Detection of the early stages of stress is crucial in stabilizing crop yields and agricultural production. The aim of this study was to construct a nondestructive and robust method to predict the early physiological drought status of the tomato (Solanum lycopersicum); for this purpose, a convolutional neural network (CNN)-based model with a one-dimensional (1D) kernel for fitting the visible and near infrared (Vis/NIR) spectral data was proposed. To prevent degradation and enhance the feature comprehension of the deep neural network architecture, residual and global context modules were embedded in the proposed 1D-CNN model, yielding the 1D spectrogram power net (1D-SP-Net). The 1D-SP-Net outperformed the 1D-CNN, partial least squares discriminant analysis (PLSDA), and random forest (RF) models in model testing, demonstrating an accuracy of 96.3%, precision of 98.0%, Matthew’s correlation coefficient of 0.92, and an F1 score of 0.95. Furthermore, when employing various synthesized imbalanced data sets, the proposed 1D-SP-Net remained robust and consistent, outperforming the other models in terms of the prediction capabilities. These results indicate that the 1D-SP-Net is a promising model resistant to the effects of imbalanced data sets and able to determine the early drought stress status of tomato seedlings in a non-invasive manner.

Early detection of drought stress in greenhouse tomato (Solanum lycopersicum) is an important issue. Real-time and nondestructive assessment of plant water status is possible by spectroscopy. However, spectral data often suffer from the problems of collinearity, class imbalance, and class overlap, which require some effective strategies to overcome. This study used a spectroscopic dataset on the tomato (cv. ‘Rosada’) vegetative stage and calculated ten spectral reflectance indices (SRIs) to develop an early drought detection model for greenhouse tomatoes. In addition, this study applied the random forest (RF) algorithm and two resampling techniques to explore efficient methods for analyzing multiple SRI data. It was found that the use of the RF algorithm to build a prediction model could overcome collinearity. Moreover, the synthetic minority oversampling technique could improve the model performance when the data were imbalanced. For class overlap in high-dimensional data, this study suggested that two to three important predictors can be screened out, and it then used a scatter plot to decide whether the class overlap should be addressed. Finally, this study proposed an RF model for detecting early drought stress based on three SRIs, namely, RNDVI, SPRI, and SR2, which only needs six spectral wavebands (i.e., 510, 560, 680, 705, 750, and 900 nm) to achieve more than 85% accuracy. This model can be a useful and cost-effective tool for precise irrigation in greenhouse tomato production, and its sensor prototype can be developed and tested in different situations in the future.

Most existing greenhouse decision support systems only consider external environmental factors, such as soil and atmosphere, rather than plant response. A conceptual plant-response-based strategy for irrigation and environmental controls for tomato (Solanum lycopersicum) seedling cultivation in greenhouse operations was proposed. Because stomatal conductance (g_sw) is a comprehensive indicator of plants, soil moisture, and atmospheric conditions, this study used g_sw to design a conceptual system by employing factors affecting g_sw as the key for decision-making. Logistic regression was performed with independent variables (i.e., temperature (T_air), vapor pressure deficit (VPD), and leaf–air temperature difference) to predict the g_sw status. When the g_sw status was “low,” the system entered into the environmental control component, which examined whether the VPD and the photosynthetic photon flux density (PPFD) were in the normal range. If the VPD and the PPFD were not in the normal range, the system would offer a suggestion for environmental control. Conversely, when both parameters were in the normal range, the system would determine that irrigation should be performed and the irrigation amount could be estimated by the evapotranspiration model. Thus, the strategy only considered leaf temperature, T_air, VPD, and PPFD, and the overall error rate to characterize g_sw was below 13.36%.

Plant factories offer a promising opportunity for fresh food production due to their minimal land requirements. Among the adjustable factors in the production system of plant factories, light serves as a critical element, significantly influencing both crop yield and quality. Cilantro, a prevalent culinary herb and a traditional flavoring agent, plays a crucial role in Taiwanese gastronomy. This research investigated cilantro plants grown under nine different light treatments with varying red to far-red ratios and green light percentages over a 49-day period. Results demonstrate that maximum fresh and dry biomass accumulation in both shoot and root tissues occurred under treatments with red to far-red ratios of approximately of 1.8 combined with medium green light intensity. Conversely, medium far-red ratios negatively affected lutein and carotenoid concentrations in foliar tissues. Carotenoid biosynthesis exhibited an inverse relationship with green light intensity, with lower green light percentages corresponding to significantly higher carotenoid concentrations. In terms of energy efficiency, a red to far-red ratio of approximately 1.8 yielded the highest energy yield (g kWh⁻¹) and photon yield (g mol⁻¹), indicating optimal energy conversion efficiency under this spectral composition. In conclusion, this study demonstrates that cilantro cultivation under R53G05B13FR29 spectral composition (53% red, 5% green, 13% blue, 29% far-red) with a 49-day production cycle maximizes biomass while optimizing energy utilization efficiency.

Trichomes in Prunus persica (L.) Batsch are crucial specialized structures that play a protective role against both biotic and abiotic stresses. The fruits with and without trichomes are respectively named as peach and nectarine. At the genetic level, the formation of trichome in peach is controlled by a single gene, PpMYB25, at the G locus. Peach (GG or Gg) is dominant to nectarine (gg), but such regulatory role was reported in a small-scale accession. In this study, we performed large-scale genotype and phenotype screening on 295 accessions. Almost all accessions supported the casual relationship between trichome formation and PpMYB25. However, a peach to nectarine mutant, named Maravilha Nectarine Mutant (MN), was discovered to possess a putative functional PpMYB25 gene sequence (Gg) but revealed nectarine phenotype. Comparative transcriptomic analyses revealed that PpMYB25 transcript was absent in MN. Correlation analyses also demonstrated that the PpMYB25-mediated regulatory network was abolished in MN. In summary, our results demonstrated an alternative mechanism beyond genetic regulation on trichome formation.