Greenhouse Indoor Research Articles

Optimization framework of clean heat and CO2 supply for agricultural greenhouses exploiting industrial symbiosis

The rising demand for greenhouse cultivation poses a challenge in providing environmentally friendly energy to maintain favorable greenhouse indoor conditions. This research presents an optimization model to identify cost-optimal symbiotic pathways to replace the import of three key greenhouse crops (tomato, cucumber, lettuce) with local greenhouse production. The proposed solutions involve greenhouses that can meet these crops' heat and CO2 demands via industrial symbiosis. Switzerland is investigated as the case study, and the (waste) heat suppliers are municipal solid waste incinerators, cement production plants and biogas plants. Upgrading biomethane production plants are also considered potential CO2 suppliers. The objective is to minimize the discounted cost when replacing 25%–100 % of vegetable imports with local agricultural greenhouses, considering availability of suitable land as well as waste heat and CO2 suppliers. Optimization results suggest prioritizing northeast and west Switzerland for greenhouse development, due to the availability of suitable land and proximity of waste heat and CO2 suppliers. Waste incinerators could provide 50%–70 % of the necessary heat, while Organic Rankine cycle in cement plants could generate 63 % of the electricity of supplementary lighting demand of greenhouses. While tailored for Switzerland, the optimization framework's general formulation can be adapted also to other regions to optimize greenhouses' heat and CO2 demands. The optimization code is provided open source for associated applications.

CFD based heat transfer parameter identification of greenhouse and greenhouse climate prediction method

Predicting the greenhouse environment is important for crop yield and energy consumption projections. This study proposes a dynamic greenhouse temperature and humidity modeling method based on integrating CFD simulation and measured data. This method utilizes computational fluid dynamics to analyze the greenhouse's two key heat transfer characteristics. The effect of wind speed and temperature differences on the heat transfer coefficient of the cover is first analyzed under the condition that the inner thermal screen is folded. Secondly, the effect of temperature differences inside and outside the greenhouse, outdoor wind speed, sky temperature, and the internal thermal screen on the heat transfer coefficient between the greenhouse's indoor and outdoor environments is investigated. Based on the thermodynamic parameters of the greenhouse microclimate obtained through CFD simulation, a regression model of the greenhouse climate model parameters with respect to the influencing variables is built using a nonlinear identification algorithm. By analyzing the mechanism of greenhouse environmental changes, this work proposes a universal structure for the agricultural greenhouse climate model and uses identified heat transfer parameters to correct the universal mechanism model of the greenhouse climate. Considering complex environmental dynamics and unmodeled factors, this study proposes a weighted parameter correction method based on adaptive particle swarm optimization (APSO). Finally, the model was validated using climate data from Venlo-type greenhouses in Shanghai. The validation results indicate that the greenhouse climate model can accurately approximate the real greenhouse climate.

Bioaccumulation Characteristics of Typical Perfluoroalkyl Compounds in Alfalfa Under Salt Stress.

Medicago sativa, commonly known as alfalfa, is widely distributed worldwide, known for its strong stress resistance and well-developed root system, making it an important plant in ecological restoration research. To investigate the absorption and transport characteristics of alfalfa for typical perfluoroalkyl substances (PFAS) under salt stress, a 30-day indoor greenhouse experiment was conducted. The results showed that alfalfa exhibited varying degrees of absorption and transport for the selected PFAS. The highest BCF (Bioconcentration Factor) for shoot tissue reached 725.4 (for PFBA), and the highest TF (Translocation Factor) reached 53.8 (for PFPeA). Different PFAS compounds exhibited distinct bioaccumulation behaviors, with short-chain PFAS more readily entering the plant's root system and being transported upwards, while long-chain PFAS tended to adsorb to the surface of the root system. Furthermore, salt stress did not significantly affect the uptake of PFAS by alfalfa. This suggests that alfalfa is salt-tolerant and holds great potential for ecological restoration in short-chain PFAS-contaminated sites.

Transient thermal modelling of a Mediterranean greenhouse for sustainable agriculture: Comparison of desert and dry-summer subtropical climates

Climate change has become increasingly dominant in recent years and threatens food production and security in many areas worldwide. Due to the increase in temperature in some regions, there is a drought problem in agricultural food production, causing issues for food security. Intergovernmental Panel on Climate Change reported that climate change causes a reduction in crop quality and yield changes in farmer livelihoods. Greenhouse agriculture is a common and more protected farming management system than open-field farming. However, the influence of different climate regions on agricultural production in greenhouses should be investigated. In this study, thermal modeling of a greenhouse with and without ventilation is performed to determine the temperature and humidity variations of the greenhouse indoor air variations in selected provinces from Türkiye and Qatar to represent the effects of different climate regions. The impacts of variable climatic conditions on the greenhouse’s indoor and agricultural plants’ temperatures are investigated. Furthermore, implementing a heat pump as heating and cooling equipment in the greenhouse is studied. The yearly average vegetation and indoor temperature, respectively, are determined as 31.41 °C and 30.04 °C for Doha and 21.69 °C and 20.43 °C for Izmir. While the annual average unconditioned indoor temperature is found as 30.04 °C, the average conditioned indoor temperature is determined as 23.67 °C in Doha City. Moreover, the combined climate control mechanisms reduce the number of hours out of the optimum temperature range from 8013 to 1.4 and from 2640 to 3.1 in Doha and Izmir, respectively.

Enhancing soil health and nutrient availability for Carrizo citrange (X Citroncirus sp.) through bokashi and biochar amendments: An exploration into indoor sustainable soil ecosystem management

This study investigated the efficacy of organic soil amendments: bokashi (Bok), biochar (BC), and their combination (Bok_BC) in promoting soil health, nutrient availability, and growth of Carrizo citrange (X Citroncirus sp. Rutaceae, Parentage Citrus sinensis x Poncirus trifoliata) under indoor greenhouse settings. Results indicate significant alterations in soil parameters like total carbon (C), total nitrogen (N), and C:N ratio due to Bok, BC, and Bok_BC treatments. BC treatments boosted total C, while Bok increased total N, compared to controls. A noteworthy 25 % average decrease in C:N ratio was observed with Bok and Bok_BC, nearing the optimal 24:1 C:N for microbial growth. This highlights the potential of waste by-products in balancing nutrient release to benefit soil health and plant development. Analysis of nitrite (NO2-), nitrate (NO3-), and ammonium (NH4-N) levels revealed a dynamic relationship between soil treatments and time. Bok and Bok_BC amendments combined with both fertilizer doses [700 and 1400 Electrical Conductivity, EC] showed an initial NH4-N spike (averaging 1513 and 1288 μg N/g dry, respectively), outperforming control soils (average 503 μg N/g dry). Other key elements like phosphorus, potassium, calcium, and chlorine also experienced initial surges in Bok and Bok_BC soils before declining, suggesting a gradual nutrient release. The concentration of potentially toxic elements remained mostly stable or inconclusive, warranting further exploration. Bok, BC, and Bok_BC treatments considerably influenced germination rate and plant growth. The germination rate averaged 24.2 %, 23 %, and 22.5 % for Bok, BC, and Bok_BC, compared to the 15.9 % control. Plant height increased with Bok, BC, and Bok_BC to 18.4 cm, 18.7 cm, and 16.4 cm, respectively, from the 14.8 cm control. The results remained consistent across fertilizer doses, emphasizing the soil amendments' role in bolstering soil and plant health. In summary, the research underscores the potential of carbon-based amendments like bokashi and biochar in enhancing soil health, reducing reliance on synthetic fertilizers, and fostering sustainable soil ecosystems. The insights are pivotal for advancing sustainable agriculture in indoor greenhouse settings for nursery plant production.

A Self-Adapting IoT Network Configuration Supported by Distributed Graph Transformations

The research described in this article aims to propose the creation of a framework that would enable the self-optimization of IoT device networks. The work is based on two foundations: distributed graph transformations and a flexible IoT network supported by the several standards and definitions proposed by The Open Group, such as the definition of microservices architecture (MSA) as well as IoT and semantic interoperability standards, providing a broader context for the research. It introduces the concept of capabilities, both at the individual device and network levels, which are used to describe the desired functions that will be performed by the given system. The network of distributed IoT devices is visualized as a graph, and graph transformations are used to specify and optimize the network in response to events like degraded performance, failures, or configuration changes. These actions are automatically performed in order to restore the original set of capabilities defined for the system. Validation of these capabilities is used to assess the success rate of the performed actions. The document describes a practical implementation of an IoT network for managing and monitoring an indoor greenhouse. Thanks to the introduction of formal representation of capabilities, the programming effort required to build the system was significantly reduced. Furthermore, automation related to the validation of capabilities and the performance of automated actions reduces the effort by a factor of a hundred compared with a manual action.

Assessment of health hazards of greenhouse workers considering UV exposure and thermal comfort

Greenhouse (GH) indoor environments are usually manipulated to enhance plant growth and yield, but those environments might not be favorable for GH workers. This study aimed to investigate the health hazards of greenhouse workers from UV radiation exposure and analyze the heat stress and thermal comfort in air-conditioned greenhouses. Two GHs with different covering materials (glass greenhouse-GGH and polycarbonate greenhouse-PCGH) were selected for this study. The UV index of GGH varied from moderate to high (3 to 8) based on the season and deployment of the shade screen, whereas PCGH was always UV risk-free below and above the shade screen. Heat stress was evaluated in terms of wet bulb globe temperature (WBGT) and thermal comfort with the predicted mean vote (PMV) and predicted percent dissatisfied (PPD). The value of WBGT in GGH varied between 17.67 °C and 26.54 °C and between 18.25 °C and 25.97 °C in PCGH. The PMV values ranged from -2.57 to 1.15 for GGH and from -2.24 to 1.42 for PCGH, depending on airspeed, metabolic rate, and indoor conditions. The PPD values ranged between 5.0 to 94.6 % for GGH and 5.0 to 75.0 % for PCGH. As the optimal target range of the indoor environmental parameters (temperature and relative humidity) were maintained in the modern GHs throughout the year, the potential for heat stress and thermal discomfort was not severe. However, as a precaution, avoiding heavy activities around noon is recommended, even in perfectly conditioned greenhouses, which could be above the danger level in low-tech greenhouses.

Prediction of Greenhouse Indoor Air Temperature Using Artificial Intelligence (AI) Combined with Sensitivity Analysis

Greenhouses are essential for agricultural production in unfavorable climates. Accurate temperature predictions are critical for controlling Heating, Ventilation, Air-Conditioning, and Dehumidification (HVACD) and lighting systems to optimize plant growth and reduce financial losses. In this study, several machine models were employed to predict indoor air temperature in an even-span Mediterranean greenhouse. Radial Basis Function (RBF), Support Vector Machine (SVM), and Gaussian Process Regression (GPR) were applied using external parameters such as outside air, relative humidity, wind speed, and solar radiation. The results showed that an RBF model with the LM learning algorithm outperformed the SVM and GPR models. The RBF model had high accuracy and reliability with an RMSE of 0.82 °C, MAPE of 1.21%, TSSE of 474.07 °C, and EF of 1.00. Accurate temperature prediction can help farmers manage their crops and resources efficiently and reduce energy inefficiencies and lower yields. The integration of the RBF model into greenhouse control systems can lead to significant energy savings and cost reductions.

Cooling and Heating a Greenhouse in Baghdad by a Solar Assisted Desiccant System

Modeling the microclimate of a greenhouse located in Baghdad under its weather conditions to calculate the heating and cooling loads by computer simulation. Solar collectors with a V-corrugated absorber plate and an auxiliary heat source were used as a heating system. A rotary silica gel desiccant dehumidifier, a sensible heat exchanger, and an evaporative cooler were added to the collectors to form an open-cycle solar assisted desiccant cooling system. A dynamic model was adopted to predict the inside air and the soil surface temperatures of the greenhouse. These temperatures are used to predict the greenhouse heating and cooling loads through an energy balance method which takes into account the soil heat gain. This is not included in conventional methods. The results showed satisfactory agreement with published papers. Also, the results of heating and cooling loads obtained revealed good agreement with those obtained from conventional methods when the soil heat gain is included. Two identical collectors in series of total area of 5.4m2 were employed as a heating system which provides an outlet air temperature of 30 o C at air mass flux of 0.06 kg/s.m2 at midday in January. While, a 65 oC outlet air temperature was achieved for the same mass flux at midday in August. The desiccant cooling system was operated in five operating modes; the ventilation mode and four recirculation modes with 20%, 50%, 70%,and 90% recirculation. The simulation results showed that a regeneration temperature of 60-70 o C is satisfactory for a cool supply air temperature of about 19.5 o C. Also, it was noted that 20-30 % recirculation of return air would result in suitable indoor greenhouse conditions for most periods of system operation. In addition, the coefficient of performance COP of the system was high compared with the conventional vapor compression systems.

АНАЛИЗ РАЗВИТИЯ РАСТЕНИЙ С ПОМОЩЬЮ МЕТОДОВ КОМПЬЮТЕРНОГО ЗРЕНИЯ

The study describes the development of a service for monitoring plants’ growth in an indoor greenhouse using computer vision models, visual data collection with the esp32-cam card, the OV5640 camera, and the YOLO v4 detection model for extracting individual plants from the images. The plants tracking was performed by the DeepSORT library. The study determined the age of plants according to their type in order to identify their growth rate and notify the user when the parameters achieved. The computer vision methods are implemented through the TensorFlow 2 framework, with 99 % of classification accuracy, and Random Forest coefficient of determination of 0.94 for the regression of a plant’s age.

Modelling and analysis of a renewable energy-driven climate-controlled sustainable greenhouse for hot and arid climates

Population growth and improved living standards led to increased demand for food and crops, of which cultivation is heavily affected by average ambient temperature increase resulting from global warming. Hot and arid climate regions with high average summer temperatures suffer further as agricultural greenhouse indoor temperature reaches levels much higher than the ambient, causing severe dehydration and eventual death of crops. This research proposes an innovative, resilient and sustainable integrated greenhouse system to control greenhouse indoor climate within the cultivation range. This system consists of concentrating photovoltaic thermal system utilizing nucleate boiling heat transfer, thermal energy storage based on phase change material, lithium bromide absorption cooling system and dehumidifier, borehole ground heat exchanger, and spectrum selective roof for a greenhouse. The system is driven by renewable energy sources: Solar irradiation, ambient air, and geothermal cooling to provide electricity, space cooling, air-conditioning, and irrigation water. A comprehensive thermodynamic analysis of the sustainable integrated greenhouse system was done to obtain the energy and exergy efficiencies of the subsystems and overall system. The analysis shows overall energy and exergy efficiencies of 43.36 % and 19.19 %, respectively. Compared to the conventional greenhouse roof, the spectrum selective roof efficiently reduced the maximum cooling load by 29.2 %. Under the investigated hot and arid climate conditions, the system produced 33.28 kW of cooling, 29.3 kW of electricity, 8.36 L/h of dehumidified irrigation water, and 250 m3/h of dry ventilation air. An economic analysis based on the life cycle cost was executed to determine the economic feasibility of the integrated system. The recorded levelized cost of cooling, electricity, and water are 0.021 USD/kWhc, 0.0366 USD/kWhele, and 8.8 USD/m3, respectively, presenting a cost-competitive standalone integrated system.

Composition identification and UV-C irradiation growth inhibition effect of green shading on the greenhouse cover

Greenhouse cover pollution with green shading composed of dust, microalgae and bacteria is a severe problem in tropical areas. The shading results in lower greenhouse indoor light intensity reducing the yield and quality of protected horticulture crops. However, few studies have focused on environmentally efficient ways to remove green shading to increase greenhouse production. In this study, five purified microalgae were isolated from the green shading of three greenhouse roofs and were identified using morphological and molecular assessments. The effects of Ultraviolet-C irradiation (UV-C, 254 nm) at doses of 100, 200 and 300 mJ cm−2 on the growth of GLY-1 microalgae were investigated. The results indicated that five purified microalgae all appeared to belong to the genus of Jaagichlorella. The purified microalgae cell density and chlorophyll content decreased respectively by 26.89–74.44 % and 42.02–77.31 % at 1–3 d after UV-C treatment with doses ranging from 100 to 300 mJ cm−2. The inhibition of the growth rate of microalgae was significantly positively correlated with the UV-C irradiation dose and significantly negatively correlated with treatment time. In summary, UV-C irradiation treatment at 300 mJ cm−2 and 3 d could substantially inhibit microalgae growth in green shading on greenhouse covers. UV-C irradiation could be an effective method for solving the problem of greenhouse cover pollution with microalgae.

Comprehensive Review on Climate Control and Cooling Systems in Greenhouses under Hot and Arid Conditions

This work is motivated by the difficulty of cultivating crops in horticulture greenhouses under hot and arid climate conditions. The main challenge is to provide a suitable greenhouse indoor environment, with sufficiently low costs and low environmental impacts. The climate control inside the greenhouse constitutes an efficient methodology for maintaining a satisfactory environment that fulfills the requirements of high-yield crops and reduced energy and water resource consumption. In hot climates, the cooling systems, which are assisted by an effective control technique, constitute a suitable path for maintaining an appropriate climate inside the greenhouse, where the required temperature and humidity distribution is maintained. Nevertheless, most of the commonly used systems are either highly energy or water consuming. Hence, the main objective of this work is to provide a detailed review of the research studies that have been carried out during the last few years, with a specific focus on the technologies that allow for the enhancement of the system effectiveness under hot and arid conditions, and that decrease the energy and water consumption. Climate control processes in the greenhouse by means of manual and smart control systems are investigated first. Subsequently, the different cooling technologies that provide the required ranges of temperature and humidity inside the greenhouse are detailed, namely, the systems using heat exchangers, ventilation, evaporation, and desiccants. Finally, the recommended energy-efficient approaches of the desiccant dehumidification systems for greenhouse farming are pointed out, and the future trends in cooling systems, which include water recovery using the method of combined evaporation–condensation, as well as the opportunities for further research and development, are identified as a contribution to future research work.

A model-based methodology for the early warning detection of cucumber downy mildew in greenhouses: An experimental evaluation

• The combination of greenhouse climate and disease models is proposed for disease predictions. • Weather forecast libraries are used to predict the greenhouse climate. • The proposed approach allows to detect diseases 72 h ahead compared with other methods. • Experimental tests are presented to demonstrate the capabilities of the proposed methodology. This study introduces a new approach combining a mechanistic greenhouse climate model and a disease model for the forecast of diseases occurrence in greenhouses. The method was evaluated in NPADB (National Precision Agriculture Demonstration Base), Beijing, China using data collected from transplanting to the primary infection that occurred in the greenhouse, in the spring season of 2021. First, the dynamic model is used to predict the greenhouse indoor climate 72 h ahead. Then, this prediction is used as input to the disease model to detect disease occurrence in advance. The predictions for the greenhouse downy mildew were compared using real-time measured data for two months. After several false-positive reports, one positive report by both methods fitted the first observation in the greenhouse on April 24, 2021. Thus, the main contribution of this work is the early warning cucumber downy mildew via coupling climate and disease models, where only transient inputs from weather forecasts are required.

Optimal smart contract for autonomous greenhouse environment based on IoT blockchain network in agriculture

The Internet of Things (IoT) has been widely adopted in many smart applications such as smart cities, healthcare, smart farms, industry etc. In recent few years, the greenhouse industry has earned significant consideration from the agriculture community due to its ability to produce fresh agricultural products with immense growth and production rate. However, labour and energy consumption costs increase the production cost of the greenhouse by 40–50% approximately. Moreover, the security and authenticity of agriculture data, particularly for yield monitoring and analysis, is also a challenging issue in current greenhouse systems.The greenhouse require optimal parameter settings with controlled environment to produce increase food production. Therefore, slight advancement can bring remarkable improvements concerning the increase in production with reduced overall cost. In this work, we contributed blockchain enabled optimization approach for greenhouse system. The proposed approach works in three steps to provide optimal greenhouse environment that are; prediction, optimization, and finally controlling. Initially, the Kalman filter algorithm is employed for predicting the greenhouse sensor data. In next step, the optimal parameters are computed for the indoor greenhouse environment. Finally, the optimized parameters are utilized by the control module to operate and regulate the actuator’s state to meet the desired settings in the indoor environment. To evaluate the performance of our proposed greenhouse system, we have developed an emulation tool. The proposed system has been investigated and compared against baseline approach concerning production rate and energy consumption. The obtained results reveal that the proposed optimization approach has improved the energy consumption by 19% against the prediction based approach and 41% against the Baseline scheme. Furthermore, the proof-of-concept based on the Hyperledger Fabric network is implemented on the top of the proposed greenhouse platform. For experimental analysis, we have conducted a series of experiments using Hyperledger calliper concerning throughput, latency, and resource utilization. These results advocates the efficiency of the proposed optimal greenhouse system.

RESULTS OF DEVELOPMENT AND TESTING OF COMPUTER-INTEGRATED TECHNOLOGY OF GREENHOUSES ARTIFICIAL LIGHTING CONTROL

The development and implementation of the automation and digitalization technologies of growing crops production processes in industrial greenhouses is being actualized for increasing of crop yields. This fact, in turn, positively influences on the re-equipment of the software and hardware base of protected soil domestic agricultural production, which stimulates an increase of investment attractiveness and long-term sustainability of Ukrainian agricultural enterprises. The main purpose of the article is developing of the scientific approaches on creation and testing of computer-integrated technology for artificial lighting control in the protected ground agricultural production conditions. The research object is automatic control process of artificial lighting. The research subject is methods and hardware-software components of the indoor greenhouse microclimate. Research methods are analysis of existing development methods, mathematical and computer modeling, hardware and software experimental testing. In course of the research, the component base has been substantiated and the block diagram of the hardware and software for the control of lighting technology has been developed. As a result, a computer model has been synthesized and tested, which is implemented on microprocessor technology and algorithms of the fuzzy logic theory for control the intensity and spectral composition of LED lamps in greenhouses. The prototype of control system for artificial additional lighting of greenhouse crops has been implemented and experimentally investigated. The implemented hardware and software means of computer-integrated technology allow to automatically control the parameters of LED lamps, taking into account the types and periods of growing crops.

Bio-economic evaluation of greenhouse designs for seasonal tomato production in Norway

Greenhouses are complex systems whose size, shape, construction material, and equipment for climate control, lighting and heating can vary largely. The greenhouse design can, together with the outdoor weather conditions, have a large impact on the economic performance and the environmental consequences of the production. The aim of this study was to identify a greenhouse design out of several feasible designs that generated the highest net financial return (NFR) and lowest energy use for seasonal tomato production across Norway. A model-based greenhouse design method, which includes a module for greenhouse indoor climate, a crop growth module for yield prediction, and an economic module, was applied to predict the NFR and energy use. Observed indoor climate and tomato yield were predicted using the climate and growth modules in a commercial greenhouse in southwestern Norway (SW) with rail and grow heating pipes, glass cover, energy screens, and CO 2 -enrichment. Subsequently, the NFR and fossil fuel use of five combinations of these elements relevant to Norwegian conditions were determined for four locations: Kise in eastern Norway (E), Mære in midwestern Norway (MW), Orre in southwestern Norway (SW) and Tromsø in northern Norway (N). Across designs and locations, the highest NFR was 47.6 NOK m −2 for the greenhouse design with a night energy screen. The greenhouse design with day and night energy screens, fogging and mechanical cooling and heating having the lowest fossil energy used per m 2 in all locations had an NFR of −94.8 NOK m −2 . The model can be adapted for different climatic conditions using a variation in the design elements. The study is useful at the practical and policy level since it combines the economic module with the environmental impact to measure CO 2 emissions. • A simulation model was applied to evaluate greenhouse design elements in Norway. • The economic and environmental performance of tomato production was determined. • Observed temperature, CO 2 -concentration and yield were predicted fairly accurately. • The greenhouse with a night energy screen had the highest Net Financial Return. • Investing in temperature regulation equipment reduced the fossil fuel use.

Identification of a New Family of Prenylated Volatile Sulfur Compounds in Cannabis Revealed by Comprehensive Two-Dimensional Gas Chromatography.

Cannabis sativa L. produces over 200 known secondary metabolites that contribute to its distinctive aroma. Studies on compounds traditionally associated with the scent of this plant have focused on those within the terpenoid class. These isoprene-derived compounds are ubiquitous in nature and are the major source of many plant odors. Nonetheless, there is little evidence that they provide the characteristic “skunk-like” aroma of cannabis. To uncover the chemical origins of this scent, we measured the aromatic properties of cannabis flowers and concentrated extracts using comprehensive two-dimensional gas chromatography equipped with time-of-flight mass spectrometry, flame ionization detection, and sulfur chemiluminescence. We discovered a new family of volatile sulfur compounds (VSCs) containing the prenyl (3-methylbut-2-en-1-yl) functional group that is responsible for this scent. In particular, the compound 3-methyl-2-butene-1-thiol was identified as the primary odorant. We then conducted an indoor greenhouse experiment to monitor the evolution of these compounds during the plant’s lifecycle and throughout the curing process. We found that the concentrations of these compounds increase substantially during the last weeks of the flowering stage, reach a maximum during curing, and then drop after just one week of storage. These results shed light on the chemical origins of the characteristic aroma of cannabis and how volatile sulfur compound production evolves during plant growth. Furthermore, the chemical similarity between this new family of VSCs and those found in garlic (allium sativum) suggests an opportunity to also investigate their potential health benefits.

Control of Plant Growth by Monitoring Soil Moisture, Temperature and Humidity in Dry Climate

Monitoring of environment parameter such as soil moisture, temperature and humidity are important parts of plant growth. This paper focused on the development of an instrumentation system and analysis on the effect of the water volume to the soil moisture, effect rate of soil moisture, temperature and humidity for an indoor greenhouse. Data were collected through two experiment. First experiment focused on effect volume of water to soil moisture. Soil hygrometer sensor used to measure soil moisture in real time. Five bottles contained different volume of water poured into soil which the soil is fixed to 200gram.Three different rate of soil moisture applied to plant and the data were analysed to determined relationship between soil moisture to the plant growth. It can be concluded that the rate of soil moisture does have effect on the stem diameter and leaves length based on the observations of the plant growth for three weeks.

Smart greenhouse control under harsh climate conditions based on data-driven robust model predictive control with principal component analysis and kernel density estimation

Efficient greenhouse climate control under harsh climate conditions at locations such as Qatar is a challenge because of the high temperature and high relative humidity. This work presents an application of a data-driven robust model predictive control for intelligent control of greenhouse indoor climate in Qatar. The framework integrates dynamic control models of temperature, CO2 concentration level, and humidity of a greenhouse with a data-driven robust optimization framework that accurately and rigorously captures uncertainty in weather forecast error. A machine learning approach combining principal component analysis (PCA) with kernel density estimation (KDE) is adopted to construct data-driven uncertainty sets for temperature, solar radiation, and humidity from historical data. The optimal control inputs that minimize control costs and state violations are obtained by solving a data-driven robust optimization problem at each time step. The application of controlling a greenhouse growing tomatoes located in Doha, Qatar is presented. The results suggest that the proposed PCA and KDE-based data-driven robust model predictive control approach needs lower total control cost than rule-based control and other model predictive control to maintain the greenhouse climate for supporting crop production under harsh climate conditions.