Biblio

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2021-04-27
Vuppalapati, C., Ilapakurti, A., Kedari, S., Vuppalapati, R., Vuppalapati, J., Kedari, S..  2020.  The Role of Combinatorial Mathematical Optimization and Heuristics to improve Small Farmers to Veterinarian access and to create a Sustainable Food Future for the World. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :214–221.
The Global Demand for agriculture and dairy products is rising. Demand is expected to double by 2050. This will challenge agriculture markets in a way we have not seen before. For instance, unprecedented demand to increase in dairy farm productivity of already shrinking farms, untethered perpetual access to veterinarians by small dairy farms, economic engines of the developing countries, for animal husbandry and, finally, unprecedented need to increase productivity of veterinarians who're already understaffed, over-stressed, resource constrained to meet the current global dairy demands. The lack of innovative solutions to address the challenge would result in a major obstacle to achieve sustainable food future and a colossal roadblock ending economic disparities. The paper proposes a novel innovative data driven framework cropped by data generated using dairy Sensors and by mathematical formulations using Solvers to generate an exclusive veterinarian daily farms prioritized visit list so as to have a greater coverage of the most needed farms performed in-time and improve small farmers access to veterinarians, a precious and highly shortage & stressed resource.
2021-03-29
Roy, S., Dey, D., Saha, M., Chatterjee, K., Banerjee, S..  2020.  Implementation of Fuzzy Logic Control in Predictive Analysis and Real Time Monitoring of Optimum Crop Cultivation : Fuzzy Logic Control In Optimum Crop Cultivation. 2020 10th International Conference on Cloud Computing, Data Science Engineering (Confluence). :6—11.

In this article, the writers suggested a scheme for analyzing the optimum crop cultivation based on Fuzzy Logic Network (Implementation of Fuzzy Logic Control in Predictive Analysis and Real Time Monitoring of Optimum Crop Cultivation) knowledge. The Fuzzy system is Fuzzy Logic's set. By using the soil, temperature, sunshine, precipitation and altitude value, the scheme can calculate the output of a certain crop. By using this scheme, the writers hope farmers can boost f arm output. This, thus will have an enormous effect on alleviating economical deficiency, strengthening rate of employment, the improvement of human resources and food security.

2021-04-27
Abraham, A., Kumar, M. B. Santosh.  2020.  A study on using private-permissioned blockchain for securely sharing farmers data. 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA). :103—106.
In agriculture, farmers are the most important entity. For supporting farmers in increasing productivity and efficiency, the government offers subsidies, loans, insurances, and so on. This paper explores the usage of Blockchain technology for securing farmer's data in the Indian scenario. The farmer needs to register through the multiple official registration systems for availing different schemes and information provided by the country. The personnel and crop-based details of each farmer are collected at the time of registration. The filing also helps in providing better services to farmers like connecting farmers and traders to ensure a fair price for quality crops, advice to farmers of agricultural practices and location. In this paper, a blockchain-based farmer's data securing system is proposed to provide data provenance and transparency of the information entered in the system. While registering, the data is collected, and it is verified. A single verified record of farmers accessed by various government agriculture departments were designed using the Hyperledger fabric framework.
2020-03-23
Bothe, Alexander, Bauer, Jan, Aschenbruck, Nils.  2019.  RFID-assisted Continuous User Authentication for IoT-based Smart Farming. 2019 IEEE International Conference on RFID Technology and Applications (RFID-TA). :505–510.
Smart Farming is driven by the emergence of precise positioning systems and Internet of Things technologies which have already enabled site-specific applications, sustainable resource management, and interconnected machinery. Nowadays, so-called Farm Management Information Systems (FMISs) enable farm-internal interconnection of agricultural machines and implements and, thereby, allow in-field data exchange and the orchestration of collaborative agricultural processes. Machine data is often directly logged during task execution. Moreover, interconnection of farms, agricultural contractors, and marketplaces ease the collaboration. However, current FMISs lack in security and particularly in user authentication. In this paper, we present a security architecture for a decentralized, manufacturer-independent, and open-source FMIS. Special attention is turned on the Radio Frequency Identification (RFID)-based continuous user authentication which greatly improves security and credibility of automated documentation, while at the same time preserves usability in practice.
2020-03-12
Gawanmeh, Amjad, Parvin, Sazia, Venkatraman, Sitalakshmi, de Souza-Daw, Tony, Kang, James, Kaspi, Samuel, Jackson, Joanna.  2019.  A Framework for Integrating Big Data Security Into Agricultural Supply Chain. 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService). :191–194.

In the era of mass agriculture to keep up with the increasing demand for food production, advanced monitoring systems are required in order to handle several challenges such as perishable products, food waste, unpredictable supply variations and stringent food safety and sustainability requirements. The evolution of Internet of Things have provided means for collecting, processing, and communicating data associated with agricultural processes. This have opened several opportunities to sustain, improve productivity and reduce waste in every step in the food supply chain system. On the hand, this resulted in several new challenges, such as, the security of the data, recording and representation of data, providing real time control, reliability of the system, and dealing with big data. This paper proposes an architecture for security of big data in the agricultural supply chain management system. This can help in reducing food waste, increasing the reliability of the supply chain, and enhance the performance of the food supply chain system.

2020-05-22
Ahsan, Ramoza, Bashir, Muzammil, Neamtu, Rodica, Rundensteiner, Elke A., Sarkozy, Gabor.  2019.  Nearest Neighbor Subsequence Search in Time Series Data. 2019 IEEE International Conference on Big Data (Big Data). :2057—2066.
Continuous growth in sensor data and other temporal sequence data necessitates efficient retrieval and similarity search support on these big time series datasets. However, finding exact similarity results, especially at the granularity of subsequences, is known to be prohibitively costly for large data sets. In this paper, we thus propose an efficient framework for solving this exact subsequence similarity match problem, called TINN (TIme series Nearest Neighbor search). Exploiting the range interval diversity properties of time series datasets, TINN captures similarity at two levels of abstraction, namely, relationships among subsequences within each long time series and relationships across distinct time series in the data set. These relationships are compactly organized in an augmented relationship graph model, with the former relationships encoded in similarity vectors at TINN nodes and the later captured by augmented edge types in the TINN Graph. Query processing strategy deploy novel pruning techniques on the TINN Graph, including node skipping, vertical and horizontal pruning, to significantly reduce the number of time series as well as subsequences to be explored. Comprehensive experiments on synthetic and real world time series data demonstrate that our TINN model consistently outperforms state-of-the-art approaches while still guaranteeing to retrieve exact matches.
2020-01-27
Xuefeng, He, Chi, Zhang, Yuewu, Jing, Xingzheng, Ai.  2019.  Risk Evaluation of Agricultural Product Supply Chain Based on BP Neural Network. 2019 16th International Conference on Service Systems and Service Management (ICSSSM). :1–8.

The potential risk of agricultural product supply chain is huge because of the complex attributes specific to it. Actually the safety incidents of edible agricultural product emerge frequently in recent years, which expose the fragility of the agricultural product supply chain. In this paper the possible risk factors in agricultural product supply chain is analyzed in detail, the agricultural product supply chain risk evaluation index system and evaluation model are established, and an empirical analysis is made using BP neural network method. The results show that the risk ranking of the simulated evaluation is consistent with the target value ranking, and the risk assessment model has a good generalization and extension ability, and the model has a good reference value for preventing agricultural product supply chain risk.

2019-09-04
Xiong, M., Li, A., Xie, Z., Jia, Y..  2018.  A Practical Approach to Answer Extraction for Constructing QA Solution. 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC). :398–404.
Question Answering system(QA) plays an increasingly important role in the Internet age. The proportion of using the QA is getting higher and higher for the Internet users to obtain knowledge and solve problems, especially in the modern agricultural filed. However, the answer quality in QA varies widely due to the agricultural expert's level. Answer quality assessment is important. Due to the lexical gap between questions and answers, the existing approaches are not quite satisfactory. A practical approach RCAS is proposed to rank the candidate answers, which utilizes the support sets to reduce the impact of lexical gap between questions and answers. Firstly, Similar questions are retrieved and support sets are produced with their high-quality answers. Based on the assumption that high quality answers would also have intrinsic similarity, the quality of candidate answers are then evaluated through their distance from the support sets. Secondly, Different from the existing approaches, previous knowledge from similar question-answer pairs are used to bridge the straight lexical and semantic gaps between questions and answers. Experiments are implemented on approximately 0.15 million question-answer pairs about agriculture, dietetics and food from Yahoo! Answers. The results show that our approach can rank the candidate answers more precisely.
2017-09-22
L. Vacek, E. Atter, P. Rizo, B. Nam, R. Kortvelesy, D. Kaufman, J. Das, V. Kumar.  2017.  sUAS for Deployment and Recovery of an Environmental Sensor Probe. IEEE International Conference on Unmanned Aircraft Systems (ICUAS) 2017.

Small Unmanned Aircraft Systems (sUAS) are already revolutionizing agricultural and environmental monitoring through the acquisition of high-resolution multi-spectral imagery on-demand. However, in order to accurately understand various complex environmental and agricultural processes, it is often necessary to collect physical samples of pests, pathogens, and insects from the field for ex-situ analysis. In this paper, we describe a sUAS for autonomous deployment and recovery of a novel environmental sensor probe. We present the UAS software and hardware stack, and a probe design that can be adapted to collect a variety of environmental samples and can be transported autonomously for off-site analysis. Our team participated in an NSF-sponsored student unmanned aerial vehicle (UAV) challenge, where we used our sUAS to deploy and recover a scale-model mosquito trap outdoors. Results from indoor and field trials are presented, and the challenges experienced in detecting and docking with the probe in outdoor conditions are discussed.

2018-03-19
Pundir, N., Hazari, N. A., Amsaad, F., Niamat, M..  2017.  A Novel Hybrid Delay Based Physical Unclonable Function Immune to Machine Learning Attacks. 2017 IEEE National Aerospace and Electronics Conference (NAECON). :84–87.

In this paper, machine learning attacks are performed on a novel hybrid delay based Arbiter Ring Oscillator PUF (AROPUF). The AROPUF exhibits improved results when compared to traditional Arbiter Physical Unclonable Function (APUF). The challenge-response pairs (CRPs) from both PUFs are fed to the multilayered perceptron model (MLP) with one hidden layer. The results show that the CRPs generated from the proposed AROPUF has more training and prediction errors when compared to the APUF, thus making it more difficult for the adversary to predict the CRPs.

2017-12-28
Suebsombut, P., Sekhari, A., Sureepong, P., Ueasangkomsate, P., Bouras, A..  2017.  The using of bibliometric analysis to classify trends and future directions on \#x201C;smart farm \#x201D;. 2017 International Conference on Digital Arts, Media and Technology (ICDAMT). :136–141.

Climate change has affected the cultivation in all countries with extreme drought, flooding, higher temperature, and changes in the season thus leaving behind the uncontrolled production. Consequently, the smart farm has become part of the crucial trend that is needed for application in certain farm areas. The aims of smart farm are to control and to enhance food production and productivity, and to increase farmers' profits. The advantages in applying smart farm will improve the quality of production, supporting the farm workers, and better utilization of resources. This study aims to explore the research trends and identify research clusters on smart farm using bibliometric analysis that has supported farming to improve the quality of farm production. The bibliometric analysis is the method to explore the relationship of the articles from a co-citation network of the articles and then science mapping is used to identify clusters in the relationship. This study examines the selected research articles in the smart farm field. The area of research in smart farm is categorized into two clusters that are soil carbon emission from farming activity, food security and farm management by using a VOSviewer tool with keywords related to research articles on smart farm, agriculture, supply chain, knowledge management, traceability, and product lifecycle management from Web of Science (WOS) and Scopus online database. The major cluster of smart farm research is the soil carbon emission from farming activity which impacts on climate change that affects food production and productivity. The contribution is to identify the trends on smart farm to develop research in the future by means of bibliometric analysis.