This study aimed to estimate the accumulated temperature requirements for phenological changes in Lilium. Eight cultivars of three lily types were cultivated in open field conditions for phenological observations based on floral organ development. Growing degree days (GDD) requirements for phenological changes were calculated and verified using Lilium LA hybrid ‘Serrada’ under greenhouse conditions. Lilium Oriental hybrids exhibited higher GDD requirements compared to Lilium FA and LA hybrids for their phenological development. Estimations of phenological change dates in greenhouse cultivation were accurate within 1–3 days. These results provide a reliable description for predicting lily development stages across diverse cultivation environments by quantifying the accumulated temperature requirements for key phenological events.

This study aims to develop a deep learning model to monitor rice serving amounts in institutional foodservice, enhancing personalized nutrition management. The goal is to identify the best convolutional neural network (CNN) for detecting rice quantities on serving trays, addressing balanced dietary intake challenges. Both a vanilla CNN and 12 pre-trained CNNs were tested, using features extracted from images of varying rice quantities on white trays. Configurations included optimizers, image generation, dropout, feature extraction, and fine-tuning, with top-1 validation accuracy as the evaluation metric. The vanilla CNN achieved 60% top-1 validation accuracy, while pre-trained CNNs significantly improved performance, reaching up to 90% accuracy. MobileNetV2, suitable for mobile devices, achieved a minimum 76% accuracy. These results suggest the model can effectively monitor rice servings, with potential for improvement through ongoing data collection and training. This development represents a significant advancement in personalized nutrition management, with high validation accuracy indicating its potential utility in dietary management. Continuous improvement based on expanding datasets promises enhanced precision and reliability, contributing to better health outcomes.

Mathematically modeling photosynthesis helps to interpret gas exchange in a plant and estimate the photosynthetic rate as affected by environmental factors. Notably, the photosynthetic rate varies among leaf vertical positions within a single plant. The objective of this study was to measure the distinct photosynthetic rate of lily (Lilium Oriental Hybrid ‘Casa Blanca’) at the upper, medium, and basal leaf positions. Subsequently, the FvCB (Farquhar-von Caemmerer-Berry) photosynthesis model was employed to determine the parameters of the model and compared it with a rectangular hyperbola photosynthesis model. The photosynthetic rates were measured at different intracellular CO2 concentrations () and photosynthetic photon flux density (PPFD) levels. SPAD values significantly decreased with lowered leaf position. The photosynthetic rates at the medium and basal leaves were lower compared with the upper leaves. FvCB model parameters,  and   , showed no significant difference between the medium and basal leaves. Estimated photosynthetic rates from derived parameters by the FvCB model demonstrated over 0.86 of R2 compared with measured data. The rectangular hyperbola model tended to overestimate or underestimate photosynthetic rates at high  with high PPFD levels or low  with high PPFD levels, respectively, at each leaf position. These results indicated that the parameters of the FvCB model with different leaf positions can be used to estimate the photosynthetic rate of lily.