The Cucumber Productivity Simulation Model (CPSM) was developed to precisely predict the dynamic process of cucumber productivity in a solar greenhouse. This research conducted a variety of sowing experiments and collected data on cucumber productivity and meteorological conditions from 2013 to 2015 and 2018 to 2020. Employing the principles of least squares, the relationship between cucumber productivity indicators and effective accumulated temperature (EAT) was fitted, determining key crop parameters and constructing the CPSM. Validation of the model was conducted using independent experimental data, evaluating its simulation accuracy. The results indicate that (1) CPSM can dynamically and meticulously simulate the formation process of different productivity indicators in cucumber. Normalized root mean square errors (NRMSE) ranged from 0.44% to 19.64%, and mean relative errors (MRE) ranged from 0.31% to 17.23% across different productivity indicator models. The models for organ water content, maximum root length, specific leaf area, and organ fresh weight distribution index demonstrated high simulation accuracy, while others showed relatively high accuracy. (2) Simulation accuracy varied with indicators and varieties. 19 indicators (34.55%) exhibited high simulation accuracy and 30 indicators (54.55%) showed relatively high accuracy. The JY35 variety (10.44 ± 8.49%) outperformed the JS206 variety (13.44 ± 8.50%) in terms of simulation accuracy. The JY35 variety had 39 superior productivity indicators (70.91%) while the JS206 variety had sixteen (29.09%). CPSM utilizes easily accessible temperature data as its input, allowing for precise and detailed simulation of productivity indicators for cucumber production in solar greenhouses. This research lays a theoretical foundation and provides technical support for guiding intelligent production management, efficient utilization of agricultural resources, and climate change productivity assessment in solar greenhouse cucumber production.

Rice is a staple grain crop extensively cultivated in Fujian Province, China. This study examined the impact of high-temperature stress on rice yield and its components, focusing on four representative rice varieties, including early and middle rice grown in Fujian Province. Results indicate significant yield losses, with the most severe reduction of 60.8% observed during the flowering stage of early rice and over 40% during the meiosis and flowering stages of middle rice. High-temperature stress primarily affects early rice yield more at the flowering stage than at the grain-filling stage, whereas in middle rice, it is more severe at the meiosis stage than at the flowering stage. Leveraging historical climatic data spanning the past 20 years, a simulation model for high-temperature stress on rice yield was developed to assess disaster-induced yield loss rates, aiming to enhance prevention and disaster damage assessment for rice under high-temperature stress. Application of the model to four rice planting sites in Fujian Province revealed contrasting temporal changes between loss rates and meteorological yield, with middle rice experiencing more severe damage than early rice. The model’s effectiveness is validated by the strong correspondence between yield loss rate and meteorological yield across different regions, highlighting its robust simulation capabilities.

This project aims to improve the wheat growth and development simulation model (WheatSM) V4.0, a renowned wheat model, by addressing limitations in its structure and modules. The WheatSM V4.0 excelled numerically but lacked modularity, hindering maintenance, improvement, and secondary development. Therefore, the project undertook a software framework redesign, adopting a modular approach and implementing WheatSM V5.0 entirely in Python. Furthermore, the project conducted a sensitivity analysis of model parameters. Additionally, WheatSM V5.0 was seamlessly integrated into AgroStudio, an agricultural model system integration platform, enabling the provision of online cloud services. The Morris analysis indicated that photoperiod parameters significantly impacted the jointing and mature stages. Furthermore, biomass was highly sensitive to p_max (the maximum photosynthetic intensity at light saturation point), while yield was influenced by tr1 (the transfer rate of photosynthate to grain before heading). The simulated results demonstrated favorable performance in soil water storage, soil nitrate nitrogen content, winter wheat nitrogen accumulation, the development period, biomass, and yield. The NRMSE ranged from 1.2% to 15.1% for calibration and 1.0% to 18.7% for validation. The project successfully transformed WheatSM into a cloud-based service on AgroStudio, migrating from a PC-based application. Generally, this enhanced model exhibits potential for climate change assessment, wheat production optimization, and digital design.

This study investigated how nitrogen fertilizer management influences the yield and grain quality of two rice cultivars, Nanjing 9108 (a late-maturing medium japonica) and Ming 1 Youzhenzhan (a three-line indica hybrid). Three field experiments were conducted to assess different nitrogen application strategies, including total nitrogen reduction, panicle-stage nitrogen reduction, and substitution of chemical fertilizer with compound fertilizer. The experiments analyzed changes in rice nitrogen uptake and transport efficiency, yield and its components, and quality and its components under different nitrogen application treatments, and fitted a function based on the effect of total nitrogen fertilizer on quality. Taking into account four quality indicators and ensuring yield reduction is less than 30%, replacing 100% chemical fertilizer with compound fertilizer in Nanjing 9108 was the optimal strategy for significantly improving quality. Reducing panicle fertilizer by 50% or omitting it also significantly improved quality. Replacing chemical fertilizers with 50% compound fertilizer also maintained nutritional quality. Reducing total nitrogen fertilizer by 25% in Ming 1 Youzhenzhan was the optimal strategy for significantly enhancing quality. Replacing chemical fertilizer with 100% compound fertilizer significantly improved quality. Reducing panicle fertilizer by 50% and substituting 50% of chemical fertilizer with compound fertilizer also significantly improved quality. Taking into account the synergistic effects of nitrogen application measures on rice yield and quality, the performance of the two rice varieties is not identical. The results showed that nitrogen fertilizer management significantly affected both yield and quality, with responses varying between the two varieties. But the two varieties can ensure optimal quality under yield conditions by reducing panicle fertilizer application by 50% and replacing chemical fertilizers with compound fertilizers.

In the context of the rapid transformation of global agricultural production towards intensification and intelligence, the precise and intelligent management of horticultural crop production processes is key to enhancing resource utilization efficiency and industry profitability. Crop growth and development models, as digital representations of the interactions between environment, crops, and management, are core tools for achieving intelligent decision-making in facility production. This paper provides a comprehensive review of the advancements in intelligent management models and systems for horticultural crop growth and development. It introduces the developmental stages of horticultural crop growth models and the integration of multi-source data, systematically organizing and analyzing the modeling mechanisms of crop growth and development process models centered on developmental stages, photosynthesis and respiration, dry matter accumulation and allocation, and yield and quality formation. Furthermore, it summarizes the current status of expert decision-support system software development and application based on crop models, achieving comprehensive functionalities such as data and document management, model parameter management and optimization, growth process and environmental simulation, management plan design and effect evaluation, and result visualization and decision product dissemination. This illustrates the pathway from theoretical research to practical application of models. Addressing the current challenges related to the universality of mechanisms, multi-source data assimilation, and intelligent decision-making, the paper looks forward to future research directions, aiming to provide theoretical references and technological insights for the future development and system integration of intelligent management models for horticultural crop growth and development.

The quantification of the relationship between morphological and color indicators in various organs of horticultural crops is of great significance for crop digital visualization research using computer vision technology. To study this relationship, observational data from a six-year experiment were collected, focusing on seven kinds of color component values of different organs including root, stem, and leaf. Using the collected color data as input, a simulation model was established based on the Elman neural network for six horticultural crops including zizania, cucumber, celery, spinach, parsley, and tea. Results indicated that the horticultural crop morphology model based on the Elman neural network exhibited high simulation accuracy with root mean square error (RMSE) ranging from 0.14 to 1.05 cm and normalized root mean square error (NRMSE) ranging from 2.02% to 11.34% for the maximum root length simulation model. The simulation model for stem length and diameter had an RMSE ranging from 1.42 to 4.96 cm and 0.25 to 1.17 mm, respectively, with NRMSE ranging from 18.19% to 25.65% and 15.13% to 27.25%, respectively. Similarly, chlorophyll content, leaf length, leaf width, and leaf area simulation models exhibited RMSE ranging from 2.80 to 8.22 SPAD, 0.44 to 18.04 cm, 0.22 to 3.49 cm, and 0.25 to 36.39 cm 2 , respectively, with NRMSE ranging from 8.63% to 21.04%, 15.00% to 22.87%, 15.12% to 33.58%, and 6.88% to 24.90%, respectively. These findings provide essential theoretical support for precision agriculture in areas of water and fertilizer management, plant growth diagnosis, and yield prediction.