農業物聯網技術整合之研究‒以設施農業施作與病蟲害管理為例

廖敏勝

doi:10.6342/NTU201702889

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农业物联网技术整合之研究‒以设施农业施作与病虫害管理为例

Study on the Integration of Agricultural Internet of Things: Practical Cases of Facility Agriculture and Pest Management

廖敏胜 , 博士　　导师：江昭皑　　

英文 DOI： 10.6342/NTU201702889

智慧农业；农业物联网；设施农业；植物病虫害管理； Smart agriculture ； Agricultural Internet of things ； Facility agriculture ； Pest management

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摘要 │ 参考文献 (152) │ 国际计量

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Recently, Internet of things (IoT) technologies have been rapidly developed. IoTs create a new world which is transparent with faster speeds of information flows and innovation. IoTs are able to implement intelligent identification and information management, and they can be integrated to various applications. All industries try to employ IoT technologies to reduce labor costs, improve the quality and yield of products, and increase the value of their industries. For example, in recent years, agricultural issues have continuously gained attention from humans. All countries in the world are considering the development of agriculture, and wondering if food can be produced in more efficient and safe ways by putting their effort on improving agricultural food safety and enhancing the value of agricultural products. Smart agriculture that utilizes IoT technologies provides an excellent solution. This paper presents serval agricultural IoT applications in facility agriculture and pest management. The paper describes the solutions in accordance with the demand of each case and analyzes the benefits of using the IoT applications.
In the applications of facility agriculture, this paper actually deployed an IoT-based monitoring system in an orchid greenhouse and plant factories. In the greenhouse, for example, this paper has successfully monitored the growth of orchids and analyzed the relations between the environment factors and the growth of orchid leaf areas. The analyzed results indicate that the growth of the areas would slow down in a highly humid environment, thereby influenced the blossom quality. And, the analyzed results provided by the IoT-based monitoring system can help greenhouse owners update their farming strategies. 
For plant factories, an IoT-based monitoring system was able to detect and ventilate local high temperature areas in plant factories. The analyzed results indicate that the fresh weights and sales of the Boston lettuce both increased while the proposed IoT-based monitoring system was used in the plant factories.
For pest management, this paper has also actually deployed serval IoT-based monitoring systems in orchards and vegetable producing areas around Taiwan to monitor the population of different insect pests, including the oriental fruit fly (Bactrocera dorsalis) and tobacco cutworm (Spodoptera litura). The IoT-based monitoring system can monitor the oriental fruit fly or the tobacco cutworm in orchards or vegetable producing areas by using self-organizing maps to establish classification models. The experimental results show that the efficiency of the classification models was excellent, and the models can help the monitoring system identify whether a pest outbreak event or an error in the monitoring data occurs. Compared to traditional monitoring methods, the proposed IoT-based monitoring system can efficiently improve temporal and spatial resolutions of monitoring and reduce labor costs.

参考文献 ( 152 ) 〈TOP〉

Aboelela, E. H., and A. H. Khan. 2012. Wireless sensors and neural networks for intruder detection and classification. In “Proc. of 2012 International Conference on Information Networking”, pp. 138–143.
连结：
Adams, R., and L. Bischof. 1994. Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16(6): 641−647.
连结：
Atzori, L., A. Iera, and G. Morabito. 2010. The Internet of things: A survey. Comput. Netw. 54(15): 2787−2805.
连结：
Barreto, G. A., J. C. Mota, L. G. Souza, R. A. Frota, and L. Aguayo. 2006. Condition monitoring of 3G cellular network through competitive neural models. IEEE Trans. Neural Netw. 16(5): 1064–1075.
连结：
Bezdek, J. C., S. Rajasegarar, M. Moshtaghi, C. Leckie, M. Palaniswami, and T. C. Havens. 2011. Anomaly detection in environmental monitoring networks. IEEE Comput. Intell. Mag. 6(2): 52–58.
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引用含有DOI的文献

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。 如想对DOI的使用与概念有进一步了解，请参考 （ ） 。

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。如想对DOI的使用与概念有进一步了解，请参考（）。