Development of Algorithms for an IoT-Based Smart Agriculture Monitoring System

被引:15
|
作者
Siddiquee, Kazy Noor-E-Alam [1 ]
Islam, Md Shabiul [2 ]
Singh, Ninni [3 ]
Gunjan, Vinit Kumar [3 ]
Yong, Wong Hin [2 ]
Huda, Mohammad Nurul [4 ]
Naik, D. S. Bhupal [5 ]
机构
[1] Univ Sci & Technol Chittagong, Dept Comp Sci & Engn, Chittagong, Bangladesh
[2] Multimedia Univ, Fac Engn, Cyberjaya, Malaysia
[3] CMR Inst Technol, Dept Comp Sci & Engn, Hyderabad, India
[4] United Int Univ, Dept Comp Sci & Engn, Dhaka, Bangladesh
[5] Vignans Fdn Sci Technol & Res, Guntur, Andhra Pradesh, India
关键词
HYBRID ENERGY HARVESTER; LOW-FREQUENCY; CONTACT ELECTRIFICATION; VEHICLE DETECTION; HOUGH TRANSFORM; VIBRATION; PAVEMENT; ASPHALT; GENERATOR; PERFORMANCE;
D O I
10.1155/2022/7372053
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Sensor-based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning-based methods. To overcome these limitations, this research is aimed at proposing an IoT-based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method-convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera-mounted mobile robot captured images from the agriculture field for which the proposed IoT-based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT-based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops.
引用
收藏
页数:16
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