臺灣博碩士論文加值系統

物聯網於近年來開始蓬勃發展，為人類帶來一個資訊透明與快速交換的世界。物聯網可實現智能化識別、資訊管理並融入各種應用當中。各產業無不想方設法導入物聯網，藉以降低成本、提升產品品質與產量，進而增加產業價值。舉例而言，近年來，農業因氣候環境變遷以及食品安全等議題，持續地被世人關注，世界各國無不在考量在農業發展上，可否用更具效率以及安全的方式生產，同時解決農業糧食問題與提升農產品價值。利用物聯網技術打造智慧農業，提供了一個兩全其美的解決方案。本論文提出多個農業物聯網之應用案例，包含設施農業與病蟲害管理，內容闡述如何依照各案例之需求提出解決方案，並分析其效益。
在設施農業上，本論文實際設置一套物聯網監測系統於蘭花溫室與植物工廠中。在蘭花溫室中，本論文可成功監測與分析蘭花生長環境與蘭花葉面積生長情況。根據分析結果，可得知蘭花處於高濕度之環境，其葉面積成長較緩慢，進而影響開花品質。藉由物聯網系統所提供之量化數據與結果，可有效幫助蘭花業者計畫耕種策略。在植物工廠中，本論文可即時偵測區域高溫，並對其進行通風，有效地提升波士頓萵苣之鮮重以及銷售價格。
在病蟲害管理上，本論文亦實際設置多套物聯網監測系統於臺灣各地之重要蔬果產區，並監測多種害蟲，包含東方果實蠅與斜紋夜蛾。此物聯網監測系統可監測該果園受到東方果實蠅或斜紋夜蛾之危害的程度，並利用自組織映射圖網路建立資料分類模型。實驗結果顯示資料分類模型，對檢測事件類型的判定效果極佳，有助於系統鑑別該果園是否達蟲害爆發之程度，以及感測資料是否異常或發生故障。相較於現有之人工定時監測方式，本論文可有效提升監測時空解析度。

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.

口試委員會審定書 I
誌謝 II
摘要 III
Abstract IV
Contents VII
List of figures XII
List of tables XXI
Chapter 1. Introduction 1
1.1 Motivations 1
1.2 Introduction of Internet of things (IoT) 2
1.2.1 Architecture of IoT 3
1.2.2 Goals of IoT 5
1.3 Research contributions 6
1.4 Organization of the dissertation 8
Chapter 2. Core technology of IoT 10
2.1 Perception layer 10
2.1.1 Sensors 11
2.1.2 Signal processing 12
2.2 Transportation layer 13
2.3 Real applications in IoT 16
Chapter 3. A case study for facility agriculture: Greenhouse 18
3.1 Overview in facility agriculture: Greenhouse 18
3.2 Monitoring subject and state-of-art in facility agriculture: Greenhouse 20
3.3 IoT-based monitoring system in facility agriculture: Greenhouse 24
3.3.1 Wireless sensor node (SN) 26
3.3.2 Gateway 28
3.3.3 IoT-based wireless imaging platform 31
3.3.4 Host analysis platform 34
3.4 Problems that plant inspection encounter in a modern greenhouse 34
3.4.1 Preparation of inspection targets 34
3.4.2 Image processing algorithm for estimating the leaf area of inspection targets 37
3.5 Experimental results of the IoT-based monitoring system used in the greenhouse 53
3.5.1 Calibration of sensors 53
3.5.2 Basic performance evaluation of the IoT-based monitoring system used in the greenhouse 57
3.5.2 Statistical analysis of the abiotic factors in the cultivation regions 62
3.5.3 Impacts of environmental factors on leaf area growth 66
3.6 Summary 71
Chapter 4. A case study for facility agriculture: Plant factory 74
4.1 Overview in facility agriculture: Plant factory 74
4.2 Monitoring subject and state-of-art in facility agriculture: Plant factory 76
4.3 IoT-based monitoring system in facility agriculture: Plant factory 79
4.3.1 IoT-based temperature monitoring system used in plant factories 81
4.3.2 Autonomous Fan-ventilating system 85
4.4 Problems that plant grow in a plant factory 87
4.4.1 Determination of the LHTA based on the OKSI 87
4.4.2 Autonomous LHTA detection algorithm 90
4.5 Experimental results and discussion 96
4.5.1 Performance evaluation of the proposed system 96
4.5.2 Evaluation of the estimation accuracy of the OKSI 100
4.5.3 Pilot experiment 102
4.5.4 The performance of the proposed system deployed in a large-scale plant factory 106
4.5.6 The economics analysis of the proposed system 110
4.6 Summary 112
Chapter 5. A case study for pest management 114
5.1 Overview in pest management 114
5.2 Monitoring subject and state-of-art in pest management 115
5.3 IoT-based monitoring system for pest management 119
5.3.1 Wireless automatic counting trap 121
5.3.2 Remote monitoring gateway 124
5.3.3 Host control platform 126
5.4 Problems that Sensors Encounter in Wild Environment 128
5.4.1 State-of-the-art sensor fault identification technique 130
5.4.2 Artificial neural networks 130
5.4.3 Other data clustering algorithms 132
5.5 Experimental procedures 136
5.5.1 Events, input vector, and pre-processing 137
5.5.2 Self-organizing maps 139
5.5.3 Boundary determination via support vector machines 143
5.6 Results and Discussion 145
5.6.1 Deployment of the IoT-based monitoring system for pest management 145
5.6.2 Performance evaluation of the proposed adaptive classification approach 151
5.7 Summary 164
Chapter 6. Conclusions and future work 166
Appendix 170
Abbreviations 170
Nomenclature 174
Greek symbols 177
Reference 179
Publication list 198

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.
Adams, R., and L. Bischof. 1994. Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16(6): 641−647.
Anthura, B. V., 2007. Cultivation Guidelines Phalaenopsis Pot Plant. Available at: http://www.anthura.nl/uploads/downloads/manuals/en/Manual%20Phalaenopsis%20pot%20plants%20ENG.pdf. Accessed: 19 Spet 2015.
Agricultural Research Institute. 2010a. Ten-Day Bulletin of Essential Insect Pests of Vegetables and Fruits (in Chinese). Council of Agriculture, Executive Yuan, Taiwan. Available at: http://www.tari.gov.tw/taric/modules/icontent/index.php?op= explore¤tDir=51. Accessed: 7 Oct., 2010.
Agricultural Research Institute. 2010b. Ten-Day Bulletin of Essential Insect Pests of Vegetables and Fruits (in Chinese). Council of Agriculture, Executive Yuan, Taiwan. Available at: http://www.tari.gov.tw/taric/modules/wfdownloads/viewcat.php?cid=26. Accessed: 15 June, 2011.
Atzori, L., A. Iera, and G. Morabito. 2010. The Internet of things: A survey. Comput. Netw. 54(15): 2787−2805.
Bahrepour, M., N. Meratnia, and P. J. M. Havinga. 2011. Online unsupervised event detection in wireless sensor networks. In “Proc. Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing”, pp. 306–311.
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.
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.
Bein, D. 2009. Self-organizing and self-healing schemes in wireless sensor networks. In “Guide to Wireless Sensor Networks”, ed. S. C. Misra, I. Woungang, and S. Misra, 293–304. New York: Springer Science+Business Media LLC.
Bezdek, J. C. 1981. Pattern recognition with fuzzy objective function algorithm. 1st ed. New York: Plenum Press.
Birrell, S. J., K. A. Sudduth, and S. C. Borgelt. 1996. Comparison of sensors and techniques for crop yield mapping. Comput. Electron. Agric. 14(2–3): 215–233.
Bokareva, T., N. Bulusu, and S. Jha. 2005. SASHA: toward a self-healing hybrid sensor network architecture. In “Proc. the 2nd IEEE workshop on Embedded Networked Sensors”, pp. 71–78.
Both, A. J., L. D. Albright, R. W. Langhans, R. A. Reiser, and B. G. Vinzant. 1997. Hydroponic lettuce production influenced by integrated supplemental light levels in a controlled environment agriculture facility: experimental results. Acta Horticulturae. 418: 45−52.
Bouvier, J. C., T. Boivin, D. Beslay, and B. Sauphanor. 2002. Age-dependent response to insecticides and enzymatic variation in susceptible and resistant codling moth larvae. Arch. Insect Biochem. Physiol. 51(2): 55–66.
Canny, J. 1986. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6): 679–698.
Cárdenas-Benítez, N., R. Aquino-Santos, P. Magaña-Espinoza, J. Aguilar-Velazco, A. Edwards-Block, and A. Medina Cass. 2016. Traffic congestion detection system through connected vehicles and big data. Sensors. 16(5): 599.
Ciavarella, S., J. Y. Joo, and S. Silvestri. 2016. Managing contingencies in smart grids via the internet of things. IEEE Trans. Smart Grid. 7(4): 2134−2141.
Chan, Y. A., M. S. Liao, C. H. Wang, Y. C. Lee, and J. A. Jiang. 2015. Image repainted method of overlapped leaves for orchid leaves area estimation. In “Proc. the 2015 9th International Conference on Sensing Technology (ICST 2015)”, pp. 205–210.
Chang, Y. K., and R. E. Veilleux. 2009. Analysis of genetic variability among Phalaenopsis species and hybrids using amplified fragment length polymorphism. J. Amer. Soc. Hort. Sci. 134(1): 58–66.
Chang, Y. C., C. Y. Lee, X. Y. Zheng, and C. L. Chuang. 2012. A data retransmitting mechanism for ecological monitoring system. In “Proc. the 2012 Fifth IEEE International Conference on Service-Oriented Computing and Applications (SOCA)”, pp. 1−6.
Chang, Y. W., T. S. Lin, J. C. Wang, J. J. Chou, K. C. Liao, and J. A. Jiang. 2011. The effect of temperature distribution on the vertical cultivation in plant factories with a WSN-based environmental monitoring system. In “Proc. the 2011 International Conference on Agricultural and Natural Resources Engineering Advances in Biomedical Engineering (ANRE-2011)”, pp. 234−240.
Cha-um, S., B. Ulziibat, and C. Kirdmanee. 2010. Effects of temperature and relative humidity during in vitro acclimatization, on physiological changes and growth characters of Phalaenopsis adapted to in vivo. Aust. J. Crop Sci. 4(9): 750–756.
Chen, C. C., and M. Y. Chien. 2012. The leaf growth model and influencing factors in Phalaenopsis orchid. Afr. J. Agric. Res. 7(28): 4045–4055.
Chen, C. P., C. L. Chuang, C. L. Tseng, E. C. Yang, M. Y. Liu, and J. A. Jiang. 2009. A novel energy efficient adaptive routing protocol for wireless sensor networks. J. Chin. Soc. Mech. Eng. 30(1): 59–65.
Chen, C. P., C. L. Chuang, and J. A. Jiang. 2013. Ecological monitoring using wireless sensor networks – Overview, challenges, and opportunities, In “Advancement in Sensing Technology – New Developments and Practical Applications”, ed. S. C. Mukhopadhyay, K. P. Jayasundera, and A. Fuchs, 1–21. Berlin: Springer-Verlag.
Chen, P., H. Ye, and J. Liu. 2006. Population dynamics of Bactrocera dorsalis (Diptera:Tephritidae) and analysis of the factors influencing the population in Ruili, Yunnan Province, China. Acta Ecol. Sin. 26(9): 2801–2809.
Cheng, E. T., Y. B. Hwang, C. H. Kao, and M. Y. Chaing. 2002. An area-wide control program for the oriental fruit fly in Taiwan. In “Proc. Symp. Insect Ecology and Fruit Fly Management”, pp. 57–71.
Chen, D., and J. N. Laneman. 2006. Modulation and demodulation for cooperative diversity in wireless system. IEEE Trans. Wirel. Commun. 5(7): 1785–1794.
Chiang, S. Y., Y. C. Kan, Y. S. Chen, Y. C. Tu, and H. C. Lin. 2016. Fuzzy computing model of activity recognition on WSN movement data for ubiquitous healthcare measurement. Sensors. 16(12): 2053.
Chinnasarn, K., Y. Rangsanseri, and P. Thitimajshima. 1998. Removing salt-and-pepper noise in text/graphics images. In “Proc. the 1998 IEEE Asia-Pacific Conference on Circuits and Systems”, pp. 459−462.
Chou, C. C., W. S. Chen, K. L. Huang, H. C. Yu, and L. J. Liao. 2000. Changes in cytokinin levels of Phalaenopsis leaves at high temperature. Plant Physiol. Bioch. 38(4): 309−314.
Chu, Y. J., C. P. Tseng, K. C. Liao, Y. C. Wu, F. M. Lu, J. A. Jiang, Y. C. Wang, C. L. Tseng, E. C. Yang, E. C. Ho, and K. Y. Ho. 2009. The first order load-balanced algorithm with static fixing scheme for centralized WSN system in outdoor environmental monitoring. In “Proc. 2009 IEEE Sensors Conference”, pp.1810–1813.
Clarke, A. R. K. F. Armstrong, A. E. Carmichael, J. R. Milne, S. R. Raghu, G. K. Roderick, and D. K. Yeates. 2005. Invasive phytophagous pests arising through a recent tropical evolutionary radiation: the Bactrocera dorsalis complex of tropical fruit flies. Annu. Rev. Entomol. 50: 293–319.
Collotta, M., and G. Pau. 2015. Bluetooth for Internet of things: A fuzzy approach to improve power management in smart homes. Comput. Electr. Eng. 44: 137−152.
Cortes, C., and V. Vapnik. 1995. Support-vector networks. Mach. Learn. 20(3): 273–297.
Council of Agriculture. 2017. Agriculture trade (in Chinese). Executive Yuan, Taiwan. Available at: http://agrstat.coa.gov.tw/sdweb/public/trade/TradeCoa.aspx. Accessed: 13 Feb., 2017.
Curran, P. J. 1988. The semivariogram in remote sensing: An introduction. Remote Sens. Environ. 24(3): 493−507.
Dayton, C. M., 2003. Model comparisons using information measures. J. Mod. Appl. Stat. Meth. 2(2): 281−292.
De Caires, S. A., M. N. Wuddivira, and I. Bekele. 2015. Spatial analysis for management zone delineation in a humid tropic cocoa plantation. Precis. Agric. 16(2): 129−147.
Despommier, D., 2009. The rise of vertical farms. Sci. Am. 301(5): 80−87.
Diacono, M., A. Castrignanò, C. Vitti, A. M. Stellacci, L. Marino, C. Cocozza, D. De Benedetto, A. Troccoli, P. Rubino, and D. Ventrella. 2014. An approach for assessing the effects of site-specific fertilization on crop growth and yield of durum wheat in organic agriculture. Precis. Agric. 15(5): 479–498.
Dissanayake, M., P. Newman, S. Clark, H. Durrant-Whyte, and M. Csorba. 2001. A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans. Robot. Autom. 17(3): 229–241.
Dhankhar, P., and N. Sahu. 2013. A review and research of edge detection techniques for image segmentation. Int. J. Comp. Sci. Mob. Comput. 2(7): 86–92.
Drew, R. A. I., and S. Raghu. 2002. The fruit fly fauna (Diptera: Tephritidae: Dacinae) of the rainforest habitat of the Western Ghats, India. Raffles Bull. Zool. 50(2): 327–352.
Kosmatos E. A., N. D. Tselikas, and A. C. Boucouvalas. 2011. Integrating RFIDs and smart objects into a unifiedInternet of Things architecture. Adv. IoT. 1(1): 5−12.
Fischer, G., M. Shah, and G. van Velthuizen. 2002. Climate change and agriculture vulnerability. International Institute for Applied Systems Analysis, Laxenburg, Astria.
Fuad, K. A. A., and S. M. Rizvi. 2015. Hardware software co-simulation of Canny edge detection algorithm. Int. J. Comput. Appl. 122(19): 7–12.
Ge, Y., J. A. Thomasson, R. Sui, and J. Wooten. 2011. Regression-kriging for characterizing soils with remote-sensing data. Front. Earth Sci. 5(3): 239–244.
Goodale, C. L., J. D. Aber, and S. V. Ollinger. 1998. Mapping monthly precipitation, temperature, and solar radiation for Ireland with polynomial regression and a digital elevation model. Clim. Res. 10(1): 35–49.
Griesbach, R. J., 2000. Potted Phalaenopsis orchid production: History, present status, and challenges for the future. HortTechnology. 10(3): 429.
Gubbi, J., R. Buyya, S. Marusic, and M. Palaniswami. 2013. Internet of things (IoT): A vision, architectural elements, and future directions. Future Gener. Comp. Syst. 29(7): 1645−1660.
Guo, G., S. Z. Li, and K. L. Chan. 2001. Support vector machine for face recognition. Image Vis. Comput. 19(9-10): 631–638.
Guo, W. J., and N. Lee. 2006. Effect of leaf and plant age, and day/night temperature on net CO2 uptake in Phalaenopsis amabilis var. formosa. J. Amer. Soc. Hort. Sci. 131(3): 320–326.
Guo, X. M., X. T. Yang, M. X. Chen, M. Li, and Y. A. Wang. 2015. A model with leaf area index and apple size parameters for 2.4 GHz radio propagation in apple orchards. Precis. Agric. 16(2): 180–200.
Haykin, S. 1998. Neural Networks: A Comprehensive Foundation. 2nd ed. London, United Kingdom: Prentice Hall.
Hew, C. S., and J. W. H. Yong. 2004. The physiology of tropical orchids in relation to the industry. 2nd ed. Singapore: World Scientific Publishing Co. Pte. Ltd.
Holford, T. R., K. Ebisu, L. A. McKay, J. F. Gent, E. W. Triche, M. B. Bracken, and B. P. Leaderer. 2010. Integrated exposure modeling: A model using GIS and GLM. Stat. Med. 29(1): 116−129.
Huang, J., S. M. Farritor, A. Qadi, and S. Goddard. 2006. Localization and follow-the-leader control of a heterogeneous group of mobile robots. IEEE-ASME Trans. Mechatron. 11(2): 205–215.
Hung, Y. T., W. H. Tsai, and K. C. Kuo. 2008. Oriental fruit fly management in Taiwan: current and future. In “Proc. the International Symposium on the Recent Progress of Tephritid Fruit Flies Management”, pp. 5–9.
Ikeda, A., Y. Tanimura, K. Ezaki, Y. Kawai, S. Nakayama, K. Iwao, and H. Kageyama. 1992. Environmental control and operation monitoring in a plant factory using artificial light. Acta Hortic. 304: 151−158.
Jamil, N., T. M. T. Sembok, and Z. A. Bakar. 2008. Noise removal and enhancement of binary images using morphological operations. In “Proc. the International Symposium on Information Technology”, pp. 1−6.
Jao, R. C., C. C. Lai, W. Feng, and S. F. Chang. 2005. Effects of red light on the growth of Zantedeschia plantlets in vitro and tuber formation using light-emitting diodes. Hortscience. 40(2): 436−438.
Jensen, M. H. 1997. Food production in greenhouses. In “Plant production in closed ecosystems”, ed. E. Goto, K. Kurata, M. Hayashi, and S. Sase, 1−14. Berlin: Springer.
Jiang, J. A., C. H. Wang, C. H. Chen, M. S. Liao, Y. L. Su, W. S. Chen, C. P. Huang, E. C. Yang, and C. L. Chuang. 2016a. A WSN-based automatic monitoring system for the foraging behavior of honey bees and environmental factors of beehives. Comput. Electron. Agric. 123: 304−318.
Jiang, J. A., C. H. Wang, M. S. Liao, X. Y. Zheng, J. H. Liu, C. L. Chuang, C. L. Hung, and C. P. Chen. 2016b. A wireless sensor network-based monitoring system with dynamic convergecast tree algorithm for precision cultivation management in orchid greenhouses. Precis. Agric. 17(6): 766–785.
Jiang, J. A., C. L. Tseng, F. M. Lu, E. C. Yang, Z. S. Wu, C. P. Chen, S. H. Lin, K. C. Lin, and C. S. Liao. 2008. A GSM-based remote wireless automatic monitoring system for field information: A case study for ecological monitoring of the oriental fruit fly, Bactrocera dorsalis (Hendel). Comput. Electron. Agric. 62(2): 243–259.
Jiang, J. A., C. P. Chen, C. L. Chuang, T. S. Lin, C. L. Tseng, E. C. Yang, and Y. C. Wang. 2009. CoCMA: Energy-efficient coverage control in cluster-based wireless sensor networks using a memetic algorithm. Sensors. 9(6): 4918–4940.
Jiang, J. A., T. S. Lin, C. L. Chuang, C. P. Chen, C. H. Sun, Y. J. Juang, J. C. Lin, and W. W. Liang. 2011. A QoS-guaranteed coverage precedence routing algorithm for wireless sensor networks. Sensors. 11(4): 3418–3438.
Jin T, L. Zeng, Y. Y. Lu, and G. W. Liang. 2011. Insecticide resistance of the oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera:Tephritidae), in mainland China. Pest Manag. Sci. 67(3): 370–376.
Juo, K. T., T. S. Lin, Y. W. Chang, J. C. Wang, J. J. Chou, K. C. Liao, J. C. Shieh, and J. A. Jiang. 2012. The effect of temperature variation in the plant factory using a vertical cultivation system. In “Proc. the 6th International Symposium on Machinery and Mechatronics for Agriculture and Biosystems Engineering”, pp. 963−968.
Kalmár, A., G. Öllös, and R. Vida. 2011. Analysis of an event forecasting method for wireless sensor networks. Acta Univ. Sapientiae Elec. Mech. Eng. 3: 26–28.
Kataoka, K., K. Sumitomo, T. Fudano, and K. Kawase. 2004. Changes in sugar content of Phalaenopsis leaves before floral transition. Sci. Hortic. 102(1): 121−132.
Kelly, S. D. T., N. K. Suryadevara, and S. C. Mukhopadhyay. 2013. Towards the implementation of IoT for environmental condition monitoring in homes. IEEE Sens. J. 13(10): 3846−3853.
Khan, M., S. Din, S. Jabbar, M. Gohar, H. Ghayvat, and S. C. Mukhopadhyay. 2016. Context-aware low power intelligent SmartHome based on the Internet of things. Comput. Electr. Eng. 52: 208−222.
Kim, Y. J., H. J. Lee, and K. S. Kim. 2013. Carbohydrate changes in Cymbidium ‘Red Fire’ in response to night interruption. Sci. Hortic. 162: 82−89.
Knotters, M., D. J. Brus, and J. H. Oude Voshaar. 1995. A comparison of kriging, co-kriging and kriging combined with regression for spatial interpolation of horizon depth with censored observations. Geoderma. 67(3−4): 227−246.
Kohonen, T. 1997. Self-Organizing Maps, 3rd ed. New York, USA: Springer-Verlag.
Kozai, T., 2005. Introduction. In “Photoautotrophic (sugar-free medium) micropropagation as a new micropropagation and transplant production system”, ed. T. Kozai, F. Afreen, and S. M. A. Zobayed, 1−5. Berlin: Springer.
Kozai, T., G. Niu, and M. Takagaki. 2015. PFAL business and RandD in the world: Current status and perspectives. In “Plant factory: an indoor vertical farming system for efficient quality food production”, ed. T. Kozai, G. Niu, and M. Takagaki, 35. USA: Academic Press.
Lee, N., and M. C. Wang. 1997. Changes in mineral composition and carbohydrate contents from juvenile to mature phase in white-flowered Phalaenopsis plants. J. Chinese Soc. Hort. Sci. 43: 295−305. (in Chinese with English abstract)
Lei, L, H. J. Wang, and C. L. Dai. 2008. Fault diagnosis for wireless sensor network''s node based on hamming neural network and rough set. In “Proc. the IEEE Conference on Robotics, Automation and Mechatronics”, pp. 566–570.
Łęski, J. 2003. Towards a robust fuzzy clustering. Fuzzy Sets Syst. 137(2): 215–233.
Li, X., J. Jiang, and Q. Fan. 2012. An improved real-time hardware architecture for Canny edge detection based on FPGA. In “Proc. the 2012 Third International Conference on Intelligent Control and Information Processing”, pp. 445−449.
Liao, M. S., C. L. Chuang, T. S. Lin, C. P. Chen, X. Y. Zheng, P. T. Chen, K. C. Liao, and Jiang, J. A. 2012. Development of an autonomous early warning system for Bactrocera dorsalis (Hendel) outbreaks in remote fruit orchards. Comput. Electron. Agric. 88: 1−12.
Liao, M. S., S. F. Chen, C. Y. Chou, H. Y. Chen, S. H. Yeh, Y. C. Chang, and J. A. Jiang. 2017. On precisely relating the growth of Phalaenopsis leaves to greenhouse environmental factors by using an IoT-based monitoring system. Comput. Electron. Agric. 136: 125−139.
Lin, G. M., and N. Lee. 1988. Leaves area estimation and the effect of temperature on the growth of Phalaenopsis leaves. J. Chinese Soc. Hort. Sci. 34: 73−80. (in Chinese with English abstract)
Lin, M. J., and B. D. Hsu. 2004. Photosynthetic plasticity of Phalaenopsis in response to different light environments. J. Plant Physiol. 161(11): 1259−1268.
Lin, M. Y., S. K. Chen, Y. C. Liu, and J. T. Yang. 2005. Pictorial key to 6 common species of the genus Bactrocera from Taiwan. Plant Prot. Bull. 47: 39–46.
Lin Y. Y., T. Jin, L. Zeng, and Y. Y. Lu. 2014. Toxicities of three insecticides to Bactrocera dorsalis (Hendel) adults with different adult density, age and gender. J. Environ. Entomol. 36: 737–743.
Liu, T. F., 2012. Factors affecting blossom quality of Phalaenopsis after flower bud differentiation. Fujian J. Agric. Sci. 27(9): 999–1003.
Lopez, R. G., and E. S. Runkle. 2005. Environmental physiology of growth and flowering of orchids. Hortscience. 40(7): 1969–1973.
MacQueen, J. B. 1967. Some methods for classification and analysis of multivariate observations. In “Proc. 5-th Berkeley Symposium on Mathematical Statistics and Probability”, Berkeley, University of California Press. 1: 281–297.
Mahajan, M., P. Nimbhorkar, and K. Varadarajan. 2009. The planar k-Means problem is NP-Hard. In “Proc. the 3rd International Workshop on Algorithms and Computation”, pp. 274–285.
Malacrida, A. R., L. M. Gomulski, M. Bonizzoni, S. Bertin, G. Gasperi, and C. R. Guglielmino. 2007. Globalization and fruitfly invasion and expansion: the medfly paradigm. Genetica. 131: 1–9.
Mangianmeli, P., S. K. Chen, and D. West. 1996. A comparison of SOM neural network and hierarchical clustering methods. Eur. J. Oper. Res. 93(2): 402–417.
Mao, Z. H., and S. G. Massaquoi. 2007. Dynamics of winner-take-all competition in recurrent neural networks with lateral inhibition. IEEE Trans. Neural Netw. 18(1): 58–69.
Mashal, I., O. Alsaryrah, T. Y. Chung, C. Z. Yang, W. H. Kuo, and D. P. Agrawal. 2015. Choices for interaction with things on Internet and underlying issues. Ad Hoc Netw. 28: 68–90.
Metcalf, R. L., and E. R. Metcalf. 1992. Fruit flies of the family Tephritidae. In “Plant Kairomones in Insect Ecology and Control”, ed. R. L. Metcalf, and E. R. Metcalf, 109–152. New York: Chapman & Hall.
Miorandi, D., S. Sicari, F. De Pellegrini, and I. Chlamtac. 2012. Internet of things: Vision, applications and research challenges. Ad Hoc Netw. 10(7): 1497–1516.
Mohamed, M. S., and T. Kavitha. 2011. Outlier detection using support vector machine in wireless sensor network real time data. Int. J. Soft Comput. Eng. 1(2): 68–72.
Paladina, L., M. Paone, G. Jellamo, and A. Puliafito. 2007. Self organizing maps for distributed localization in wireless sensor networks. In “Proc.the 12th IEEE Symposium on Computers and Communications”, pp. 1113–1118.
Pan Z. P., Y. Y. Lu, L. Zeng, and X. N. Zeng. 2008. Development of resistance to trichlorophon, alphamethrin, and abamectin in laboratory populations of the oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera:Tephritidae). Acta Entomol. Sin. 51(6): 609–617.
Paradiso, R., and S. D. Pascale. 2014. Effects of plant size, temperature, and light intensity on flowering of Phalaenopsis hybrids in Mediterranean greenhouses. The Scientific World Journal, Volume 2014, Article ID 420807, November 2014, 9 pages.
Parello, J., B. Claise, B. Schoening, and J. Quittek. 2014. Energy management framework. Available at: https://www.rfc-editor.org/rfc/rfc7326.txt. Accessed 26 May 2017.
Patra, J. C., P. K. Meher, and G. Chakraborty. 2011. Development of Laguerre neural-network-based intelligent sensors for wireless sensor networks. IEEE Trans. Instrum. Meas. 60(3): 725–734.
Peng, Y. H., C. S. Hsu, P. C. Huang, and Y. D. Wu. 2014. An effective wavelength utilization for spectroscopic analysis on orchid chlorophyll measurement. In “Proc. the 2014 IEEE International Conference on Automation Science and Engineering (CASE)”, pp. 716−721.
Postolache, O. A., P. M. B. S. Girao, J. M. D. Pereira, and H. M. G. Ramos. 2005. Self-organizing maps application in a remote water quality monitoring system. IEEE Trans. Instrum. Meas. 54(1): 322–329.
Raj, A. B., M. V. Ramesh, R. V. Kulkarni, and T. Hemalatha. 2012. Security enhancement in wireless sensor networks using machine learning. In “Proc. the IEEE 14th International Conference on High Performance Computing and Communications”, pp. 1264–1269.
Ray, P. P. 2016. Internet of things cloud enabled MISSENARD index measurement for indoor occupants. Measurement. 92: 157–165.
Robinson, T. P., and G. Metternicht. 2006. Testing the performance of spatial interpolation techniques for mapping soil properties. Comput. Electron. Agric. 50(2): 97−108.
Roman, R., C. Alcaraz, J. Lopez, and N. Sklavos. 2011. Key management systems for sensor networks in the context of the Internet of things. Comput. Electr. Eng. 37(2): 147–159.
Rosenzweig, C., A. Iglesias, X. B. Yang, P. R. Epstein, and E. Chivian. 2001. Climate change and extreme weather events; implications for food production, plant diseases, and pests. Global Changes Hum. Health. 2(2): 90–104.
Runkle, E. S., 2007. Innovative production systems for ornamental potted plants: a case study for Phalaenopsis orchids. In “Proc. the International Conference on Quality Management in Supply Chains of Ornamentals (ISHS Acta Horticulturae 755)”, pp. 55–60.
Said, O. and M. Masud. 2013. Towards Internet of things: Survey and future vision. Int. J. Comput. Netw. 5(1): 1–17.
Sailo, N., D. Rai, and L. C. De. 2014. Physiology of temperate and tropical orchids – An overview. Int. J. Sci. Res. 3(12): 3−8.
Sakanishi, Y., H. Imanishi, and G. Ishida. 1980. Effect of temperature on growth and flowering of Phalaenopsis amabilis. Bul. Univ. Osaka, Ser. B. Agr. Biol.-Osaka (Prefecture) Daigaku. 32: 1–9.
Salleh, A., M. K. Ismail, N. R. Mohamad, M. Z. A Abd Aziz, M. A. Othman, and M. H. Misran. 2013. Development of greenhouse monitoring using wireless sensor network through ZigBee technology. Int. J. Eng. Sci. Invention. 2(7): 6–12.
Senthilkumaran, N., and R. Rajesh. 2009. Edge detection techniques for image segmentation – A survey of soft computing approaches. Int. J. Recent Trends Eng. Technol. 1(2): 250−254.
Sensirion AG, Switzerland. 2017. Datasheet SHT1x Available at: https://cdn-shop.adafruit.com/datasheets/Sensirion_Humidity_SHT1x_Datasheet_V5.pdf. Accessed: 12 Jan., 2017.
Sharma, A. B., L. Golubchik, and R. Govindan. 2010. Sensor faults: Detection methods and prevalence in real-world datasets. ACM Trans. Sens. Netw. 6(3): 23.
Sheu, J. P., .C. J. Chang, C. Y. Sun, and W. K. Hu. 2008. WSNTB: A testbed for heterogeneous wireless sensor networks. In “Proc. the 2008 First IEEE International Conference on Ubi-Media Computing”, pp. 338−343.
Siripanadorn, S., W. Hattagam, and N. Teaumroong. 2010. Anomaly detection in wireless sensor networks using self-organizing map and wavelets. Int. J. Commun. 4(3): 74–83.
Smith, A. M., and S. C. Zeeman. 2006. Quantification of starch in plant tissues. Nat. Protoc. 1(3): 1342–1345.
Smith, P. H., 1989. Behavioral partitioning of the day and circadian rhythmicity. In: Robinson, A. S., Hooper, G. (Eds.), Fruit Flies: Their Biology, Natural Enemies, and Control (World Crop Pests Series), vol. 3B. Elsevier, Amsterdam, NL, pp. 325–341. Vargas, R. I., Miyashita, O., Nishida, T., 1984. Life history and demographic parameters of three laboratory-reared tephritids (Diptera: Tephritidae). Ann. Entomol. Soc. Am. 77, 651–656.
Spanò, E., L. Niccolini, S. Di Pascoli, and G. Iannacconeluca. 2015. Last-meter smart grid embedded in an Internet-of-Things platform. IEEE Trans. Smart Grid. 6(1): 468−476.
Stancato, G. C., P. Mazzafera, and M. S. Buckeridge. 2002. Effects of light stress on the growth of the epiphytic orchid Cattleya forbesii Lindl. X Laelia tenebrosa Rolfe. Braz. J. Bot. 25(2): 229–235.
Steiner, F. 1952. Methyl eugenol as an attractant for oriental fruit fly. J. Econ. Entomol. 75: 173–178.
Subramaniam, S., T. Palpanas, D. Papadopoulos, V. Kalogerakiand, and D. Gunopulos. 2006. Online outlier detection in sensor data using nonparametric models. In “Proc. the 32nd International Conference on Very large data bases”, pp. 187–198.
Sukkhawatchani, P. and W. Usaha. 2008. Performance evaluation of anomaly detection in cellular core networks using self-organizing map. In “Proc. of ECTI-CON 2008”. 1: 361–364.
Sundmaeker, H., P. Guillemin, P. Friess, S. Woelfflé. 2010. Internet of things vision. In “Vision and challenges for realising the internet of things” ed. H. Sundmaeker, P. Guillemin, P. Friess, S. Woelfflé, 43–48. Luxembourg: European Commission Information Society and Media.
Sutherst, R. W., F. Constable, K. J. Finlay, R. Harrington, J. Luck, and M. P. Zalucki. 2011. Adapting to crop pest and pathogen risks under a changing climate. Wiley Interdiscip. Rev.- Clim. Change. 2(2): 220–237.
Taylor, R. 1990. Interpretation of the correlation coefficient: A basic review. J. Diagn. Med. Sonogr. 6(1): 35–39.
Thompson, H. C., R. W. Langhans, A. J. Both, and L. D. Albright. 1998. Shoot and root temperature effects on growth of lettuce in a floating hydroponic system. J. Am. Soc. Hortic. Sci. 123(3): 361–364.
UN Comtrade, 2017. Trade data extraction interface. Available at: http://comtrade.un.org/data/. Accessed: 13 Feb., 2017.
Uyan, M. 2016. Determination of agricultural soil index using geostatistical analysis and GIS on land consolidation projects: A case study in Konya/Turkey. Comput. Electron. Agric. 123: 402−409.
Vargas, R. I., D. Miyashita, and T. Nishida. 1984 Life history and demographic parameters of three laboratory-reared tephritids (Diptera: Tephritidae). Ann. Entomol. Soc. Am. 77(6):651–656.
Venkatesan, J., R. Rajathilagam, P. V. R. Ranushka, and V. Sindhuja. 2015. Contactless palm vein recognition using a Canny edge detection algorithm. Int. J. Innov. Trends Emerg. Technol. 1(1): 58–61.
Verdouw, C. N., J. Wolfert, A. J. M. Beulens, and A. Rialland. 2016. Virtualization of food supply chains with the internet of things. J. Food Eng. 176: 128–136.
Voltz, M., and R. Webster. 1990. A comparison of kriging, cubic splines and classification for predicting soil properties from sample information. Eur. J. Soil Sci. 41(3): 473−490.
Wallance, M. K., and D. M. Hawkins. 1994. Applications of geostatistics in plant nematology. J. Nematol. 26(4S): 626–634.
Wang, T., Z. Liang, and C. H. Zhao. 2009. A detection method for routing attacks of wireless sensor network based on fuzzy c-means clustering. In “Proc. 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery”, pp. 445–449.
Wang, Y. T., and T. Y. Hsu. 1994. Flowering and growth of Phalaenopsis orchids following growth retardant applications. Hortscience. 29(4): 285–288.
Weems, H. V., J. B. Heppner, J. L. Nation, and T. R. Fasulo. 2008. Oriental Fruit Fly, Bactrocera dorsalis (Hendel) (Insecta: Diptera: Tephritidae). Gainesville: Institute of Food and Agricultural Sciences, University of Florida, FL, Tech. Rep. EENY-083.
Wu, Y. H., M. C. Hung, and J. Patton. 2013. Assessment and visualization of spatial interpolation of soil pH values in farmland. Precis. Agric. 14(6): 565–585.
Yang, J. S., Y. Q. Wang, and P. V. August. 2004. Estimation of land surface temperature using spatial interpolation and satellite-derived surface emissivity. J. Environ. Inform. 4(1): 37−44.
Yate Loon Electronics Co., Ltd., Taiwan. 2017. DC fan (80 mm − 92 mm). Available at: http://www.yateloon.com/s/2/product-38836/DC-FAN-SERIES-92x92x25.html?TTo=en. Accessed: 12 Jan., 2017.
Yick, J., B. Mukherjee, and D. Ghosal. 2008. Wireless sensor network survey. Comput. Netw. 52(12): 2292–2330.
Yigit, M., V. C. Gungor, E. Fadel, L. Nassef, N. Akkari, and I. F. Akyildiz. 2016. Channel-aware routing and priority-aware multi-channel scheduling for WSN-based smart grid applications. J. Netw. Comput. Appl. 71: 50−58.
Zhang, S. W., C. Y. Shen, X. Y. Chen, H. C. Ye, Y. F. Huang, and S. Lai. 2013. Spatial interpolation of soil texture using compositional kriging and regression kriging with consideration of the characteristics of compositional data and environment variables. J. Integr. Agric. 12(9): 1673−1683.
Zhang, Y. Y., H. C. Chao, M. Chen, L. Shu, C. H. Park, and M. S. Park. 2010. Outlier detection and countermeasure for hierarchical wireless sensor networks. IET Inf. Secur. 4(4): 361–373.
Zhu, H. Y., and L. Lei. 2010. Fault diagnosis of node in wireless sensor network based on the interval-numbers rough neural network. In “Proc. the 2nd IEEE International Conference on Information Management and Engineering”, pp. 535–538.