FSPM-P： towards a general functional-structural plant model for robust and comprehensive model development

In the last decade, functional-structural plant modelling （FSPM） has become a more widely accepted paradigm in crop and tree production, as 3D models for the most important crops have been proposed. Given the wider portfolio of available models, it is now appropriate to enter the next level in FSPM development, by introducing more efficient methods for model development. This includes the consideration of model reuse （by modularisafion）, combination and comparison, and the enhancement of existing mod- els. To facilitate this process, standards for design and communication need to be defined and established. We present a first step towards an efficient and general, i.e., not speciesspecific FSPM, presently restricted to annual or bi-annual plants, but with the potential for extension and further generalization. Model structure is hierarchical and object-oriented, with plant organs being the base-level objects and plant individual and canopy the higher-level objects. Modules for the majority of physiological processes are incorporated, more than in other platforms that have a similar aim （e.g., photosynthesis, organ formation and growth）. Simulation runs with several general parameter sets adopted from the literature show that the present prototype was able to reproduce a plausible output range for different crops （rapeseed, barley, etc.） in terms of both the dynamics and final values （at harvest time） of model state variables such as assimilate production, organ biomass, leaf area and architecture.

Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests

Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.

Choice of the Best Production Prediction Model for the Zagtouli Solar Power Plant in Burkina-Faso

In this paper, we present a study on the prediction of the power produced by the 33 MWp photovoltaic power plant at Zagtouli in Burkina-Faso, as a function of climatic factors. We identified models in the literature, namely the Benchmark, input/output, Marion, Cristo-fri, Kroposki, Jones-Underwood and Hatziargyriou prediction models, which depend exclusively on environmental parameters. We then compared our linear model with these seven mathematical models in order to determine the most optimal prediction model. Our results show that the Hatziargyriou model is better in terms of accuracy for power prediction.

A Functional-Structural Model of Rice Seedling Coupled with Nitrogen Metabolism

With the ability of representing the association and inner-feedback between plant morphological structure and physiological functions, functional-structural plant modeling （FSPM） approach has been used in many works, trying to better understand the mechanisms of integrating plant functions and its structure, and their communication with environmental factors. To do so, an FSPM of rice seedling was developed in this study, including structural morphogenetic model, photosynthetic model and biomass partitioning module. It can thus describe the developmental course of the rice structure dynamically based on the processes of biomass producing and partitioning. Furthermore, the processes of nitrogen metabolism, which influence the N content and growth dynamics of the virtual rice, were also considered. The model was developed with L-system on a platform established with Java programming language, which took over the parsing and visualization of the L-system strings to 3D objects using Java 3D extended library. The physiological processes in the model can be modified and further improved to gradually meet the needs for modeling the whole life cycle of rice, e.g., considering the respiration, and interaction with other environmental factors like CO2, temperature, etc.. The model was developed to provide a platform to systematically study and understand how plant systems like rice seedling work. The model and the virtualization platform can be expanded to provide decision support on N fertilizer application for the rice seedling and the other crops.

Tomato detection method using domain adaptive learning for dense planting environments

This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.

Business Model Analysis of Shunxiang Hazelnut Planting Cooperative Using the Business Model Canvas

Agricultural cooperatives serve as typical entities within the agricultural sector of China and encounter common representative challenges during their growth.This study employs the business model canvas theory to analyze the profit and business models of the Shunxiang Hazelnut Planting Cooperative while clarifying its ongoing developmental hurdles.By addressing pivotal concerns the paper puts forth branding suggestions and outlines future developmental directions.

Quantification of CO_(2) Emissions from Three Power Plants in China Using OCO-3 Satellite Measurements

Coal-fired power plants are a major carbon source in China. In order to assess the evaluation of China's carbon reduction progress with the promise made on the Paris Agreement, it is crucial to monitor the carbon flux intensity from coal-fired power plants. Previous studies have calculated CO_(2) emissions from point sources based on Orbiting Carbon Observatory-2 and-3(OCO-2 and OCO-3) satellite measurements, but the factors affecting CO_(2) flux estimations are uncertain. In this study, we employ a Gaussian Plume Model to estimate CO_(2) emissions from three power plants in China based on OCO-3 XCO_(2) measurements. Moreover, flux uncertainties resulting from wind information, background values,satellite CO_(2) measurements, and atmospheric stability are discussed. This study highlights the CO_(2) flux uncertainty derived from the satellite measurements. Finally, satellite-based CO_(2) emission estimates are compared to bottom-up inventories.The satellite-based CO_(2) emission estimates at the Tuoketuo and Nongliushi power plants are ~30 and ~10 kt d^(-1) smaller than the Open-Data Inventory for Anthropogenic Carbon dioxide(ODIAC) respectively, but ~10 kt d^(-1) larger than the ODIAC at Baotou.

Effect of dry-wet cycles on dynamic properties and microstructures of sandstone:Experiments and modelling

Underground pumped storage power plant(UPSP)is an innovative concept for space recycling of abandoned mines.Its realization requires better understanding of the dynamic performance and durability of reservoir rock.This paper conducted ultrasonic detection,split Hopkinson pressure bar(SHPB)impact,mercury intrusion porosimetry(MIP),and backscatter electron observation(BSE)tests to investigate the dynamical behaviour and microstructure of sandstone with cyclical dry-wet damage.A coupling FEM-DEM model was constructed for reappearing mesoscopic structure damage.The results show that dry-wet cycles decrease the dynamic compressive strength(DCS)with a maximum reduction of 39.40%,the elastic limit strength is reduced from 41.75 to 25.62 MPa.The sieved fragments obtain the highest crack growth rate during the 23rd dry-wet cycle with a predictable life of 25 cycles for each rock particle.The pore fractal features of the macropores and micro-meso pores show great differences between the early and late cycles,which verifies the computational statistics analysis of particle deterioration.The numerical results show that the failure patterns are governed by the strain in pre-peak stage and the shear cracks are dominant.The dry-wet cycles reduce the energy transfer efficiency and lead to the discretization of force chain and crack fields.

Prediction and Analysis of Total Nitrogen in a Sewage Treatment Plant Effluent

Total nitrogen was an important indicator for characterizing eutrophication of polluted water. Although the use of water quality online monitoring instrument can monitor water quality changes in real time, the degree of intelligence was low, so it was urgent to predict the water quality and take precautions in advance. A predictive model for total nitrogen levels in a sewage treatment plant utilizing the Anaerobic-Anoxic-Oxic (AAO) process was investigated in this paper. This model demonstrated significant practical application value. Based on the ARIMA (Autoregressive Integrated Moving Average) model and taking into account the impact of Biochemical Oxygen Demand (BOD), a prediction model for effluent total nitrogen was developed. However, the initial results exhibited significant deviations. To address this issue, seasonal factors were further considered. Then, the dataset was divided into winter and Non-winter sub-samples, leading to a reconstruction of the prediction model. Additionally, in developing the Non-winter prediction model, life cycle considerations were incorporated, and consequently, a SARIMA (Seasonal Autoregressive Integrated Moving Average) model was established. The predicting deviation associated with both the winter and Non-winter forecasting models showed a significant reduction.

Construction of Early-warning Model for Plant Diseases and Pests Based on Improved Neural Network

By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particle swarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.

Virtual Plant Modeling Based on Mutual Feedback of Function-structure

[Objective] To study virtual plant modeling based on mutual feedback of function-structure.[Method] With the analysis of the shortcomings of current virtual plant modeling method,the modeling with the idea of function-structure mutual feedback was put forward,and the steps of this modeling were elaborated,including the determination of morphological structure model,biomass production model,biomass allocation model,organ reconstruction model,and the integration method of function model and morphological structure model.[Results] The breakthrough of function-structure mutual feedback based mechanism from the boundaries of physiological ecology model and morphological structure model can solve the difficulty of data transmission between the two models and build an integrated model from the two,which can effectively reflect the incidence relation between plant morphology and function,and more suitable for the growth mechanisms of plants.This modeling approach has significant advantages in the dynamic simulation of plant growth.[Conclusion] The virtual plant modeling based on function-structure mutual feedback provides basis for the simulation of plant growth status in the next stage,and has important significance for the accurate simulation of the dynamic growth process of plant.

Analysis of Genetic Effects of Nuclear-Cytoplasmic Interaction on Quantitative Traits:Genetic Model for Diploid Plants

A genetic model was proposed for simultaneously analyzing genetic effects of nuclear, cytoplasm, and nuclear-cytoplasmic interaction （NCI） as well as their genotype by environment （GE） interaction for quantitative traits of diploid plants. In the model, the NCI effects were further partitioned into additive and dominance nuclear-cytoplasmic interaction components. Mixed linear model approaches were used for statistical analysis. On the basis of diallel cross designs, Monte Carlo simulations showed that the genetic model was robust for estimating variance components under several situations without specific effects. Random genetic effects were predicted by an adjusted unbiased prediction （AUP） method. Data on four quantitative traits （boll number, lint percentage, fiber length, and micronaire） in Upland cotton （Gossypium hirsutum L.） were analyzed as a worked example to show the effectiveness of the model.

MORPHOLOGICAL MODEL FOR RHEOLOGICAL PROPERTIES OF PLANT CELL SUSPENSION CULTURE SYSTEM

This paper puts forward a physical and mathematical model for the rheological properties of a plant cell suspension culture system.The model can explain why the system is pseudoplastic satisfactorily,thus the rheological properties of the system as the effect of the flow behavior index on plant cell concentration are interpreted correctly and the mechanism of the rheological properties of the system is further understood.Therefore the model can be applied in the technological design and optimum conditions of the system and the reformation,evaluation and scale up of reactors.

Mathematical Model and Simulation of Harmful Invasive Plant Control

Plant invasion refers to the phenomenon that some plants grow too fast due to they are far away from the original living environment or predators, affecting the local environment. With the development of tourism and trade, the harm caused by invasive plants will be more and more serious. Therefore, it is necessary to ex- plore an effective method for controlling plant invasion through qualitative and quan- titative research. In this paper, the models were established for the early and late harmful plant invasion control. The huge computation was completed by the com- puter programming to obtain the optimal solutions of the models. The real meaning of the optimal solution was further discussed. Through numerical simulations and discussion, it could be concluded that the quantitative research on the invasive plant control had a certain application value.

对《Plant Growth Modeling and Applications》一书的评介

农业信息技术能够快速完成农业科技、市场和政府等信息的传输和利用,加速科研成果的形成、规范、集成,缩短转化周期,科学指导和管理生产,从而改变传统农业高度分散、生产规模小、时空变异大、规模化程度差、稳定性和可控程度低等行业性弱点,提高农业生产者和管理者的素质和能力.农业信息化近些年来在我国有了快速的发展.2003年12月10日,我国科技部863计划支持的重大专项"智能化农业信息技术应用示范工程"(俗称"电脑农业")荣获世界信息峰会全球大奖,标志着中国在农业专家系统和智能化农业信息技术应用方面取得了重大成就.

Model Selection of Gas Turbine for Large Scale Gas-Fired Combined Cycle Power Plant

This paper briefs the configuration and performance of large size gas turbines and their composed combined cycle power plants designed and produced by four large renown gas turbine manufacturing firms in the world, providing reference for the relevant sectors and enterprises in importing advanced gas turbines and technologies.

Genetic control of flowering time in woody plants： Roses as an emerging model

Genetic control of the timing of flowering in woody plants is complex and has yet to be adequately investigated due to their long life-cycle and difficulties in genetic modification.Studies in Populus,one of the best woody plant models,have revealed a highly conserved genetic network for flowering timing in annuals.However,traits like continuous flowering cannot be addressed with Populus.Roses and strawberries have relatively small,diploid genomes and feature enormous natural variation.With the development of new genetic populations and genomic tools,roses and strawberries have become good models for studying the molecular mechanisms underpinning the regulation of flowering in woody plants.Here,we review findings on the molecular and genetic factors controlling continuous flowering in roses and woodland strawberries.Natural variation at TFL1 orthologous genes in both roses and strawberries seems be the key plausible factor that regulates continuous flowering.However,recent efforts suggest that a two-recessive-loci model may explain the controlling of continuous flowering in roses.We propose that epigenetic factors,including non-coding RNAs or chromatin-related factors,might also play a role.Insights into the genetic control of flowering time variation in roses should benefit the development of new germplasm for woody crops and shed light on the molecular genetic bases for the production and maintenance of plant biodiversity.

Plant Temperature and Its Simulation Model of Thermo-Sensitive Genic Male Sterile Rice

Plant temperature （Tp） and its relations to the microclimate of rice colony and irrigation water were studied using a thermo-sensitive genic male sterile （TGMS） rice line, Pei＇ai 64S. Significant differences in the daily change of temperature were detected between Tp and air temperature at the height of 150 cm （TA）. From 8：00 to 20：00, Tp was lower than TA, but they were similar during 21：00 to next 7：00. The maximum Tp occurred one hour earlier than the maximum TA, though they both reached the minimum at 6：00. Tp fluctuated less than TA. At the same height, during 6：00-13：00, Tp was higher than air temperature （Ta）, and Tp reached the maximum one hour earlier than Ta. During the rest time on sunny day, Tp was close to or even a little lower than Ta. On overcast day, Tp was higher than Ta in the whole day, and both maximized at the same time. In addition, Tp was regulated by solar radiation, cloudage and wind speed in daytime, and by irrigation water at night. The present study indicated that a TA of 29.6℃ was the critical point, at which Tp was increased or decreased by irrigation water. Tp and the difference between water and air temperatures showed a conic relation. Tp fluctuation was also regulated by the absorption or reflection of solar radiation by leaves during daytime and release of heat energy during nighttime. By analysis on correlation and regression simulation, two models of Tp were established.

Numerical Analysis of Stochastic Vector Borne Plant Disease Model

We are associating the solutions of stochastic and deterministic vector borne plant disease model in this manuscript.The dynamics of plant model depends upon threshold number P^(∗).If P^(∗)<1 then condition helpful to eradicate the disease in plants while P^(∗)>1 explains the persistence of disease.Inappropriately,standard numerical systems do not behave well in certain scenarios.We have been proposed a structure preserving stochastic non-standard finite difference system to analyze the behavior of model.This system is dynamical consistent,positive and bounded as defined by Mickens.

Kinetics of zinc sulfide concentrate direct leaching in pilot plant scale and development of semi-empirical model

The direct leaching kinetics of an iron-poor zinc sulfide concentrate in the tubular reactor was examined.All tests werecarried out in the pilot plant.To allow the execution of hydrostatic pressure condition,the slurry with ferrous sulfate and sulfuric acidsolution was filled into a vertical tube(9m in height)and air was blown from the bottom of the reactor.The effects of initial acidconcentration,temperature,particle size,initial zinc sulfate concentration,pulp density and the concentration of Fe on the leachingkinetics were investigated.Results of the kinetic analysis indicate that direct leaching of zinc sulfide concentrate follows shrinkingcore model(SCM).This process was controlled by a chemical reaction with the apparent activation energy of49.7kJ/mol.Furthermore,a semi-empirical equation is obtained,showing that the order of the iron,sulfuric acid and zinc sulfate concentrationsand particle radius are0.982,0.189,-0.097and-0.992,respectively.Analysis of the unreacted and reacted sulfide particles bySEM-EDS shows that insensitive agitation in the reactor causes detachment of the sulfur layer from the particles surface in lowerthan60%Zn conversion and lixiviant in the face with sphalerite particles.