A novel and smart automatic light-seeking flowerpot for monitoring flower growth environment

Although the flowerpot is widely used for indoor flowers, it cannot meet the needs of intelligent management during the uncared-for period. The objective of this study was to design a new microcontroller-based smart flowerpot. Its overall system was composed of three parts: information collection layer, automatic control layer and data transmission layer. Firstly, in the process of collecting information, the Laiyite criterion and the normalized weighted average algorithm were adopted to improve the accuracy of information collection. Secondly, for making precise control decisions, the fuzzy control was used to achieve automatic on-demand watering. Finally, the method for comparative analysis of regional light intensity was utilized to achieve light-seeking and light-supplementing. Experimental results showed that the smart flowerpot had strong anti-jamming performance for information collection, the relative soil moisture of flowers could be stably maintained near the optimum humidity (65%), and the light was well-distributed on the flower with the error angle of light-supplementing ranged from –3° to 3°. Keywords: smart flowerpot, automatic watering, seeking light, supplementing light control, microcontroller DOI: 10.25165/j.ijabe.20181102.2786 Citation: Zhang X H, Liu D, Fan C G, Du J L, Meng F F, Fang J L. A novel and smart automatic light-seeking flowerpot for monitoring flower growth environment. Int J Agric & Biol Eng, 2018; 11(2): 184–189.

[1]  Wang Jie,et al.  Smartphone based precise monitoring method for farm operation , 2016 .

[2]  Yang Zidong,et al.  Design and experiment on intelligent fuzzy monitoring system for corn planters , 2013 .

[3]  Chenyu Shi,et al.  Connectivity of wireless sensor networks for plant growth in greenhouse Citation , 2016 .

[4]  Xiaosheng Qin,et al.  A Sequential Fuzzy Model with General-Shaped Parameters for Water Supply–Demand Analysis , 2015, Water Resources Management.

[5]  Sun Jun,et al.  Simulation of Smith fuzzy PID temperature control in enzymatic detection of pesticide residues. , 2015 .

[6]  John B. Solie,et al.  Expression of Variability in Corn as Influenced by Growth Stage Using Optical Sensor Measurements , 2007 .

[7]  Pedro Sánchez,et al.  Wireless Sensor Networks for precision horticulture in Southern Spain , 2009 .

[8]  Jijun Xing Study on Remote Wireless Smart Pot System Based on ZIGBEE+MQTT , 2016 .

[9]  Fouad H. Jaber,et al.  Wireless Data Acquisition and Control Systems for Agricultural Water Management Projects , 2006 .

[10]  Rathinasamy Sakthivel,et al.  Reliable H∞\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H_{\infty }$$\end{document} Stabilization of Fuzzy Systems , 2015, Circuits, Systems, and Signal Processing.

[11]  Guanghong Yang,et al.  Optimal partitioning method for stability analysis of continuous/discrete delay systems , 2015 .

[12]  Nicholas Dercas,et al.  Investigating the effects of soil moisture sensors positioning and accuracy on soil moisture based drip irrigation scheduling systems , 2015 .

[13]  B. Nyambo,et al.  A low cost automatic irrigation controller driven by soil moisture sensors. , 2013 .

[14]  K. Mathiyalagan,et al.  Exponential stability result for discrete-time stochastic fuzzy uncertain neural networks , 2012 .

[16]  Li Xiao-lin Application of Normalized Weighted Average Algorithm in Temperature Acquisition System , 2012 .

[17]  Su Ki Ooi,et al.  Real-time remote monitoring system for crop water requirement information. , 2014 .

[18]  Sun,et al.  An Improved Frequency Domain Technique for Determining Soil Water Content , 2005 .