Development of Web-Based Plant Sensor Tool Data Processing Application
Article Sidebar
Main Article Content
Abstract
The rapid development of technology in the 4.0 era brings changes in the use of technology. The industrial 4.0 era makes IoT (Internet of Things) media a technology that can support all sectors of activity including the agriculture or farming sector. IoT media takes place in the form of plant sensor device that can be used to measure changes in environmental parameters. These changes include light intensity, temperature, humidity, soil fertility, are the supporting factors for plant growth. This study aims to design a website-based application that can read plant sensor data. Sensor device data is collected and downloaded in CSV (Comma Separated Values) format. Data processing requires the Naïve Bayes algorithm as a method for grouping parameter data. The results of the study are in the form of a website design that can help analyze the ideal environmental parameters for farming activities.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Y. Feng, E. Benassi, L. Zhang, X. Li, D. Wang, F. Zhou, and W. Liu, “Concealed Wireless Warning Sensor Based on Triboelectrification and Human-Plant Interactive Induction”, in Research, 2021, pp. 1–11. https://doi.org/10.34133/2021/9870936
S. N. Daskalakis, G. Goussetis, S. D. Assimonis, M. M. Tentzeris, and A. Georgiadis, “A uW Backscatter-Morse-Leaf Sensor for Low-Power Agricultural Wireless Sensor Networks”, in IEEE Sensors Journal, 2018, 18(19), 7889–7898. https://doi.org/10.1109/JSEN.2018.2861431
D. K. Sreekantha and A. M. Kavya, “Agricultural crop monitoring using IOT-a study”, in 2017 11th International conference on intelligent systems and control (ISCO), IEEE, 2017, January, pp. 134-139.
S. Srisruthi, N. Swarna, G. S. Ros and E. Elizabeth, “Sustainable agriculture using eco-friendly and energy efficient sensor technology”, in 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE, 2016, May, pp. 1442-1446
T. M. Connolly and C. E. Begg, “Database Systems: A Practical Approach to Design, Implementation and Management 6th Edition”, Boston: Pearson Education. 2015.
V. H. Wangi, and J. T. Beng, “Start to End: Recommended Travel Routes Based on Tourist Preference”, in IOP Conference Series: Materials Science and Engineering, IOP Publishing, 2020, July, Vol. 852, No. 1, p. 012163.
M. M. Saritas and A. Yasar, “Performance analysis of ANN and Naive Bayes classification algorithm for data classification”, in International Journal of Intelligent Systems and Applications in Engineering, 2019, 7(2), pp.88-91.
F. C. Chen and M. R. Jahanshahi, “NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion”, in IEEE Transactions on Industrial Electronics, 2018, 65(5), 4392–4400. https://doi.org/10.1109/TIE.2017.2764844
D. Berrar, “Bayes’ theorem and naive Bayes classifier”, Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics; Elsevier Science Publisher: Amsterdam, The Netherlands, 2018, 403-412.
Y. Feng, E. Benassi, L. Zhang, X. Li, D. Wang, F. Zhou, and W. Liu, “Concealed Wireless Warning Sensor Based on Triboelectrification and Human-Plant Interactive Induction”, in Research, 2021, 1–11. https://doi.org/10.34133/2021/9870936
D. Ma, N. Carpenter, H. Maki, T. U. Rehman, M. R. Tuinstra, and J. Jin, “Greenhouse environment modeling and simulation for microclimate control”, in Computers and Electronics in Agriculture, 2019, 162(November 2018), 134–142. https://doi.org/10.1016/j.compag.2019.04.013
W. William, H. Suharto, and H. Tanudjaja, “Sistem pemantauan dan pengendalian parameter lingkungan pertumbuhan pada tanaman hidroponik”, in TESLA: Jurnal Teknik Elektro, 2017, 18(2), 188-207.
J. T. Beng, A. F. Amanto, A. Aurelia, D. Chandra, K. Y. D. Mandey, L. A. Ramadhani, R., Stephanie, and S. Tiatri, “Designing an Android-based Green School Program Application with the Fuzzy Logic Weather Forecast Method: A Case Study of School Students Participating in a School Greening Program, [Unpublished], in AIP Conference Proceedings, 2022.
T. A. Pratiwi, M. Irsyad, R. Kurniawan, S. Agustian, and B. S. Negara, “Klasifikasi Kebakaran Hutan Dan Lahan Menggunakan Algoritma Naïve Bayes Di Kabupaten Pelalawan”, in CESS (Journal of Computer Engineering, System and Science), 2021, 6(1), pp. 139-148.
S. Agarwal, N. Bhangale, K. Dhanure, S. Gavhane, V. A. Chakkarwar, and M. B. Nagori, “Application of colorimetry to determine soil fertility through Naive Bayes classification algorithm”, in 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, 2018, pp. 1-6.
K. Karadağ, M. E. Tenekeci, R. Taşaltın, and A. Bilgili, “Detection of pepper fusarium disease using machine learning algorithms based on spectral reflectance”, in Sustainable Computing: Informatics and Systems, 2020, 28, 100299.