem models. We examined a weather generator (WG), one of the stochastic downscale methods generating daily precipitation and air temperature in biomass carbon and water balance simulation by Biome-BGC model. Beijing and Urumqi, China having different climatic conditions, were selected as case study sites. The biomass carbon and water balance in Beijing by WG-meteorological data showed close values to those by measured meteorological data. In contrast, the water outflow by WG data showed large relative error (-85%) to one by measured data in Urumqi. To reduce the error of outputs we applied the bias correction to daily precipitation generated by WG. The biomass carbon by corrected WG data showed smaller error. Water outflow error was also reduced, however, it still had -20% relative error to the one by measured data. This error is probably attributed to the increasing trend of daily precipitation with duration of continuous rainy days which was not reproduced by present WG. This study showed the potential of WG as a front end of ecosystem models that downscales low spatio-temporal resolution meteorological data, however it also showed a limitation of WG in such cases where the characteristics of meteorological elements that are not reproduced by WG are important in ecological processes.

As a highly sophisticated strategy for environmental control in greenhouses, the concept of speaking plant approach (SPA) has attracted a great deal of attention. Sensor-based plant diagnosis techniques to monitor plant physiological status are the first and most important step in SPA. Currently, imaging systems for plant growth monitoring are especially required in wide range of agricultural production not only in advanced greenhouses but also in traditional vinyl houses. In this study, we developed a compact- and lightweight- multiple imaging system that is consist of an infrared camera filtered with a long-pass filter (λ > 620 nm), a color camera, and blue/red/infrared/white LEDs. The developed imaging system enable us to conduct chlorophyll fluorescence imaging, NDVI imaging, and color imaging at the same time. We applied the system for imaging of tomato plant grown under greenhouse condition and successfully obtained an obvious chlorophyll fluorescence induction curve derived from a series of chlorophyll fluorescence images and NDVI and color images that are useful to identify the matured tomato fruits.