Irrigation management continues to be an important issue for tomato cultivation, especially in plant factories. Accurate and timely assessment of tomato leaf water status is a key factor in enabling appropriate irrigation, which can save nutrition solution and labor. In recent decades, hyperspectral imaging has been widely used as a nondestructive measurement method in agriculture to obtain plant biological information. The objective of this research was to establish an approach to obtain the tomato leaf water status—specifically, the relative water content (WC) and equivalent water thickness (MC)—for five different tomato cultivars in real time by using hyperspectral imaging. The normalized difference vegetation index (NDVI) and two-band vegetation index (TBI) analyses were performed on the tomato leaf raw relative reflection (RAW), the inversion-logarithm relative reflection (LOG), and the first derivative of relative reflection (DIFF) from wavelengths of 900 nm to 1700 nm. The best regression model for WC assessment was obtained by TBI regression using DIFF at wavelengths of 1410 nm and 1520 nm, and the best regression model for MC assessment was obtained by NDVI regression using RAW at wavelengths of 1300 nm and 1310 nm. Higher model performance was obtained with MC assessment than with WC assessment. The results will help improve our understanding of the relationship between hyperspectral reflectance and leaf water status.

Cucumber (Cucumis sativus L., cv. “Nina Z”) plants grafted onto squash (Cucurbita maxima, cv. “Yu Yu Ikki”) were grown in a greenhouse using the newly developed nutrient film technique (NFT) hydroponic system “Kappa land” (Mitsubishi aqua solutions Co., Ltd., Tokyo, Japan), from February to June 2022. The growth and development of cucumbers were examined under two different training methods: Lowering training (LT) and Pinching training (PT). Data collected were related to water and nutrient consumption, plant growth and development parameters, and the workload of the main activities. The results showed that plants grown under the LT recorded significantly higher total stem length (10.9 m) and number of nodes (133). In addition, from 21 April to 19 May, the leaf area index was significantly higher in the LT treatment. The highest total yield (15.4 kg m⁻²) and marketable yield per unit area (13.8 kg m⁻²) were recorded in the LT treatment. Regarding fruit growth, the fruits took 14 and 19 days to reach the standard harvest weight in the PT and LT treatments, respectively. In addition, the fruits were more straight in the PT treatment. The water use efficiency was not significantly different between the two treatments. However, the nutrient use efficiency was significantly higher in the PT treatment because plants produced more vegetative organs in the LT treatment instead of fruits. The work for removing old leaves and harvesting fruits was simplified in the LT treatment. The LT method can be effective for the automation of old leaf removal and fruit picking by the robot in the future.

Grafted cucumber plants were grown in a new hydroponic system (“Kappa Land”, Mitsubishi Chemical Aqua Solutions, Co., Ltd., Tokyo, Japan). Two different nutrient management methods were applied to the plants as treatments: Electrical Conductivity-based Management (ECM) and Quantitative Nutrient Management (QNM). During the growth period, we examined plant growth characteristics and productivity, fruit growth characteristics and quality, and nutrient use characteristics. The results revealed that the QNM technique significantly reduced the nutrient supply rate per plant for Ca²⁺, SO₄²⁻, and N by 28.5%, 25.5%, and 23.3%, respectively. Similarly, the absorption rates per plant of SO₄²⁻, K⁺, and PO₄³⁻ were reduced by 17.8%, 11.9%, and 10.9%, respectively. However, N, Ca²⁺, and Mg²⁺ absorption rates slightly increased in the QNM treatment. The nutrient wastes generated per kilogram of produced fruits were also reduced by 66.4%, 60.7%, and 30.2% for N, Ca²⁺, and SO₄²⁻, respectively. Although the QNM technique reduced the plant’s leaf area, it significantly increased its total length by 9.4%. The total and marketable yields were not significantly different between the ECM (9.0 and 8.0 kg plant⁻¹) and QNM (9.1 and 8.2 kg plant⁻¹) treatments. However, the QNM treatment produced the highest total dry matter of 617 g plant⁻¹, surpassing the ECM treatment by 6.9%. On the other hand, differences in nutrient management methods did not significantly affect fruit quality, including total soluble solids, water content, skin color, size, and shape. These results suggest that with the QNM method, it is possible to produce quality cucumbers with high nutrient use efficiency while protecting the environment from nutrient wastes.

Plant factories with artificial lighting (PFALs), with well-insulated and airtight structures, enable the production of large quantities of high-quality plants year-round while achieving high resource use efficiency. However, despite the controlled environment in PFALs, variations in plant individuals have been found, which affect productivity in PFAL operations. Plant phenotyping plays a crucial role in understanding how the surrounding microenvironment affects variations in plant phenotypes. In the current study, a modular phenotyping system for seedling production was developed, focusing on practicality and scalability in commercial PFALs. Experiments on seedlings, which strongly affect productivity, were conducted to obtain cotyledon unfolding time and the time series projected area of cotyledons and true leaves of individual seedlings of romaine lettuce (Lactuca sativa L. var. longifolia), using RGB images. This was also undertaken to analyze how the surrounding microenvironment of photosynthetic photon flux densities and nutrients affect growth variations for plant cohort research. In agreement with the actual measurements, variations in seedling growth were identified even under similar microenvironments. Furthermore, the results demonstrated larger variations in seedlings with higher relative growth. Aiming for simplified interactions of phenotypes with the microenvironment, management, and genotype, seedling selection and breeding with plant production in PFALs may enable plant uniformity and higher productivity.

Photoplethysmography is a widely used technique to noninvasively assess heart rate, blood pressure, and oxygen saturation. This technique has considerable potential for further applications—for example, in the field of physiological and mental health monitoring. However, advanced applications of photoplethysmography have been hampered by the lack of accurate and reliable methods to analyze the characteristics of the complex nonlinear dynamics of photoplethysmograms. Methods of nonlinear time series analysis may be used to estimate the dynamical characteristics of the photoplethysmogram, but they are highly influenced by the length of the time series, which is often limited in practical photoplethysmography applications. The aim of this study was to evaluate the error in the estimation of the dynamical characteristics of the photoplethysmogram associated with the limited length of the time series. The dynamical properties were evaluated using recurrence quantification analysis, and the estimation error was computed as a function of the length of the time series. Results demonstrated that properties such as determinism and entropy can be estimated with an error lower than 1% even for short photoplethysmogram recordings. Additionally, the lower limit for the time series length to estimate the average prediction time was computed.

We propose a platform called “LCA-Multidimensional Map (LAMP)” to support companies and individuals aiming for CO₂ zero emissions (CZEs) by efficiently conducting life cycle assessments (LCAs) of their products and activities, collecting information necessary for CZEs, and developing CO₂ reduction plans. LAMP is a multidimensional platform that supports the development of CZEs targets in cyberspace without temporal or spatial constraints. Using this software, LCAs can be conducted using existing database groups constructed in two and three dimensions in various sectors as well as data groups with temporal information, such as evolving and continuous records, and spatial information in a cross-sectional manner. Furthermore, based on the LCA results, CO₂-emission-reduction plans can be formulated (extraction of alternatives), and their effectiveness can be confirmed through LCA again. As an example of how to use LAMP, we introduce the characteristics of LCA in horticulture, the second-largest source of CO₂ emissions after livestock in the agricultural sector, along with examples of alternative plans for greenhouses, collaboration plans with other sectors, and basic methods for promoting CZEs in horticulture. Although this concept needs to be tested and validated in the future, it might encourage individuals or companies to cooperate in LAMP development or inspire them to advocate for more progressive ideas.