Smart Greenhouse Research Articles

Optimization of a Smart Greenhouse with a Solar Energy System for Floating Raft Hydroponic Cultivation: Implementation and Performance Evaluation

Abstract The rapid growth of the global population has increased food demand, posing challenges for traditional agriculture due to limited natural resources. Hydroponic systems offer a sustainable solution by optimizing the growing environment. This study evaluates the performance of a floating-raft hydroponic smart greenhouse powered by solar energy. The system integrates advanced technologies and renewable energy sources, including three 120 Wp and 130 Wp solar panels arranged in a series-parallel configuration, a solar charge controller, two parallel 12V 65Ah and 12V 200Ah batteries, a microcontroller, DHT22 sensors, and actuators such as LED lights and a mist-type DC pump sprayer. These components effectively regulate humidity, temperature, lighting, and spraying, with data transmission to the Antares platform via a monitoring system utilizing NodeMCU ESP8266. The greenhouse maintained optimal environmental conditions, reducing the temperature from 38°C to 32°C within 30 minutes. The system’s daily electrical load was 1006.36 Wh, leaving an energy reserve of 68.35%. The plants cultivated in the greenhouse responded favorably to the system. This study highlights the potential of combining renewable energy with smart agricultural technologies to address the limitations of traditional farming practices and enhance sustainability.

The design and implementation of a smart greenhouse control system

Abstract Against the dual backdrop of continuously improving agricultural production levels and the global scarcity of water resources, countries worldwide are actively exploring the topic of water conservation. The development of an intelligent irrigation control system could make a significant contribution to water conservation efforts, while also helping to increase crop yields. This project adopts a modular design approach, utilizing sensors to collect real-time environmental data surrounding the crops. If the data falls outside the predefined optimal range for crop growth, the system automatically initiates irrigation, cooling, and supplemental lighting. The hardware design primarily consists of a data perception module and a control terminal module. After testing, the system runs stably and can accurately obtain information such as soil moisture, light intensity and ambient temperature, and basically realize the digital intelligent control of crop growing environment.

RANCANG BANGUN PROTOTYPE SMART GREENHOUSE MENGGUNAKAN THINGSPEAK

The design of this smart greenhouse prototype is one of the implementations of artificial intelligence controlled by the Arduino uno microcontroller, which has actuators in the form of lights and pumps and is equipped with DHT22 sensors, Soil Moisture and Temperature Sensors. The method used in making this smart greenhouse prototype is the Prototype method. There are seven stages in the prototype method that guide the process of making robots, namely gathering requirements, building prototypes/prototypes, evaluating prototypes, programming systems, testing systems, evaluating systems, and using the system. The most important stage in making this smart greenhouse prototype is the stage where at this stage testing is carried out on the results of the design and programming of the robot that has been made to prove the robot prototype is in accordance with what is desired or not. The testing process is carried out in tandem with the assembly process and programming process. In the process of assembling and programming the robot prototype, it is done with system errors. Testing with system errors is carried out to get a robot prototype that works optimally.

Advancing Sustainable Cyber-Physical System Development with a Digital Twins and Language Engineering Approach: Smart Greenhouse Applications

In recent years, the integration of Internet of Things technologies in smart agriculture has become critical for sustainability and efficiency, to the extent that recent improvements have transformed greenhouse farming. This study investigated the complexity of IoT architecture in smart greenhouses by introducing a greenhouse language family (GreenH) that comprises three domain-specific languages designed to address various tasks in this domain. The purpose of this research was to streamline the creation, simulation, and monitoring of digital twins, an essential tool for optimizing greenhouse operations. A three-stage methodology was employed to develop the GreenH DSLs, a detailed metamodel for enhanced smart monitoring systems. Our approach used high-level metamodels and extended Backus–Naur form notation to define the DSL syntax and semantics. Through a comprehensive evaluation strategy and a selected language usability metrics, the expressiveness, consistency, readability, correctness, and scalability of the DSL were affirmed, and areas for usability improvement were highlighted. The findings suggest that GreenH languages hold significant potential for advancing digital twin modeling in smart agriculture. Future work should be aimed at refining usability and extending its application range. The anticipated integration with additional model-drive engineering and code generation tools will improve interoperability and contribute to digital transformation in the smart greenhouse domain and promote more sustainable food production systems.

Data-driven farming: implementing internet of things for agricultural efficiency

<p>Integrating internet of things (IoT) technology into agriculture has become essential to address challenges such as low crop productivity, which is often due to insufficient soil nutrients and suboptimal environmental conditions. This paper discusses the design and implementation of an IoT-based system for agriculture that aims to automate key parameters, facilitate real-time monitoring, and promote sustainable practices. Equipped with a graphical user interface (GUI), the system focuses on improving irrigation, regulating temperatures, and correcting soil nutrient deficiencies to improve crop productivity. Our research includes the use of humidity sensors to monitor soil moisture and temperature sensors. Soil nutrient levels, especially nitrogen, phosphorus, and potassium (NPK), were also assessed. We conducted experiments on three radish varieties using this IoT system and compared the results with traditional farming methods. The germination rate was impressive, reaching 98% within the first four days, while in a traditional greenhouse, it did not exceed 50%. As for plant height and leaf area, the smart greenhouse was better. These results were promising and demonstrated the potential of IoT in enhancing agricultural productivity. These results highlight the significant impact of IoT technology in enhancing agricultural productivity and its potential for broader application in this sector.</p>

Impact of Smart Greenhouse Using IoT for Enhanced Quality of Plant Growth

Greenhouses play a crucial role in manipulating environmental conditions for optimal plant growth. While existing greenhouses enhance control over environmental factors, manual controls such as watering and humidity regulation often lead to suboptimal production and increased costs. This study proposes the development of a smart greenhouse with an automatic control system using fuzzy logic, specifically fuzzy Sugeno, to regulate watering and lighting based on soil moisture, temperature, and light intensity. The system's architecture involves sensor inputs, microcontroller processing, and the activation of actuators, such as UV lights and water pumps. Fuzzy logic is applied to interpret soil moisture and temperature inputs and determine optimal irrigation durations. The system's functionality is tested and validated through functional testing, Blynk application testing, and fuzzy Sugeno testing. Results indicate the successful implementation of the proposed smart greenhouse system. Functional testing demonstrates accurate sensor readings, including temperature and soil moisture. The Blynk application enables real-time monitoring and control of environmental conditions. Fuzzy Sugeno testing validates the irrigation control system, with an average error rate of 1.3%, affirming the system's alignment with desired specifications. Plant testing in different conditions showcases the effectiveness of the smart greenhouse in supporting plant growth and development.

Development Of Smart Greenhouse Farming Based On Internet Of Things

This project aim is to develop smart greenhouse farming based on IoT system for monitoring the quality for the plant which use sensor and Node-RED as a website for monitoring. The monitoring system using microcontroller such as humidity sensor,soil moisture to control the condition of soil moisture level. The phenomenon of greenhouse plants is particularly significant since it has the potential to help improve the economy and cut manufacturing costs. Given the agricultural industry's lack of human resources, one of the cultivation techniques that have to be improved is hydroponic plants, also known as greenhouse plants. Hydroponic plant technology can be used in conjunction with hydroponic plants. Tropical greenhouses have various advantages in plant cultivation as well as practical manufacturing potential.This technology can be utilized to apply according to plant demands to maximize yield in agriculture. The project employed is a tool for monitoring soil moisture that is based on a prototype using an online platform for monitoring humidity and plant quality, and it can assist farmers in decreasing the budget for production.

Pengaruh Iklim Mikro dan Penggunaan Media Tanam yang Berbeda pada Pertumbuan Tanaman Melon Varietas Sweet Net

Melon are plants that are sensitive to environmental changes. Currently, developing technology for cultivating melons in greenhouses is used to modify the microclimate for the growth of melon plants. This research aims to determine the effect of microclimate and different types of planting media on growth response of sweet net variety melon plants that planted in the Politeknik Negeri Jember smart green house. The research was carried out in January – May 2024. The treatments tested consisted of two treatments, namely: the use of soil media+compos and cocopeat media which would be repeated 15 times. The parameters observed include microclimate (temperature and humidity) and also plant growth indicators including plant length and number of leaves, number of flowers, and potential weight of melon. The data obtained were analyzed using the T-test. The research results show that the microclimate inside the Politeknik Negeri Jember smart green house is still classified as an optimal microclimate to support the growth of melon plants. Apart from that, the use of cocopeat planting media was able to provide the best results for the length of the melon plants in the 4th to 6th week of observation, but there was no significant difference in the parameters of the number of leaves, number of flowers and potential weight of melon of. The use of cocopeat media and soil media are both good media in supporting the growth of melon plants in a greenhouse.

Four Tales of International Development Cooperation toward Vulnerable-Smart Agriculture: The Korean Projects of Smart Greenhouse for High-valued Crops

Four Tales of International Development Cooperation toward Vulnerable-Smart Agriculture: The Korean Projects of Smart Greenhouse for High-valued Crops

GreenhouseGuard: Enabling real-time warning prediction for smart greenhouse management

GreenhouseGuard: Enabling real-time warning prediction for smart greenhouse management

Droplet Energy Harvesting System Based on Total-Current Nanogenerator.

The droplet-based nanogenerator (DNG) is a highly promising technology for harvesting high-entropy water energy in the era of the Internet of Things. Yet, despite the exciting progress made in recent years, challenges have emerged unexpectedly for the AC-type DNG-based energy system as it transitions from laboratory demonstrations to real-world applications. In this work, we propose a high-performance DNG system based on the total-current nanogenerator concept to address these challenges. This system utilizes the water-charge-shuttle architecture for easy scale-up, employs the field effect to boost charge density of the triboelectric layer, adopts an on-solar-panel design to improve compatibility with solar energy, and is equipped with a novel DC-DC buck converter as power management circuit. These features allow the proposed system to overcome the existing bottlenecks of DNG and empower the system with superior performances compared with previous ones. Notably, with the core architecture measuring only 15 cm × 12.5 cm × 0.3 cm in physical dimensions, this system reaches a record-high open-circuit voltage of 4200 V, capable of illuminating 1440 LEDs, and can charge a 4.7 mF capacitor to 4.5 V in less than 24 min. In addition, the practical potential of the proposed DNG system is further demonstrated through a self-powered, smart greenhouse application scenario. These demonstrations include the continuous operation of a thermohygrometer, the operation of a Bluetooth plant monitor, and the all-weather energy harvesting capability. This work will provide valuable inspiration and guidance for the systematic design of next-generation DNG to unlock the sustainable potential of distributed water energy for real-world applications.

The analysis of nutritive value, vitamin, and minerals content of natural isotonic beverages formulated from Smart Green House Melon

The development of isotonic beverages on the market is very rapid because many types of isotonic beverages meet the standards for consumption. One of the local food ingredients that has the potential to be developed as a natural isotonic beverage is melon because it has good nutritional and energy content. This study aims to determine the nutritional and mineral content of three isotonic beverage formulations made from Smart Green House (SGH) melon. The method of this study is an experimental laboratory. There are three formulations of isotonic beverages, P1 contains 89.5% of melon, P1 contains 84.5% of melon, and P3 contains 74.5% of melon. The Laboratory analysis showed that formula P1 has higher nutritional content than formula P2 and P3. The nutrition contents of P1 are potassium 149 + 2.83 mg/100g, sodium 132 + 4.24 mg/100g, pH 4.35 + 0.01, vitamin C 11.3 + 0.28%, calories 44.99 + 0.06 kcal/100 g, carbohydrates 10.23 + 0.04%, and sucrose 8.78 + 0.04%. Based on the results of this research, it can be concluded that SGH melon has the potential to be developed into a healthy isotonic beverage.

Automatic Water Pumping System for Smart Greenhouse Using Renewable Energy Resources

This paper proposes an automatic water pumping system for a smart greenhouse, optimizing plant irrigation using renewable energy. It features sensors monitoring soil moisture, a pump powered by solar or wind, and a control unit adjusting pump operation based on sensor readings. The system, linked to the Internet, enables remote monitoring and control. Expected benefits include reduced manual watering, decreased reliance on non-renewable energy, and enhanced greenhouse efficiency. Smart greenhouses aim to enhance plant cultivation sustainability, with a crucial role played by the automated irrigation system, comprising a control unit, pump, and moisture sensors. Internet connectivity facilitates remote monitoring and adjustment for informed greenhouse operation.

Implementasi greenhouse untuk mendukung agropark di Desa Tanjung Pinang II Kecamatan Tanjung Batu Kabupaten Ogan Ilir

Tanjung Pinang II Village is one of the districts in Tanjung Batu, Ogan Ilir Regency, South Sumatra, which is currently developing its potential to become a tourist village through an agro-park. However, the process of transforming the village into a tourism and educational area for the community has not yet implemented technology. Therefore, in this community service, a smart greenhouse was developed and implemented in Tanjung Pinang II Village. The methods used in this community service included situational analysis, greenhouse construction, counseling and training, as well as evaluation, analysis, and conclusion. The constructed greenhouse is equipped with temperature and humidity sensors to measure the conditions inside the room. The greenhouse is built with a steel frame and uses UV plastic for its roof and walls. The results of the community service showed that the village residents and the community benefited from the greenhouse in the process of developing Tanjung Pinang II Village as an ecotourism area.

Camera-based plant growth monitoring for automated plant cultivation with controlled environment agriculture

Building integrated photovoltaic thermal (BIPVT) greenhouse technology exhibits great potential to enhance crop yielding, energy harvesting, and water management for highly efficient farming and enables controlled environment agriculture (CEA) for automated plant cultivation. The proposed smart greenhouse system uses BIPVT, geothermal, image-based sensing, and control technologies for high energy efficiency and high-fidelity plant growth control. A camera-based, computer-controlled system was demonstrated to successfully monitor single and multiple food plants' growth. The growth rate over the life-cycle of the plant was automatically recorded by cameras without human interference. A novel algorithm was developed to extract the plants from the background of the image and then to measure the length from the ground to the top of the longest plant with different shapes or profiles. The growth rate was automatically generated as fundamental data for farming management and control. Because the plant growth is slow and the errors caused by image noise can be comparable with the actual growth when the interval between images is not large enough, the noise of a camera is evaluated by comparing multiple images under the same exposure condition, which is used to provide the error range of the measurements. The plant growth monitoring system will be integrated into an ongoing greenhouse project for smart greenhouse operation.

A Study of Irrigation Management in Smart Farming and IoT for Greenhouse Tomato Production

In this study of the relevant literature, we look at the development of smart farming and precision agriculture in greenhouse tomato cultivation, paying particular attention to the role that the Internet of Things (IoT) and automated irrigation systems have played thus far. Five terms were used to compile this literature review: automated watering systems, greenhouses, the Internet of Things, tomatoes, and smart farming. This article summarizes recent advancements in smart farming tech, their impact on greenhouse tomato farming, and their potential to boost future crop yield and quality. The evaluation starts by introducing 'smart farming' and its significance in agriculture, followed by a review of the greenhouse sector and an overview of the Internet of Things (IoT) within it, with subsequent examination of the pros and cons of employing smart farming and IoT in greenhouse tomato cultivation through specific industry technology examples. The following overview section discusses the benefits of cultivating tomatoes in a smart greenhouse, techniques for achieving optimal results in greenhouse tomato farming, and includes case studies to demonstrate the advantages of automated irrigation for smart greenhouse tomato cultivation, while this literature review analyzes the current state of innovative farming technology and its effect on greenhouse tomato cultivation, covering smart farming, greenhouse cultivation, the Internet of Things, tomato cultivation, and automated irrigation, summarizing significant results and recommendations for future research, including promising increases in crop yield and quality.

Application of Fuzzy logic and IoT in a small-scale Smart Greenhouse System

This research aims to address the critical challenges caused by global population increase, climate change, and urbanization on food production by means of a Fuzzy Logic controller in a small-scale greenhouse and improving agricultural efficiency and sustainability.The growing global population has stressed the significance of implementing efficient food production techniques. Furthermore, the impact of climate change and extreme weather events complicates the production process. The goal of this study is to combine an innovative small greenhouse design with a fuzzy logic-based control system in order to develop an IoT-enabled self-regulating environment for autonomous plant growing.Continuous advances in technology have been focusing on automating greenhouses to provide self-regulating microclimates and optimal developmental conditions. Current trends focus on automating greenhouses to provide self-regulating microclimates. Looking ahead, the strategy entails combining automated approaches with precisely planned structures via IoT.This study's key findings include the successful integration of a fuzzy logic control system with greenhouse infrastructure, allowing for real-time monitoring and adjustment of environmental parameters, as well as the development of a pilot IoT ecosystem demonstrating the feasibility of self-regulating microclimates for improved plant growth.

An Integrated IoT Sensor-Camera System toward Leveraging Edge Computing for Smart Greenhouse Mushroom Cultivation

Industrial greenhouse mushroom cultivation is currently promising, due to the nutritious and commercial mushroom benefits and its convenience in adapting smart agriculture technologies. Traditional Device-Cloud protocol in smart agriculture wastes network resources when big data from Internet of Things (IoT) devices are directly transmitted to the cloud server without processing, delaying network connection and increasing costs. Edge computing has emerged to bridge these gaps by shifting partial data storage and computation capability from the cloud server to edge devices. However, selecting which tasks can be applied in edge computing depends on user-specific demands, suggesting the necessity to design a suitable Smart Agriculture Information System (SAIS) architecture for single-crop requirements. This study aims to design and implement a cost-saving multilayered SAIS architecture customized for smart greenhouse mushroom cultivation toward leveraging edge computing. A three-layer SAIS adopting the Device-Edge-Cloud protocol, which enables the integration of key environmental parameter data collected from the IoT sensor and RGB images collected from the camera, was tested in this research. Implementation of this designed SAIS architecture with typical examples of mushroom cultivation indicated that low-cost data pre-processing procedures including small-data storage, temporal resampling-based data reduction, and lightweight artificial intelligence (AI)-based data quality control (for anomalous environmental conditions detection) together with real-time AI model deployment (for mushroom detection) are compatible with edge computing. Integrating the Edge Layer as the center of the traditional protocol can significantly save network resources and operational costs by reducing unnecessary data sent from the device to the cloud, while keeping sufficient information.

Estimation of cucumber net primary production using environmental and control information in a smart multi-span plastic greenhouse

A smart greenhouse can be considered as a very large chamber under closed conditions. The amount of change in CO2 concentration inside the greenhouse during periods without CO2 fertilization depends on the net primary production (i.e., net carbon assimilated by plants via photosynthesis) (NPP) of the crops grown in the greenhouse. In this study, we estimated the NPP of cucumbers in a small-scale smart multi-span plastic greenhouse using environmental and control information collected during a low-temperature period when the greenhouse was frequently closed. The timescale for robustly calculating NPP using the temporal change in the CO2 concentration inside the greenhouse was empirically determined (=10 min), and a random forest-based NPP model was established using the calculated NPP and internal and external environmental greenhouse data to estimate NPP, even in the cases of opening roof windows or supplying CO2. The estimated NPP showed previously known relationships with environmental variables in the greenhouse, and cumulative NPP was consistent with cucumber growth. Based on the CO2 balance equation of a smart greenhouse and estimated NPP, we quantified the amount of CO2 supplied and CO2 emissions via the roof windows. The CO2 budget estimation errors and suggestions for reducing errors were discussed. The quantified CO2 budget in the greenhouse, including NPP, can be used for evaluating economic feasibility and improving crop productivity.

Digital twin framework for smart greenhouse management using next-gen mobile networks and machine learning

Due to the increase in world population, arable land has been reduced. Consequently, the concept of urban greenhouses is on the rise. Smart greenhouses need to monitor physical parameters for the healthy growth of plants from remote locations. A digital twin is a representation of physical assets in the digital world, and this emerging technology has opened up opportunities for efficient system development for Industry 4.0. The digital twin receives real-time operational data to monitor the asset in the digital domain. It performs real-time processing, data analysis, and machine learning to predict optimized decisions. In the era of next-generation mobile networks, IoT devices can communicate and perform their remote operations in a timely manner. In smart greenhouse technology, the digital twin could be a revolutionary substitute for real-time remote monitoring and process management. However, there has been limited work on digital twin-driven smart greenhouse technology. In this paper, a process management framework is developed that can be interpreted as a machine learning and cloud-based data-driven digital twin for smart greenhouses. The proposed framework consists of three layers: the physical, fog, and cloud layers. The physical greenhouse measurements are monitored using a highly immersive cloud-based, real-time 3D environment. We present an example architecture using commercial cloud and open-source tools to verify the proof of concept. Additionally, different ML techniques are utilized to predict the operational requirements for smart greenhouses.