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.

To date, studies on the application of loop-mediated isothermal amplification (LAMP) in the detection of genetically modified organisms (GMOs) are stably increasing and demonstrates LAMP is a potential and promising method for on spot identification of GMOs. However, little information is known for detection of GM potato events by LAMP. In this report, we developed an optimized and visual LAMP assay with high specificity and sensitivity to rapidly amplify genomic DNA of potato EH92-527-1 within 45 min. The limit of detection of LAMP in our study is 10-fold higher than the conventional PCR. Furthermore, LAMP products can be directly observed via naked eyes by addition of SYBR Green I without gel electrophoresis analysis and PCR-based equipment. Therefore, the LAMP assay developed in this paper provides an efficient, convenient and cost-effective tool for the detection of GM potato EH92-527-1.

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%.