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(This article belongs to the Special Issue Life Cycle Sustainability Assessment within the Theoretical Framework of Circular Economy: Methodological Challenges and Epistemological Implications for Agri-Food Sector)
Modern agricultural technology management is nowadays crucial in terms of the economy and the global market, while food safety, quality control, and environmentally friendly practices should not be neglected. This review aims to give perspectives on applying big data analytic and modern technologies to increase the efficacy and effectiveness of the coffee supply chain throughout the process. It was revealed that several tools such as wireless sensor networks, cloud computing, Internet of Things (IoT), image processing, convolutional neural networks (CNN), and remote sensing could be implemented in and used to improve the coffee supply chain. Those tools could help in reducing cost as well as time for entrepreneurs and create a reliable service for the customer. It can be summarized that in the long term, these modern technologies will be able to assist coffee business management and ensure the sustainable growth for the coffee industry.
Recently, many problems have arisen in the agricultural sector of Thailand. Especially, the agri-products do not meet international standards because of farmers’ lack of knowledge and marketing insight. Thus, they cannot be competitive with other entrepreneurs. Moreover, this will lead to a lack of opportunities for finding new markets and increasing market share in the future [1].
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These issues reflect the fact that farmers still lack the tools for sustainable development, particularly in terms of know-how about increasing productivity in the production process in the long term. Therefore, entrepreneurs are focusing on big data analysis for raising productivity and controlling the quality of the production process system. Thus, big data analysis can be applied for developing, improving, and increasing productivity and efficiency. Likewise, big data can be applied to operations and operating systems’ control to meet both farmers’ and consumers’ needs. Nowadays, more people are becoming interested in studying big data and applying it to organizations in the agricultural industry sector, as shown in Figure 1.
Moreover, big data can be applied in supply chain management for building organizations’ competitive advantage by using a data supply chain (DSC). It will help in examining interconnected data characteristics and their relationship to and impact on the organization. In the same way, it can assist with the agile monitoring and management of supply chain risks in the process management framework [2, 3]. Therefore, big data is one of the most essential tools in organization management and will likewise enable entrepreneurs to operate their businesses sustainably in the future [4].
Currently, technology is being applied in many agricultural sectors, where it is adopted to increase operational efficiency and create value in the business. Technology is used in the production industry to increase opportunities and productivity as well as to reduce costs, resources, and management. As a result, modern technology is progressing and contributing to greater efficiency [5]. Agricultural technology refers to technology or machines used for the agricultural production process, such as farmland, operations, and production [6]. It can help in the design and practical use of the agricultural process at all stages [7]. It comprises emerging digital technologies such as remote sensing [8, 9, 10, 11], wireless sensor networks (WSN) [12, 13], cloud computing [12, 14, 15], the Internet of Things (IoT) [13, 16, 17, 18], image processing [19, 20, 21], and convolutional neural networks (CNN) [19].
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Meanwhile, a smart farm is part of the agricultural revolution towards green agriculture necessary for the new world, with science and technology at the center of its operation process. Smart farming can increase a coffee plantation’s productivity and create high-quality coffee beans following customers’ needs [20]. Additionally, it is used to create innovation and improve the reality of customer demand and the appropriate redesign of the value chain. Smart farming can be used to control the quality of the environment and resources based on human food needs [21, 22]. Likewise, smart farming will lead to creating safe and environmentally friendly practices [23, 24]. Big data can help improve the forecasting and operational efficiency of large-scale farms in the future [25].
Hence, smart farming will lead to modern farms in which agricultural management with suitable and sophisticated technology will consist of sensors, devices, machines, and information technology. Additionally, smart farming will increase the efficiency and effectiveness of the agricultural production process, for example, using new technology to increase the value of products and integrating smart technology for cross-industry technology. These advanced technologies include (a) next-generation farms use traceability technology such as QR code, RFID, and blockchain; (b) precision agriculture and predictive analytics, for example, remote sensing, WSN, and CNN; (c) robotic and drone; (d) plant data/analysis such as vertical application, ad/media application, business intelligence, analytics and visualization, analytics infrastructure, and structured database; (e) smart irrigation, for example, data collection, irrigation control, 4G/3G/NB-IoT, data processing, failure alarm, and video surveillance; (f) farm management software consisting of cloud computing, the Internet of Things, CPS, and machine learning). Those technologies will help businesses increase their profitability, efficiency, safety, and environmentally friendly operations, as shown in Figure 1.
According to the outline mentioned above, the smart farming technologies played important roles in supplying organic agriculture products, which are now in higher demand [26]. The technologies could assist in control and reduce the use of chemicals, antibiotics, and synthetic chemical fertilizers, which is good for the health of both consumers and farmers. Big data can also enhance the production industry’s business model to become the future’s modern agricultural technology [27].
Apac Uam Report Ye2021
Sustainable growth is defined as the optimal growth rate that can be preserved without creating other significant economic, environmental, and human problems into the future [28]. Currently, sustainability has three aspects: (a) Economics—relevant to practically obtainable growth in operation and finance that a company could maintain without problems, also increased efficiency and productivity effectiveness. (b) Environment—the environmental aspect is manufacturing that is environmentally friendly (reuse, reverse, and recycle) in the long term without creating pollution to the environment, for example, economizing of water, reducing waste in production process and zero waste implementation, protecting soils by stopping insecticide and fertilizer, preserving land use, reducing the destruction of nature and forest to keep high biodiversity, and reducing pesticides and chemical usage. (c) Human—creating engagement within the community and creating cooperation between company and community [29]. It is among the most important issue for modern agricultural management.
Coffee is popular in the modern world. It is considered an important consumer product and economic crop that creates value for the food industry in domestic and export markets [30, 31]. Typical coffee supply chain from utmost upstream to downstream customer is shown in Figure 2. When analyzing the process, it was found that the cost of coffee production in Thailand from plantings to the roasted beans coffee is relatively high when compared with coffee-making from neighboring countries [32]. The major problems such as high cultivation costs, lack of cultivation standards to produce high-quality coffee, and unplanned incoming material (coffee cherry) that led to continuous losses and disappearing customer confidence were identified [33, 34, 35].
Problems and challenges in managing the coffee supply chain were identified. It was realized that they may be solved largely by adopting big data and smart technology in coffee farmer database management. Big data can potentially increase the coffee supply chain’s effectiveness in the long run [36, 37, 38, 39, 40]. Several technologies and related tools were discovered in solving coffee operations and production problems. Han et al. [41] applied SVM to increase production crop through yield prediction (90%). Pinto et al. [19] used the image processing technique for sorting defective coffee beans with higher efficiency of 72.4–98.7%. Moreover, Apiletti and Pastor [42] applied data mining for improving coffee quality when serving to downstream customers. It was clear that improving the performance of the coffee supply chain could be done via big data. Additionally, the sustainability aspect of the application is also of great interest to modern agricultural management.
Berkeley Moves Forward With Plan To Install 'smart Kiosks' In Shopping Districts
This review article will
Meanwhile, a smart farm is part of the agricultural revolution towards green agriculture necessary for the new world, with science and technology at the center of its operation process. Smart farming can increase a coffee plantation’s productivity and create high-quality coffee beans following customers’ needs [20]. Additionally, it is used to create innovation and improve the reality of customer demand and the appropriate redesign of the value chain. Smart farming can be used to control the quality of the environment and resources based on human food needs [21, 22]. Likewise, smart farming will lead to creating safe and environmentally friendly practices [23, 24]. Big data can help improve the forecasting and operational efficiency of large-scale farms in the future [25].
Hence, smart farming will lead to modern farms in which agricultural management with suitable and sophisticated technology will consist of sensors, devices, machines, and information technology. Additionally, smart farming will increase the efficiency and effectiveness of the agricultural production process, for example, using new technology to increase the value of products and integrating smart technology for cross-industry technology. These advanced technologies include (a) next-generation farms use traceability technology such as QR code, RFID, and blockchain; (b) precision agriculture and predictive analytics, for example, remote sensing, WSN, and CNN; (c) robotic and drone; (d) plant data/analysis such as vertical application, ad/media application, business intelligence, analytics and visualization, analytics infrastructure, and structured database; (e) smart irrigation, for example, data collection, irrigation control, 4G/3G/NB-IoT, data processing, failure alarm, and video surveillance; (f) farm management software consisting of cloud computing, the Internet of Things, CPS, and machine learning). Those technologies will help businesses increase their profitability, efficiency, safety, and environmentally friendly operations, as shown in Figure 1.
According to the outline mentioned above, the smart farming technologies played important roles in supplying organic agriculture products, which are now in higher demand [26]. The technologies could assist in control and reduce the use of chemicals, antibiotics, and synthetic chemical fertilizers, which is good for the health of both consumers and farmers. Big data can also enhance the production industry’s business model to become the future’s modern agricultural technology [27].
Apac Uam Report Ye2021
Sustainable growth is defined as the optimal growth rate that can be preserved without creating other significant economic, environmental, and human problems into the future [28]. Currently, sustainability has three aspects: (a) Economics—relevant to practically obtainable growth in operation and finance that a company could maintain without problems, also increased efficiency and productivity effectiveness. (b) Environment—the environmental aspect is manufacturing that is environmentally friendly (reuse, reverse, and recycle) in the long term without creating pollution to the environment, for example, economizing of water, reducing waste in production process and zero waste implementation, protecting soils by stopping insecticide and fertilizer, preserving land use, reducing the destruction of nature and forest to keep high biodiversity, and reducing pesticides and chemical usage. (c) Human—creating engagement within the community and creating cooperation between company and community [29]. It is among the most important issue for modern agricultural management.
Coffee is popular in the modern world. It is considered an important consumer product and economic crop that creates value for the food industry in domestic and export markets [30, 31]. Typical coffee supply chain from utmost upstream to downstream customer is shown in Figure 2. When analyzing the process, it was found that the cost of coffee production in Thailand from plantings to the roasted beans coffee is relatively high when compared with coffee-making from neighboring countries [32]. The major problems such as high cultivation costs, lack of cultivation standards to produce high-quality coffee, and unplanned incoming material (coffee cherry) that led to continuous losses and disappearing customer confidence were identified [33, 34, 35].
Problems and challenges in managing the coffee supply chain were identified. It was realized that they may be solved largely by adopting big data and smart technology in coffee farmer database management. Big data can potentially increase the coffee supply chain’s effectiveness in the long run [36, 37, 38, 39, 40]. Several technologies and related tools were discovered in solving coffee operations and production problems. Han et al. [41] applied SVM to increase production crop through yield prediction (90%). Pinto et al. [19] used the image processing technique for sorting defective coffee beans with higher efficiency of 72.4–98.7%. Moreover, Apiletti and Pastor [42] applied data mining for improving coffee quality when serving to downstream customers. It was clear that improving the performance of the coffee supply chain could be done via big data. Additionally, the sustainability aspect of the application is also of great interest to modern agricultural management.
Berkeley Moves Forward With Plan To Install 'smart Kiosks' In Shopping Districts
This review article will
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