Mathematical modeling has been used to describe the characteristics of crop growth. Establishing a growth model can help to better understand the responses of crops to their environment and improve the efficiency of agricultural production. This study establishes empirical growth models to predict the growth of greenhouse tomato. In this study, we collected beef tomato (Solanum lycopersicum cv. ‘993′) growth data over two crop seasons in Taiwan and established growth models by employing the commonly used Gompertz and Logistic curves. Days after transplanting (DAT) and growing degree-days (GDD) were introduced as independent variables and their relationships with five traits, i.e., plant height, leaf area index, stem dry matter, leaves dry matter, and fruits dry matter were determined. The performances of GDD models were slightly better than those of the DAT models. In addition, we inferred five critical points with biological meaning based on the proposed growth models. The critical points estimated by the Logistic model are closer to our expectation than those of the Gompertz model, and they were applicable for the ‘993′ tomato in Taiwan. These results can be used to predict tomato growth and adjust the fieldwork schedule to improve the efficiency of the greenhouse production of tomatoes.

The rise in extreme weather events due to climate change challenges the balance of supply and demand for high-quality agricultural products. In Taiwan, greenhouse cultivation, a key agricultural method, faces increasing summer temperatures and higher operational costs. This study presents the innovative AI-powered greenhouse environmental control system (AI-GECS), which integrates customized gridded weather forecasts, microclimate forecasts, crop physiological indicators, and automated greenhouse operations. This system utilizes a Multi-Model Super Ensemble (MMSE) forecasting framework to generate accurate hourly gridded weather forecasts. Building upon these forecasts, combined with real-time in-greenhouse meteorological data, the AI-GECS employs a hybrid deep learning model, CLSTM-CNN-BP, to project the greenhouse’s microclimate on an hourly basis. This predictive capability allows for the assessment of crop physiological indicators within the anticipated microclimate, thereby enabling preemptive adjustments to cooling systems to mitigate adverse conditions. All processes run on a cloud-based platform, automating operations for enhanced environmental control. The AI-GECS was tested in an experimental greenhouse at the Taiwan Agricultural Research Institute, showing strong alignment with greenhouse management needs. This system offers a resource-efficient, labor-saving solution, fusing microclimate forecasts with crop models to support sustainable agriculture. This study represents critical advancements in greenhouse automation, addressing the agricultural challenges of climate variability.

Background: Unintentional introgression from genetically modified (GM) oilseed rape (Brassica napus) to a relative is inevitable in the open field. A feasible and practical strategy for restricting the spread of GM offspring is to set a reasonable isolated distance between GM B. napus and the relatives. To define the isolated distance, a pollen donor/recipient pair is a prerequisite to conducting the field trial of pollen flow. However, because the cultivation of GM B. napus is prohibited in Taiwan, it is difficult to obtain relevant information. Thus, this study explored the morphological and genetic characteristics of five varieties of B. napus (donor), three varieties of B. rapa (recipient), and the 15 corresponding F₁ hybrids, aiming to construct phenotypic data and genetic variation data and to select the most appropriate pollen donor/recipient for future field trials of pollen flow. Results: The genome size of all F₁ hybrids estimated using flow cytometry showed intermediate DNA content between B. napus and B. rapa varieties. Most of the F₁ hybrids had intermediate plant height and blooming period, and the rosette leaves type and colors resembled those of B. napus varieties. The results of sequence-related amplified polymorphism (SRAP) showed an average of 9.52 bands per primer combination and 67.87 polymorphic bands among the F₁ hybrid population. Similarity and cluster analyses revealed higher similarity between F₁ hybrids and B. napus varieties than between F₁ hybrids and B. rapa varieties. Furthermore, we identified a specific 1100-bp band (LOC106302894) in F₁ hybrids and B. napus varieties but not in B. rapa varieties. Conclusions: The rosette leaves and the DNA marker LOC106302894 observed in F₁ hybrids are consistent phenotypic and genetic characteristics that can be used to identify the presence of unintentional hybridization from B. napus to B. rapa in Taiwan. Due to the prohibition of GM crop cultivation, the hybridization system of non-GM Brassica species in this study can be utilized as a mimic scheme to conduct pollen flow trials, thus facilitating the determination of the proper isolated distance.