Digital Livestock Farming
As the global human population increases, livestock agriculture must adapt to provide more livestock products and with improved efficiency while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this paper is to critically review the current state of the art in digitalizing animal agriculture with Precision Livestock Farming (PLF) technologies, specifically biometric sensors, big data, and blockchain technology. Biometric sensors include either noninvasive or invasive sensors that monitor an individual animal’s health and behavior in real time, allowing farmers to integrate this data for population-level analyses. Real-time information from biometric sensors is processed and integrated using big data analytics systems that rely on statistical algorithms to sort through large, complex data sets to provide farmers with relevant trending patterns and decision-making tools. Sensors enabled blockchain technology affords secure and guaranteed traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. Thanks to PLF technologies, livestock agriculture has the potential to address the abovementioned pressing concerns by becoming more transparent and fostering increased consumer trust. However, new PLF technologies are still evolving and core component technologies (such as blockchain) are still in their infancy and insufficiently validated at scale. The next generation of PLF technologies calls for preventive and predictive analytics platforms that can sort through massive amounts of data while accounting for specific variables accurately and accessibly. Issues with data privacy, security, and integration need to be addressed before the deployment of multi-farm shared PLF solutions be- comes commercially feasible. Implications Advanced digitalization technologies can help modern farms optimize economic contribution per animal, reduce the drudgery of repetitive farming tasks, and overcome less effective isolated solutions. There is now a strong cultural emphasis on reducing animal experiments and physical contact with animals in-order-to enhance animal welfare and avoid disease outbreaks. This trend has the potential to fuel more research on the use of novel biometric sensors, big data, and blockchain technology for the mutual benefit of livestock producers, consumers, and the farm animals themselves. Farmers’ autonomy and data-driven farming approaches compared to experience-driven animal manage- ment practices are just several of the multiple barriers that digitalization must overcome before it can become widely implemented.
Keywords: Precision Livestock Farming | digitalization | Digital Technologies in Livestock Systems | sensor technology | big data | blockchain | data models | livestock agriculture
Feature based classification of voice based biometric data through Machine learning algorithm
طبقه بندی مبتنی بر ویژگی داده های بیومتریک مبتنی بر صدا از طریق الگوریتم یادگیری ماشین-2021
In the era of big data and growing artificial intelligence, the requirement and necessity of biometric identification increase in a rapid manner. The digitalization and recent Pandemic crisis gives a boost to need to authorized identification which get fulfilled with biometric identification. Our paper focuses on same concept of checking the identification accuracy of machine learning algorithm REPTree on selected bio- metric dataset which is being deployed and evaluated on a data mining tool WEKA. Our target is to achieve more or equal to 95 percentages in order to predict the given sample data is accurately classified into our target variables values i.e. male female. The selected algorithm REPTree is a kind of decision tree classification algorithm which works on same concept as C4.5 and decision tree algorithm with speciality of generation of both kind of output i.e. discrete and continuous. The selection of algorithm gives us ben- efits with achievement of higher accuracy and selection of dataset also become easy with some required modification and pre-processing of data with some dimension reduction filters.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Con- ference on Computations in Materials and Applied Engineering – 2021.
Keywords: Prediction | Biometric data | Voice samples | Male | Female | Cost complexity pruning (CCP) | Dimension reduction
To introduce a store brand or not: Roles of market information in supply chains
معرفی یک نام تجاری فروشگاهی یا نه: نقش اطلاعات بازار در زنجیره های تأمین-2021
Retailers in multiple market segments have opposing business practices regarding whether to introduce a store brand. Assuming that manufacturers and retailers have symmetric knowledge on market information, prior literature has shown that retailers have incentive to introduce a store brand in cases in which the store brand intensively competes with the national brand. Information asymmetry, however, is prevalent in supply chains. In the era of big data, the latest advances in technology intensify information asymmetry by allowing retailers to access market information with improved accuracy. To address such information asymmetry, some retailers share information with manufacturers, while others do not. The gap between prior studies and current industrial practices motivates us to explore the roles of market information in supply chains regarding store brand
Keywords: Store brand | National brand | Market information | Information accuracy | Information sharing
A framework based on BWM for big data analytics (BDA) barriers in manufacturing supply chains
چارچوبی مبتنی بر BWM برای موانع تجزیه و تحلیل داده های بزرگ (BDA) در تولید زنجیره های تأمین-2021
Due to its potential utility, Big Data (BD) recently attracted researchers and practitioners in decision- making. Big Data analytics (BDA) becomes more common among manufacturing companies because it lets them gain insight and make decisions based on BD. Given the importance of both BD and BDA, this study aims to identify and analyse essential BDA adoption barriers in supply chains. This study explores the current knowledge base using a BWM (Best Worst Method) to discuss these barriers. Data were obtained from five Indian manufacturing companies. Research findings show that data-related barriers are most significant. The findings will help managers understand the exact nature of the challenges and possible advantages of the BDA and implement BDA policies for the growth and output of supply chain operations.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 3rd International e-Con- ference on Frontiers in Mechanical Engineering and nano Technology.
Keywords: Big data analytics | Barriers | Manufacturing supply chains | Best worst method (BWM)
Circular supply chain management with large scale group decision making in the big data era: The macro-micro model
مدیریت زنجیره تأمین دایره ای با تصمیم گیری گروهی در مقیاس بزرگ در عصر داده های بزرگ: مدل خرد خرد-2021
Today, achieving the circular economy is a common goal for many enterprises and governments all around the world. In the big data era, decision making is well-supported and enhanced by a massive amount of data. In particular, large scale group decision making (LSGDM), which refers to the case in which a lot of decision makers join the decision making process, has emerged. Social network analyses are known to be relevant to LSGDM. In this paper, we examine the literature on LSGDM and highlight the current methodological advances in the area. We review the works focusing on applications of LSGDM. We study how big data can be used in circular supply chains. Based on the reviewed studies, we further construct the three-stage LSGDM CSCM micro framework as well as the five-step LSGDM CSCM macro framework (with a feedback loop) and form the Macro-Micro Model. We discuss how the Macro-Micro Model can help to support circular supply chain management (CSCM). We propose future research directions and areas. This paper contributes by being the first study uncovering systematically how LSGDM can be applied to support CSCM in the big data era using the Macro-Micro Model.
Keywords: Large scale group decision making (LSGDM) | Circular supply chains | Research agenda | Literature review | Frameworks | Macro-micro model
A fuzzy based hybrid decision framework to circularity in dairy supply chains through big data solutions
چارچوب تصمیم ترکیبی مبتنی بر فازی برای مدور بودن در زنجیره های تامین لبنیات از طریق راه حل های داده های بزرگ-2021
This study determines the potential barriers to achieving circularity in dairy supply chains; it proposes a framework which covers big data driven solutions to deal with the suggested barriers. The main contribution of the study is to propose a framework by making ideal matching and ranking of big data solutions to barriers to circularity in dairy supply chains. This framework further offers a specific roadmap as a practical contribution while investigating companies with restricted resources. In this study the main barriers are classified as ‘eco- nomic’, ‘environmental’, ‘social and legal’, ‘technological’, ‘supply chain management’ and ‘strategic’ with twenty-seven sub-barriers. Various big data solutions such as machine learning, optimization, data mining, cloud computing, artificial neural network, statistical techniques and social network analysis have been suggested. Big data solutions are matched with circularity focused barriers to show which solutions succeed in overcoming barriers. A hybrid decision framework based on the fuzzy ANP and the fuzzy VIKOR is developed to find the weights of the barriers and to rank the big data driven solutions. The results indicate that among the main barriers, ‘economic’ was of the highest importance, followed by ‘technological’, ‘environmental’, ‘strategic’, ‘supply chain management’ then ‘social and legal barrier’ in dairy supply chains. In order to overcome circularity focused barriers, ‘optimization’ is determined to be the most important big data solution. The other solutions to overcoming proposed challenges are ‘data mining’, ‘machine learning’, ‘statistical techniques’ and ‘artificial neural network’ respectively. The suggested big data solutions will be useful for policy makers and managers to deal with potential barriers in implementing circularity in the context of dairy supply chains.
Keywords: Dairy supply chain | Barriers | Circular economy | Big data solution | Fuzzy ANP - VIKOR | Group decision making system
Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks
پدیده های گیاهی در زمان واقعی تحت راه اندازی رباتیک کشاورزی: یک پلت فرم مبتنی بر دید برای کارهای پیچیده فنوتیپ سازی گیاهان-2021
Plant phenotyping in general refers to quantitative estimation of the plant’s anatomical, ontogenetical, physiological and biochemical properties. Analyzing big data is challenging, and non-trivial given the different complexities involved. Efficient processing and analysis pipelines are the need of the hour with the increasing popularity of phenotyping technologies and sensors. Through this work, we largely address the overlapping object segmentation & localization problem. Further, we dwell upon multi-plant pipelines that pose challenges as detection and multi-object tracking becomes critical for single frame/set of frames aimed towards uniform tagging & visual features extraction. A plant phenotyping tool named RTPP (Real-Time Plant Phenotyping) is presented that can aid in the detection of single/multi plant traits, modeling, and visualization for agricultural settings. We compare our system with the plantCV platform. The relationship of the digital estimations, and the measured plant traits are discussed that plays a vital roadmap towards precision farming and/or plant breeding.
Keywords: Phenotype | Image processing | Spectral | Robotics | Object localization | Precision agriculture | Plant science | Pattern recognition | Computer vision | Automation | Perception
A big data based architecture for collaborative networks: Supply chains mixed-network
یک معماری مبتنی بر داده های بزرگ برای شبکه های مشارکتی: شبکه های مخلوط شبکه های تأمین-2021
Nowadays, the world knows a high-speed development and evolution of technologies, vulnerable economic environments, market changes, and personalised consumer trends. The issue and challenge related to enterprises networks design are more and more critical. These networks are often designed for short terms since their strategies must be competitive and better adapted to the environment, social and economical changes. As a solution, to design a flexible and robust network, it is necessary to deal with the trade-off between conflicting qualitative and quantitative criteria such as cost, quality, delivery time, and competition, etc. To this end, using Big Data (BD) as emerging technology will enhance the real performances of these kinds of networks. Moreover, even if the literature is rich with BD models and frameworks developed for a single supply chain network (SCN), there is a real need to scale and extend these BD models to networked supply chains (NSCs). To do so, this paper proposes a BD architecture to drive a mixed-network of SCs that collaborate in serial and parallel fashions. The collaboration is set up by sharing their resources, capabilities, competencies, and information to imitate a unique organisation. The objective is to increase internal value to their shareholders (where value is seen as wealth) and deliver better external value to the end-customer (where value represents customer satisfaction). Within a mixed-network of SCs, both values are formally calculated considering both serial and parallel networks configurations. Besides, some performance factors of the proposed BD architecture such as security, flexibility, robustness and resilience are discussed.
Keywords: Big data architecture | Collaborative networks | Enterprises network | Supply chain network | Flexibility | Robustness
The quiet revolution in machine vision - a state-of-the-art survey paper, including historical review, perspectives, and future directions
انقلاب آرام در بینایی ماشین-مقاله ای پیشرفته مروری، شامل مرور تاریخی ، چشم اندازها و جهت های آینده-2021
Over the past few years, what might not unreasonably be described as a true revolution has taken place in the ﬁeld of machine vision, radically altering the way many things had previously been done and offering new and exciting opportunities for those able to quickly embrace and master the new techniques. Rapid developments in machine learning, largely enabled by faster GPU-equipped computing hardware, has facilitated an explosion of machine vision applications into hitherto extremely challenging or, in many cases, previously impossible to automate industrial tasks. Together with developments towards an internet of things and the availability of big data, these form key components of what many consider to be the fourth industrial revolution. This transformation has dramatically improved the efﬁcacy of some existing machine vision activities, such as in manufacturing (e.g. inspection for quality control and quality assurance), security (e.g. facial biometrics) and in medicine (e.g. detecting cancers), while in other cases has opened up completely new areas of use, such as in agriculture and construction (as well as in the existing domains of manufacturing and medicine). Here we will explore the history and nature of this change, what underlies it, what enables it, and the impact it has had - the latter by reviewing several recent indicative applications described in the research literature. We will also consider the continuing role that traditional or classical machine vision might still play. Finally, the key future challenges and developing opportunities in machine vision will also be discussed.© 2021 Elsevier B.V. All rights reserved.
Keywords: Machine vision | Machine learning | Deep learning | State-of-the-art
Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery
رویکرد بینایی رایانه ای برای توصیف فنوتیپ های اندازه و شکل محصولات باغی با استفاده از تصاویر با توان بالا-2021
For many horticultural crops, variation in quality (e.g., shape and size) contributes significantly to the crop’s market value. Metrics characterizing less subjective harvest quantities (e.g., yield and total biomass) areroutinely monitored. In contrast, metrics quantifying more subjective crop quality characteristics such as ideal size and shape remain difficult to characterize objectively at the production-scale due to the lack of modular technologies for high-throughput sensing and computation. Several horticultural crops are sent to packing facilities after having been harvested, where they are sorted into boxes and containers using high-throughput scanners. These scanners capture images of each fruit or vegetable being sorted and packed, but the images are typically used solely for sorting purposes and promptly discarded. With further analysis, these images could offer unparalleled insight on how crop quality metrics vary at the industrial production-scale and provide further insight into how these characteristics translate to overall market value. At present, methods for extracting and quantifying quality characteristics of crops using images generated by existing industrial infrastructure have not been developed. Furthermore, prior studies that investigated horticultural crop quality metrics, specifically of size and shape, used a limited number of samples, did not incorporate deformed or non-marketable samples, and did not use images captured from high-throughput systems. In this work, using sweetpotato (SP) as a use case, we introduce a computer vision algorithm for quantifying shape and size characteristics in a high-throughput manner. This approach generates 3D model of SPs from two 2D images captured by an industrial sorter 90 degrees apart and extracts 3D shape features in a few hundred milliseconds. We applied the 3D reconstruction and feature extraction method to thousands of image samples to demonstrate how variations in shape features across SP cultivars can be quantified. We created a SP shape dataset containing SP images, extracted shape features, and qualitative shape types (U.S. No. 1 or Cull). We used this dataset to develop a neural network-based shape classifier that was able to predict Cull vs. U.S. No. 1 SPs with 84.59% accuracy. In addition, using univariate Chi-squared tests and random forest, we identified the most important features for determining qualitative shape type (U.S. No. 1 or Cull) of the SPs. Our study serves as a key step towards enabling big data analytics for industrial SP agriculture. The methodological framework is readily transferable to other horticultural crops, particularly those that are sorted using commercial imaging equipment.
Keywords: Crop phenotyping | Machine learning | Computer vision