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.

No abstract

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 (gsw) is a comprehensive indicator of plants, soil moisture, and atmospheric conditions, this study used gsw to design a conceptual system by employing factors affecting gsw as the key for decision-making. Logistic regression was performed with independent variables (i.e., temperature (Tair), vapor pressure deficit (VPD), and leaf–air temperature difference) to predict the gsw status. When the gsw 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, Tair, VPD, and PPFD, and the overall error rate to characterize gsw was below 13.36%.

Herbs are rich in the active ingredients of drugs for preventing or treating various disorders. However, conventional bioactivity-guided separation is time and labor-intensive and neglects the additive effect of multiple components. These problems hinder the development of new medicines from natural products. This study established a chemometric analysis method that integrates processes based on the spectrum-effect relationship for the rapid identification of the primary active components of a plant. The high-performance liquid chromatography (HPLC) fingerprints of 171 Salvia miltiorrhiza extracts (SMEs) with varied constituent profiles were analyzed. Chemometric analysis was performed to establish an HPLC fingerprint–bioactivity relationship to explore the components of SMEs that contribute to the antioxidant activity and cytotoxicity effect, respectively. The results indicated that the developed strategy can be used to identify components largely contributing to particular bioactivities and re-evaluate the efficacy of previously neglected components. The present study identified not only the primary active components of S. miltiorrhiza but also the optimal ratios of constituents, validating the method for use in the future investigation and development of herbal medicines. Keywords: Chemometric, herb, antioxidant, cytotoxicity, fingerprint, Salvia miltiorrhiza.

Regular water management is crucial for the cultivation of tomato (Solanum lycopersicum L.). Inadequate irrigation leads to water stress and a reduction in tomato yield and quality. Therefore, it is important to develop an efficient classification method of the drought status of tomato for the timely application of irrigation. In this study, a simple classification and regression tree (CART) model that includes air temperature, vapor pressure deficit, and leaf–air temperature difference was established to classify the drought status of three tomato genotypes (i.e., cherry type ‘Tainan ASVEG No. 19’, large fruits breeding line ‘108290’, and wild accession ‘LA2093’). The results indicate that the proposed CART model exhibited a higher predictive sensitivity, specificity, geometric mean, and accuracy performance compared to the logistic model. In addition, the CART model was applicable not only to three tomato genotypes but across vegetative and reproductive stages. Furthermore, while the drought status was divided into low, medium, and high, the CART model provided a higher predictive performance than that of the logistic model. The results suggest that the drought status of tomato can be accurately classified by the proposed CART model. These results will provide a useful tool of the regular water management for tomato cultivation.