A Comparative Study of Conventional and Smart Farming
01 Jan 2020-pp 1-8
TL;DR: The main objective is to provide a comparative study of smart farms to conventional farms; these smart farms employ machine learning algorithms in real time to tackle problems related to water and energy.
Abstract: Agriculture is at the heart of all occupations in developing countries, and with developing technologies, the application should be cost-effective and efficient. The proposed setup includes low-cost moisture, temperature sensors for optimizing water usage and yield, and radar sensors for monitoring any invasion in the farm. The setup is aimed to provide a study a miniature setup representing smart agriculture including smart water management with consistent monitoring for weather conditions in the present and future. An intelligent invasion monitoring system which can indicate animals or specifically pests invading the fields. This setup represents a part of a grid which will be utilizing solar power to prevent periodic replacements of batteries, and for this purpose, a solar panel will be used in the miniature farm. The main objective is to provide a comparative study of smart farms to conventional farms; these smart farms employ machine learning algorithms in real time to tackle problems related to water and energy. The Internet of things and machine learning have been advancing industrial purposes in each and every way, and finding its way in agriculture is still difficult due to the expenses which might not be affordable for a farmer. This research is a step toward efficient yet cost-effective farming.
Citations
More filters
TL;DR: In this article, the authors argue that despite their contribution to the economic and environmental performance of farming, big data act as a speciation mechanism, leading to new forms of intraspecific, interspecific and intergeneric competition.
35 citations
TL;DR: An attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.
Abstract: The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop
12 citations
Cites methods from "A Comparative Study of Conventional..."
..., [6], presented the smart agriculture concept using IOT, using Arduino kit....
[...]
01 Jan 2021
TL;DR: A semantically enriched agent based model called Agent Based Semantic Model for Smart Agriculture, ABSMSA which uses SAGRO-Lite, a light weight ontology designed by the authors for specific farming characteristics in developing countries as mentioned in this paper.
Abstract: The recent advancement of the Internet of Things (IoT) has led to the possibilities to process a large number of sensor data streams built upon large-scale IoT platforms. In developed countries IoT is already emerged successfully as a reasonable technique assuring the goal of self-complacency, hybrid and advanced decisions and computerization in the horticulture industry. Instant adoption of IoT in farming is impractical in developing nations because of less literacy, hesitance towards technology, smaller farm sizes and high cost of IoT farming solutions. Through a light weight IOT specifically focused on farming style of developing countries like India, farmers can increase their quality of farming by the use of this technology. The authors have developed a semantically enriched agent based model called Agent Based Semantic Model for Smart Agriculture, ABSMSA which uses SAGRO-Lite, a light weight ontology designed by the authors for specific farming characteristics in developing countries. The system uses two more ontologies the IoT-Lite and Complex Event Service Ontology (CESO) for semantic sensing and event recognition and handling.
2 citations
27 Aug 2020
TL;DR: In this article, a smart structure with smart control has been designed to control possible factors which are relevant to optimal plants health, such as temperature, humidity and oxygen, carbon diffusion rate.
Abstract: Climate change and increase population are the major causes of insecurity for food production. Climate change also effective agriculture like temperature rising, arid environment, unconditional weather changing and clean water deficiency. In cold areas, winter farming has been carried out on glass based structural or transparent greenhouse. These types of greenhouse are not suitable to summer farming in South Asian countries because sunlight radiations cause hazard greenhouse effect (Increase temp. humidity and oxygen, carbon diffusion rate). Recently, Smart farming in agriculture provide great flexibility to control environment factors (temperature, moisture, and water irrigation and gasses. Smart agriculture depending upon controlled artificial generated environment like as greenhouse farming and hydroponic culture (vertical farming). In proposed study, smart structure with smart control has been designed to control possible factors which are relevant to optimal plants health. Smart control inducing by smart sensor based on intelligent control system (environmental sensors with processing devices) and android application use for analysis of current condition of smart green house. The proposed greenhouse has been made by using wooden air ventilations transpiration sheet structure. This structure evaporates heat and isolated harm sunlight radiation for summer farming in arid areas. This isolation sheets save electric energy for maintaining temperature and spectrum of light. Intelligent Smart farming is necessary for better future of the nation and also better than traditional farming due to reduction of human interaction and automation system.
1 citations
Cites background from "A Comparative Study of Conventional..."
...Pressure of food security reduce by smart faming and hybrid techniques of productions.in which inducing smart devices and controlling system (Katyal and Pandian 2020)....
[...]
09 May 2021
TL;DR: In this article, the authors present how SMEs or companies can implement Industry 4.0 based on case studies in Thai SMEs, including identifying business trends, foresight strategy, gap analysis, industrial research and capacity development, and technology blueprint development.
Abstract: This chapter presents how SMEs or companies can implement Industry 4.0 based on case studies in Thai SMEs. Additionally, the chapter examined implementation strategies to convert a company successfully to SME 4.0. The implementation process includes identifying business trends, foresight strategy, gap analysis, industrial research and capacity development, and technology blueprint development plan. By working closely as the triple helix with universities and tech-development agencies, the inside-out and outside-in approaches are used. There can be several challenges during the implementation, such as the requirement of new skills, new technology, and investment needs. The challenges can be addressed by setup and implement the appropriate strategy for developing workers’ skill sets, making investments in the new technologies, and improving the efficiency of the production process.
1 citations
References
More filters
09 Nov 2016
TL;DR: This paper is to propose a Novel Smart IoT based Agriculture Stick assisting farmers in getting Live Data (Temperature, Soil Moisture) for efficient environment monitoring which will enable them to do smart farming and increase their overall yield and quality of products.
Abstract: Internet of Things (IoT) technology has brought revolution to each and every field of common man’s life by making everything smart and intelligent. IoT refers to a network of things which make a self-configuring network. The development of Intelligent Smart Farming IoT based devices is day by day turning the face of agriculture production by not only enhancing it but also making it cost-effective and reducing wastage. The aim / objective of this paper is to propose a Novel Smart IoT based Agriculture Stick assisting farmers in getting Live Data (Temperature, Soil Moisture) for efficient environment monitoring which will enable them to do smart farming and increase their overall yield and quality of products. The Agriculture stick being proposed via this paper is integrated with Arduino Technology, Breadboard mixed with various sensors and live data feed can be obtained online from Thingsspeak.com. The product being proposed is tested on Live Agriculture Fields giving high accuracy over 98% in data feeds.
125 citations
01 Aug 2017
TL;DR: The bottleneck problems of smart agriculture in the big data times are studied, such as type and precision problem of sensors used specially for agriculture, as well as intelligent processing and intelligent application of agricultural big data.
Abstract: This paper presents the construction goals of smart agriculture according to the requirements of smarter planet and smart agriculture, and designs an overall scheme for smart agriculture based on technologies such as GIS (Geographic Information System), cloud computing, IOT (Internet of Things), big data and sensing technology. The scheme contains five layers: all-sided perception layer, reliable transmission layer, intelligent processing layer, intelligent application layer, and supporting environment layer. The all-sided perception layer is mainly concentrated on the marks of things and intelligent acquisition of agricultural big data; the transmission layer is mainly about the construction of a reliable transmission network; the intelligent processing layer is mainly aimed at storage, processing and control technologies of agricultural big data; the application layer is mainly to study the construction of application system and intelligence application; and the supporting environment layer is aimed at the implementation of smart agriculture and the long-term application of its results. According to the overall scheme for smart agriculture and the National High-tech R&D Program of China (the 863 project) sponsored by the author, part of the farmland in Yanzhou District, Jining Municipality, Shandong Province was chosen as the experimental area for the research. On this basis, the bottleneck problems of smart agriculture in the big data times are studied, such as type and precision problem of sensors used specially for agriculture, as well as intelligent processing and intelligent application of agricultural big data. Finally, the countermeasures to solve the above problems are discussed. The first is to break through the technical bottleneck of high performance sensors. The second is to establish smart agriculture cloud platform based on GIS and cloud computing.
17 citations