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Modern technologies are revolutionizing the way humans have lived. The world's population is expected to reach 9.6 billion by year 2050 and to serve this much population, the agricultural industries and layman farmers need to embrace IoT and e-agriculture or ICT in agriculture. Feeding the global population is the biggest problem of the world. The...
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The production and distribution planning of fresh produce is a complex optimization problem, which is affected by many factors including its perishable characteristics. Farmers cannot guarantee the efficiency and accuracy of production and distribution decisions. Given the close relationship between the production and distribution of annual fresh p...
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... Approach: Finally, the approach and perspective stakeholders have towards A.I. technologies significantly influence their adoption. For example, many farmers do not even go through the process of assessing the benefits they could have by adopting these technologies simply because they prefer to stick to traditional and well-known farming practices [63]. This phenomenon is further intensified if the stakeholder's needs are satisfied by the performance of current conventional technologies [56]. ...
Food production faces significant challenges, mainly due to the increase in the Earth’s population, combined with climate change. This will create extreme pressure on food industries, which will have to respond to the demand while protecting the environment and ensuring high food quality. It is, therefore, imperative to adopt innovative technologies, such as Artificial Intelligence, in order to aid in this cause. To do this, we first need to understand the adoption process that enables the deployment of those technologies. Therefore, this research attempts to identify the factors that encourage and discourage the adoption of Artificial Intelligence technologies by professionals working in the fields of agriculture, livestock farming and aquaculture, by examining the available literature on the subject. This is a systematic literature review that follows the PRISMA 2020 guidelines. The research was conducted on 38 articles selected from a pool of 225 relevant articles, and led to the identification of 20 factors that encourage and 21 factors that discourage the adoption of Artificial Intelligence. The factors that appeared most were of economic nature regarding discouragement (31.5%) and product-related regarding encouragement (28.1%). This research does not aim to quantify the importance of each factor—since more original research becoming available is needed for that—but mainly to construct a list of factors, using spreadsheets, which could then be used to guide further future research towards understanding the adoption mechanism.
... The attitudes of farmers toward smart farming play crucial roles in the success and democratization of smart farming, especially since most farmers prefer to not take risks and continue with traditional farming practices [179]. The required skills and knowledge might represent a barrier for farmers that hinders them from adopting smart farming. ...
Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers’ profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.
... The attitudes of farmers toward smart farming play crucial roles in the success and democratization of smart farming, especially since most farmers prefer to not take risks and continue with traditional farming practices [181]. The required skills and knowledge might represent a barrier for farmers that hinders them from adopting smart farming. ...
Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers’ profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.
... The attitudes of farmers toward smart farming play crucial roles in the success and democratization of smart farming, especially since most farmers prefer to not take risks and continue with traditional farming practices [181]. The required skills and knowledge might represent a barrier for farmers that hinders them from adopting smart farming. ...
Due to the increasing global population and the growing demand for food worldwide as well as changes in weather conditions and the availability of water, artificial intelligence (AI) such as expert systems, natural language processing, speech recognition, and machine vision have changed not only the quantity but also the quality of work in the agricultural sector. Researchers and scientists are now moving toward the utilization of new IoT technologies in smart farming to help farmers use AI technology in the development of improved seeds, crop protection, and fertilizers. This will improve farmers’ profitability and the overall economy of the country. AI is emerging in three major categories in agriculture, namely soil and crop monitoring, predictive analytics, and agricultural robotics. In this regard, farmers are increasingly adopting the use of sensors and soil sampling to gather data to be used by farm management systems for further investigations and analyses. This article contributes to the field by surveying AI applications in the agricultural sector. It starts with background information on AI, including a discussion of all AI methods utilized in the agricultural industry, such as machine learning, the IoT, expert systems, image processing, and computer vision. A comprehensive literature review is then provided, addressing how researchers have utilized AI applications effectively in data collection using sensors, smart robots, and monitoring systems for crops and irrigation leakage. It is also shown that while utilizing AI applications, quality, productivity, and sustainability are maintained. Finally, we explore the benefits and challenges of AI applications together with a comparison and discussion of several AI methodologies applied in smart farming, such as machine learning, expert systems, and image processing.
... On the other hand, the modern technologies that prevail in the 21 st century are shaping the way we all live , by integrating into various domains. Based on the latest predictions the world population is expected to reach more than 9 billion and thus to serve this much of population by the time of 2050, the agriculture industry must expand all its capacity towards mega-scale production incorporating various technologies, for automating all its production tasks [3]- [6]. Nevertheless, it is evident that it requires a boost of 70% by the time of 2050 to serve that much of the population [5]. ...
... Nevertheless, it is evident that it requires a boost of 70% by the time of 2050 to serve that much of the population [5]. Hence in order to compete with that amount of demand and production, the agriculture industry and the farmers need to embrace the IoT and related technologies towards leveraging the agricultural work to the next level , where it is known as smart agriculture [4]- [6]. ...
... Also, agriculture is a major contributor to the economies of most emerging countries where it continues to contribute considerably to the gross domestic product (GDP) of many nations, as well as providing livelihood and job possibilities. According to the 2 nd Sustainable Development Goal (SDG) declared by Unites Nations, it aims to eradicate hunger, ensure food security and better nutrition, and promote sustainable agriculture by 2030 [6]. On the other hand, based on the latest studies, it is evident that crop output in the agriculture sector has not progressed significantly in recent years and the food prices are rapidly rising since the agricultural output is not keeping up with demand [7]- [11]. ...
Owing to the ever-increasing world population it
makes a doubt about feeding billions of people, as to the amount
of current rate of agricultural food production, where it often
hindered by many natural facts such as droughts, climate changes,
floods, pests, and disease carriers and so on. Apart from that
tendency towards traditional farming methods and obsolete skills
of farmers, this would also lead to a reduction of agricultural food
production where it is believed to be not enough by 2050 based on
the latest predictions and subjected to the growing population. On
the other hand, the entire planet and the human race is shaping
with the advancement of various technologies such as the Internet
of Things (IoT), where it can lead to the creation of a ubiquitous
connection between every digital object that prevails in the world
through the Internet. This has been applied in many domains
including agriculture industry for automating most of the tasks
which can reduce the amount of time; and to increase the overall
efficiency and have a better crop yield, by making smarter
decisions. The aim of this study is to identify and examine the role
of IoT in leveraging this smart agriculture to the next level; thus
making it sustainable in the long run. Hence in this study, we
synthesize the recent challenges, opportunities, and future
directions with regard to IoT-based smart agriculture, for
carrying out further research in this area and for the betterment
of human society.
... Agriculture CPS lately got quite well researched in the topic of smart agriculture in the development of CPS systems [4,28]. Precision agriculture is playing a major role to enhance the efficiency and resource savings [65,89]. ...
Current food system evolved in a great degree because of the development of processing and food engineering technologies: people learned to bake bread long before the advent of agriculture; salting and smoking supported nomad lifestyles; canning allowed for longer military marches; etc. Food processing technologies went through evolution and significant optimization and currently rely on minor fraction of energy comparing with initial prototypes. Emerging processing technologies (high-pressure, pulsed electric fields, ohmic heating, ultrasound) and novel food systems (cultured biomass, 3-D bioprinting, cyber-physical chains) try to challenge the existing chains by developing potentially more nutritious and sustainable food solutions. However, new food systems rely on low technology readiness levels and estimation of their potential future benefits or drawbacks is a complex task mostly due to the lack of integrated data. The research is aimed for the development of conceptual guidelines of food production system structuring as cyber-physical systems. The study indicates that cyber-physical nature of modern food is a key for the engineering of more nutritious and sustainable paths for novel food systems. Implementation of machine learning methods for the collection, integration, and analysis of data associated with biomass production and processing on different levels from molecular to global, leads to the precise analysis of food systems and estimation of upscaling benefits, as well as possible negative rebound effects associated with societal attitude. Moreover, such data-integrated assessment systems allow transparency of chains, integration of nutritional and environmental properties, and construction of personalized nutrition technologies.
... In this paper, the researcher conceptualised globality as the mutual encroachment of various cultures, technologies and theories, to move it from the periphery to the centre in order to learn and unlearn from each other and share various technologies that can contribute to the creation of sustainable futures. For example, many countries are moving towards smart farming, which produces greater harvests than traditional ways of farming (Bach and Mauser 2018;Chowhan and Dayya 2019;Saiz-Rubio and Rovira-Más 2020). Evoking globality in this paper aims to share best practices, which have the impetus to end poverty, social exclusion, and social injustice, while at the same time promoting the use of local resources. ...
Agriculture is a primary sector that contributes greatly to global economic growth. It provides food and employment for millions of people around the world, and its importance is increasing as the global population grows. However, the agricultural sector is subject to numerous challenges, including unpredictable weather patterns, limited water resources, and rising labor costs. In response to these challenges, smart agriculture, which incorporates advanced technologies such as the Internet of Things (IoT), robotics, sensors, and drones, has emerged as a promising solution. One of the key benefits of smart agriculture is the ability to optimize production processes and maximize crop yields. For example, IoT sensors can be used to monitor soil moisture, temperature, and other environmental factors, helping farmers make informed decisions on planting, fertilizing, and harvesting. Additionally, robots can perform tedious manual tasks such as planting, plowing, and harvesting, reducing the dependency on human labor and improving effectiveness. Furthermore, sensors can aid in the management of water resources by monitoring soil moisture levels and optimizing irrigation schedules, thereby reducing water waste and promoting sustainable agricultural practices. Drones can also be utilized in agriculture for various purposes, such as crop monitoring, pest detection, and precision spraying. This technology can improve crop yield and reduce the use of pesticides and other chemicals, leading to a safer and more sustainable agricultural system. In conclusion, smart agriculture has the potential to revolutionize the agricultural sector by integrating advanced technologies to enhance productivity, sustainability, and efficiency. With its ability to optimize production processes, minimize waste, and improve outcomes, smart agriculture offers a promising solution to many of the challenges faced by the agricultural sector.
