Query: smart farm hytroponice

title: IoT based adaptive network mechanism for reliable smart farm system
abstract: This paper presents an adaptive network mechanism for a smart farm system by using LoRaWAN and IEEE 802.11ac protocols. Generally, the internet of things (IoT) system for agriculture application is used in an environment where significant interferences can occur. These interferences can disrupt the network performance of the system. In this paper, an adaptive network mechanism is designed to improve the network performance of the system, in order to achieve a more reliable smart farm system. Specifically, the proposed adaptive network mechanism is implemented in the application layer. The system has the ability to adjust a protocol based on the network condition. For instance, the IEEE 802.11ac is suitable for transmitting data which require high data rate such as image or video. On contrary, the LoRaWAN protocol is suitable for sending data that only have small data packets such as sensor reading data. An adaptive mechanism, which combines advantages of both protocols, leads the system to achieve reliability while performing the monitoring task. The system has been evaluated for the real deployment scenario. The result demonstrates that the proposed system brings the reliability in terms of average latency and total gathered number of sensors' data.

title: IoT and agriculture data analysis for smart farm
abstract: In this paper, we propose developing a system optimally watering agricultural crops based on a wireless sensor network. This work aimed to design and develop a control system using node sensors in the crop field with data management via smartphone and a web application. The three components are hardware, web application, and mobile application. The first component was designed and implemented in control box hardware connected to collect data on the crops. Soil moisture sensors are used to monitor the field, connecting to the control box. The second component is a web based application that was designed and implemented to manipulate the details of crop data and field information. This component applied data mining to analyze the data for predicting suitable temperature, humidity, and soil moisture for optimal future management of crops growth. The final component is mainly used to control crop watering through a mobile application in a smartphone. This allows either automatic or manual control by the user. The automatic control uses data from soil moisture sensors for watering. However, the user can opt for manual control of watering the crops in the functional control mode. The system can send notifications through LINE API for the LINE application. The system was implemented and tested in Makhamtia District, Suratthani Province, Thailand. The results showed the implementation to be useful in agriculture. The moisture content of the soil was maintained appropriately for vegetable growth, reducing costs and increasing agricultural productivity. Moreover, this work represents driving agriculture through digital innovation.

title: An Analysis on the Educational Needs for the Smart Farm: Focusing on SMEs in Jeon nam Area
abstract: This study determined effective educational strategies by investigating and analyzing the related educational demands for SMEs (small and medium sized enterprises) in the 4th Industrial Revolution based area of smart farms. In order to derive the approprate educational strategies, Importance Performance Analysis (IPA) and Borich's Needs Assessment Model were conducted based on the smart farm technological field. As a result, the education demand survey showed high demand for production systems and intelligent farm machinery. In detail, Borich's analysis showed the need for pest prevention and diagnosis technology (8.03) network and analysis SW linkage technology (7.83) and intelligent farm worker agricultural power system electric energy hybrid technology (7.43) In contrast, smart plant factories (4.09) lighting technology for growth control (4.46) and structure construction technology (4.62) showed low demands. Based on this, the IPA portfolio shows that the network and analysis SW linkage technology and the CAN based complex center are urgently needed. However, the technology that has already been developed, such as smart factory platform development, growth control lighting technology and structure construction technology, was oversized. Based on these results, it is possible to strategically suggest the customized training programs for industrial sectors of SMEs that reflect the needs for efficiently operating smart farms. This study also provides effective ways to operate the relevant training programs.

title: Development of an ultrasonic wave emission system based on multimedia database in a smart farm
abstract: Due to the development of the recent ICT (Information and Communication Technology) ICT has been applied to the agriculture and it is improving the productivity and quality of the crop through its utilization. In addition, with the realization of fast networks like 5G, IoT (Internet of Things) technology which can link various things around us has been applied in various aspects of life. Consequently, the services and related markets applying it are expected to grow greatly in the future. On the other hand, in the field of crop cultivation, it was possible to improve productivity by spraying fertilizer or pesticide on crops in the past, but for consumers who seek healthy food, they want to be eco friendly, and cause no harm to the health of crops. In this paper, we develop an ultrasonic wave emission system based on multimedia database in a smart farm using ICT. Through this, environmental information of crops growing in smart farm is collected by using IoT device and is managed by multimedia database. In addition, by providing an optimal environment through ultrasonic wave emission, we can promote crop growth and suppress diseases and pests. Also, this system stores and manages multimedia data such as temperature, humidity, and ultrasonic waves, and suggests a method of extracting knowledge information for ultrasonic emission based on this. In order to prevent transmission delay time between each component, we also propose optimization methods for database processing. Using the system developed in this paper, it will be possible to make consumers interested in finding healthy food by cultivating more eco friendly crops.

title: UAV Deployment Design for Maximizing Effective Data with Delay Constraint in a Smart Farm
abstract: Data transmission and real time processing of Internet of Things (IoT) devices in remote unmanned area are becoming a challenging issue. In this paper, an Unmanned Aerial Vehicle (UAV) enabled computing system is investigated in a smart farm, where multiple UAVs are deployed to make intelligent decisions based on data collected by sensors. First, considering the delay constraint of data processing, the concept of effective data is introduced. Next, the effective data of farm monitoring devices (FMDs) is maximized by jointly optimizing computing and communication resources allocation and the deployment of UAVs. The formulated problem is a combinatorial optimization problem that is hard to tackle. Furthermore, this problem is transformed into two sub problems and a two layer iterative optimization algorithm is proposed to get an approximate optimal solution. Finally, simulation results demonstrate that the proposed algorithm has a significant increase in terms of effective data.

title: Smart farm and monitoring system for measuring the Environmental condition using wireless sensor network IOT Technology in farming
abstract: Internet of things (IoT) gives a new proportion of smart farming and agriculture territory. Because with the development of the current world, the internet of things field has peaked with modern technology and modern techniques. In the modern world, IoT is used in every domain like smart city, smart university, smart car park system, etc. This paper is about the implementation of the smart farm. IoT concept helps in cost efficient farming activities like crop and other resource management. With a wireless sensor network, it is easy to connect with every sensor node placed in the farming environment. Also, with the wireless sensor network, it can connect with long distance ranges. With the help of a sensor network, it can collect the data from the farming environment and analyze it according to the pre defined values. The proposed system used IoT sensors to collect the data are soil moisture sensors, temperature sensors, water volume sensors, etc. According to the existing system analysis, the proposed solution contains a smart farm environment and a real time monitoring system with the wireless sensor network for node connectivity. The proposed system provides a more reliable and flexible smart concept for the farmers, and it is a simple architecture that contains the IoT sensors that collect the data from the farm field and transfer those data through wireless sensor network to the central server and according to the input data, the primary server assigning the task to the particular devices.

title: Representative Farm Based Sustainability Assessment of the Organic Sector in Switzerland Using the SMART Farm Tool
abstract: The agricultural sector faces serious environmental, social and economic challenges. In response, there has been a proliferation of labels and certifications aiming to ensure minimum farm sustainability performance. Organic agriculture (OA) a prominent example, having received substantial research attention relating to agronomic and environmental performance. While international OA movements are evolving to include broader sustainability aspirations, limited research exists on the social and economic performance of OA. To address this, we conducted a representative farm based assessment of the Swiss organic sector to evaluate its contribution to sustainability across a wide range of themes based on the FAO Sustainability of Agriculture and Food Assessment (SAFA) Guidelines. We assessed 185 farms using the Sustainability Assessment and Monitoring RouTine (SMART) Farm Tool, chosen through stratified random sampling by farm type and agricultural zone. The results indicate that the Swiss organic sector makes a substantially positive contribution to sustainability, with average scores for theme goal achievement of 62% (Good Governance) 77% (Environmental Integrity) 70% (Economic Resilience) and 87% (Social Well being) A set of 45 influential indicators (28 for plant production/mix farms and 30 for livestock farms) were selected based on the ability to explain variance (using Principal Component Analysis) and importance for goal achievement. The indicator sets explained a large amount of variation (ca. 70% for both farm types) and revealed a snapshot of management topics relevant to sustainability performance across the sector. These covered socio political engagement, emissions to air and water, biodiversity, animal welfare, profitability, vulnerability, product quality, local economy, capacity building, and workplace risks. The spread of results across the sample, and comparisons to secondary data (literature and official statistics) revealed the importance of both well studied issues (e.g. wide spread of energy consumption, variable yield levels/stability, local value chain dynamics) and more novel insights (e.g. strong political engagement, variable price premiums, lacking social security of farming families, insecure land tenure) We propose these topics as a basis for deeper analysis, designing improvement measures and conducting comparative research. This would bring much needed breadth into the typically narrow debate surrounding the relative merits of OA.

title: Comparison of Growth Conditions of Cut Spray Chrysanthemum in Smart Farm and Greenhouse
abstract: The yield and quality of cut flowers (Chrysanthemum) in a conventional greenhouse and a smart farm were assessed. The two sites were compared in terms of facilities, soils, and growth conditions. The three cultivars of cut spray chrysanthemums used in this study were 'Abishag' 'Ford' and 'Henna' The smart farm was equipped with 16 automatic sensor channels for monitoring temperature, humidity, CO2, nutri culture solution, air flow, and light. Fog spray devices were used in the smart farm but not in the greenhouse, where temperature was lowered by opening side windows for ventilation. We found that cut chrysanthemums of excellent quality are produced in the smart farm, due to the uniform environment year round. The chrysanthemum crop was harvested at the smart farm 3.5 times per year and produced an average of 129 plants/m2 annually, in one year, which is an improvement over the conventional greenhouse, in which the crop was harvested 3 times per year, yielding 91 crops/m2 annuallyin 1 year. The smart farm has an appropriate amount of organic matter and pH; however, the greenhouse has high organic matter. The electrical conductivity in the smart farm was 3.8 times higher than the corresponding value in the greenhouse. The three varieties grown on the smart farm were all superior to the greenhouse plants in terms of plant height, number of leaves, and stem diameter. On the other hand, chlorophyll content tended to be higher in the greenhouse plants. Through these results, we found that cut chrysanthemums of commercially excellent quality are produced in the smart farm, due to the uniform environment year round using smart farming system.

title: L M Farm: A Smart Farm based on LoRa MQTT
abstract: Smart farms are the future of Agriculture, which are expected to increase productivity with low cost and high convenience. Nevertheless, farmers are skeptical of running smart farms, due to their expensiveness, inefficient energy consumption, difficult management, and potential data leakage. Many studies suggest using LoRa (Long Range) a type of LPWAN (long power wide area network) technology, which is capable of long range with economical price and small battery consumption. However, studies overlook on how to transmit data proficiently, safely and assuredly. Therefore, this study proposes L M Farm, a LoRa and MQTT (Message Queue Telemetry Transport) based smart farm. MQTT is a lightweight messaging protocol that guarantees the reliability and security of data while minimizing the wastage of packet space. The prototype uses two Arduino Boards with Dragino LoRa Hat, in which one is connected to the VH400 soil moisture sensor and DHT11 temperature and humidity sensor, the other connected to a solenoid valve irrigation actuator. Also, there is a Raspberry Pi irrigation node, which uses Open Weather API to get 5 days of weather data. In addition, a web based application for farmers to conveniently manage the smart farm.

title: Mobile Application Development of Hydroponic Smart Farm using Information Flow Diagram
abstract: The innovative technology of Internet of Things has been gradually replaced and adapted in the digital age. Precision agriculture is an important consideration to design and develop smart farm systems, automatically and accurately operating. Hydroponic smart farm is one of the challenging tasks to deal with various design and development techniques. In this research, we aim to focus on the architecture design of the hydroponic smart farm system and the user interface design using Information Flow Diagram, and to evaluate our hydroponic smart farm system based on Technology Acceptance Model (TAM) We have found a lack of farmers' Internet of Things acceptance through the smart farm system. Therefore, our propose enables to achieve the better Internet of Things system and application in a conceivable smart architecture design on mobile usage.