Coordination Control of Greenhouse Environmental Factors

Optimal control of greenhouse climate is one of the key techniques in digital agriculture.Greenhouse climate,a nonlinear and uncertain system,consists of several major environmental factors such as temperature,humidity,light intensity,and CO 2 concentration.Due to the complex coupled correlations,it is a challenge to achieve coordination control of greenhouse environmental factors.This paper proposes a model-free coordination control approach for greenhouse environmental factors based on Q-learning.Coordination control policy is found through systematic interaction with the dynamic environment to achieve optimal control for greenhouse climate with the control cost constraints.In order to decrease systematic trial-and-error risk and reduce the computational complexity in Q-learning algorithm,case-based reasoning （CBR） is seamlessly incorporated into the Q-learning process.The experimental results demonstrate that this approach is practical,highly effective and efficient.

A Design of Modern Greenhouse Environmental Monitoring System

Based on the problems in the current greenhouse environmental monitoring system such as difficult connection layout,low flexibility and high costs,this paper builds the greenhouse environmental monitoring system based on wireless sensor network,and designs the sensor nodes and gateway nodes. The sensor nodes of this system are responsible for collecting environmental parameters and sending the data to gateway nodes via wireless sensor network. And the gateway nodes transmit the data to the remote monitoring platform. The microprocessor module of node hardware uses MSP430F149 microprocessor for data processing and control; wireless communication module consists of nRF905 RF chip and peripheral circuit,responsible for transmitting and receiving data; sensor module uses AM2301 sensor for data measurement; the power supply module uses the circuit consisting of LT1129-3. 3,LT1129-5 and Max660 to provide 3. 3 and ± 5V power. The C language development is employed for wireless routing protocol of node and time synchronization algorithm,to achieve node data acquisition and processing,rule forwarding and remote transmission. Remote monitoring software uses NET. ASP,HTML and C# development to provide visual WEB mode remote data management platform for users. The system goes through networking testing in greenhouse in Xining City,and test results show that the system operation is stable and reliable,and the average network packet loss rate is 2. 4%,effectively solving the problems in greenhouse environmental monitoring system and meeting the application requirements of greenhouse cultivation environmental monitoring.

Numerical investigation for effects of natural light and ventilation on 3D tomato body heat distribution in a Venlo greenhouse

Maintaining suitable temperature level around tomato in the greenhouse is essential for the high-quality production.However,in summer,the temperature level around the tomato is usually unclear except using a high-precision temperature imager.To solve this problem,thermal performance of 3D(three-dimensional)tomato model built based on SolidWorks was investigated by the computational fluid dynamics(CFD)simulations.To assess the effect of temperature distribution around the tomato,a simplified 3D tomato numerical model was firstly validated by a set of field measurement data.The light intensity and indoor ventilation were regarded as the mainly environment factors in the Venlo greenhouse,thermal stratification around tomatoes at different time of day was further studied.The numerical results illustrated the different temperature distribution around tomato body under different radiation intensity.It was found that ventilation could obviously adjust the temperature gradient around the tomato,and alleviate high temperature effect particularly in summer.Suitable ventilation could create a suitable thermal environment for the tomato growth.This study clearly demonstrated 3D temperature distribution around tomatoes,which is beneficial to provide the reference for accurate detection of 3D tomato temperature and appropriate thermal environment design.

Investigation on environment monitoring system for a combination of hydroponics and aquaculture in greenhouse

This work develops a distributed environmental monitoring system for the combination of hydroponics and aquaculture based on the internet of things technology,which mainly includes the information perception layer,the information transmission layer and the sys-tem architecture.The system has employed multiple sensors terminal to real-time acqui-sition,including air and water temperatures,dissolved oxygen etc.LoRa protocol is suitable for sending small data and the 4G was employed to collect data and send to the cloud plat-form.Java is used to develop background applications,to access cloud platforms and local data processing.Based on the collection and processing of environmental data and cloud service platform,the mobile application program client and remote login desktop have been developed.It has been implemented and tested in Tongzhou,Beijing for 3 months in 2020.The results showed the proposed monitoring system stability for overall operation and accuracy data transmission,which can support the actual hydroponics and aquacul-ture production management.After analysis of monitoring data collected from the devel-oped monitoring system,indoor air and water temperature have the obvious correlation with atmospheric pressure(0.7 and 0.9)and outdoor temperature(1.0 and 0.9),respectively.

Overview of modelling techniques for greenhouse microclimate environment and evapotranspiration

Domestication of plants by man through greenhouse crop production has revolutionized agricultural farming systems worldwide.Selecting the appropriate greenhouse technology together with the user-friendly evapotranspiration(ETc)model can optimize crop water use.The greenhouse microclimate environment has nearly zero wind speed and low radiation,hence low transpiration due to high temperature and humidity.Therefore,matching the greenhouse microclimate with the appropriate ETc model will certainly optimize crop water use efficiency since water is becoming a scarce resource globally,more so in the greenhouse environment.This is one of the main reasons why the gap between the dissemination of various advanced ETc models and the application by the greenhouse crop producers’community needs to be bridged.The likelihood or chances of rapidly disseminating and adopting advances in ETc estimating technology by a larger greenhouse crop producers community will increase if greenhouse ETc models become more user-friendly and available.The contribution of the greenhouse system to increased and sustainable food production must come through improved disseminating,adopting and use of existing greenhouse ETc models.FAO recommends a standard approach for the determination of crop water requirements utilizing the product of reference evapotranspiration(ET0)and crop coefficient(Kc)values.The FAO approach can also be used in greenhouse cultivation systems.However,studies connecting greenhouse technologies and methodologies for measuring ET0 or ETc in greenhouses are not available.There are also few studies undertaken that compared the performance of ET0 or ETc models under different categories of greenhouse conditions.In this review,a link between greenhouse technology and ET0 model or ETc model,and how existing knowledge and methodologies in ET0 or ETc measurements can actually enhance the sustainability of greenhouse farming have been highlighted.The categories of greenhouses,equipment commonly used,and the data collected for ET0 and ETc measurements have been established in the article.This review aimed to evaluate and summarize ET0 and ETc models currently available and being used in the various greenhouse categories.The accuracy assessment levels of the ET0 models about the category of the greenhouse microclimate environment were carried out.

A state of art review on time series forecasting with machine learning for environmental parameters in agricultural greenhouses

Agricultural greenhouse production has to require a stable and acceptable environment,it is therefore essential for future greenhouse production to obtain full and precisely internal dynamic environment parameters.Dynamic modeling based on machine learning methods,e.g.,intelligent time series prediction modeling,is a popular and suitable way to solve the above issue.In this article,a systematic literature review on applying advanced time series models has been systematically conducted via a detailed analysis and evaluation of 61 pieces selected from 221 articles.The historical process of time series model application from the use of data and information strategies was first discussed.Subsequently,the accuracy and generalization of the model from the selection of model parameters and time steps,providing a new perspective for model development in this field,were compared and analyzed.Finally,the systematic review results demonstrate that,compared with traditional models,deep neural networks could increase data structure mining capabilities and overall information simulation capabilities through innovative and effective structures,thereby it could also broaden the selection range of environmental parameters for agricultural facilities and achieve environmental prediction end-to-end optimization via intelligent time series model based on deep neural networks.