A Granular GA-SVM Predictor for Big Data in Agricultural Cyber-Physical Systems
| العنوان: | A Granular GA-SVM Predictor for Big Data in Agricultural Cyber-Physical Systems |
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| المؤلفون: | Hua Jiang, Weizhen Rao, Xiaoyu Li, Junhu Ruan, Felix T.S. Chan, Yan Shi |
| المصدر: | IEEE Transactions on Industrial Informatics. 15:6510-6521 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | business.industry, Computer science, 020208 electrical & electronic engineering, Big data, Cyber-physical system, 02 engineering and technology, computer.software_genre, Computer Science Applications, Support vector machine, Granulation, Control and Systems Engineering, Agriculture, Kernel (statistics), Genetic algorithm, 0202 electrical engineering, electronic engineering, information engineering, Data mining, Precision agriculture, Electrical and Electronic Engineering, Dimension (data warehouse), business, computer, Information Systems |
| الوصف: | The connection of physical agriculture with corresponding cyber systems is helpful to achieve precision agriculture. Real-time data from agriculture sensors can provide decision supports to improve the yields and quality of agricultural products, but also bring about challenges one of which is how to mine useful information from these vast amounts of data at acceptable computation costs. To deal with the dimension disaster problem faced by most conventional mining algorithms, in this paper we combine granulation techniques and genetic algorithm (GA) with a support vector machine (SVM) to propose a granular GA-SVM. In the integrated predictor, three granulation methods, that is, Min–Median–Max granulation, Quartile–Median granulation, and fuzzy granulation, are introduced to break down big data in agricultural cyber-physical systems into small-scale granules, and GA is used to find the optimal values of SVM penalty parameter and kernel parameter from the reduced granules. Internet of Things (IoT) data from Luochuan Apple Experimental Demonstration Station in Shaanxi Province, China, verified that the proposed granular GA-SVM predictor is effective to make big data prediction with reduced computation time and equivalent accuracy. Moreover, the predicted environment information could provide guidance for growers achieving precise management of apple planting. |
| تدمد: | 1941-0050 1551-3203 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::eae362029fc565c8ee2957c52509563a https://doi.org/10.1109/tii.2019.2914158 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........eae362029fc565c8ee2957c52509563a |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19410050 15513203 |
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