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Open AccessArticle

Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique

1
University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University Rawalpindi, Rawalpindi 48312, Pakistan
2
Department of Industrial Engineering, King Saud University, Riyadh 11451, Saudi Arabia
3
Department of Statistics and Operations Research, King Saud University, Riyadh 11451, Saudi Arabia
4
Manukau Institute of Technology, Auckland 2023, New Zealand
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(14), 4033; https://doi.org/10.3390/s20144033
Received: 21 June 2020 / Revised: 16 July 2020 / Accepted: 17 July 2020 / Published: 20 July 2020
(This article belongs to the Special Issue Smart Sensors and Devices in Artificial Intelligence)
Real-time monitoring of fruit ripeness in storage and during logistics allows traders to minimize the chances of financial losses and maximize the quality of the fruit during storage through accurate prediction of the present condition of fruits. In Pakistan, banana production faces different difficulties from production, post-harvest management, and trade marketing due to atmosphere and mismanagement in storage containers. In recent research development, Wireless Sensor Networks (WSNs) are progressively under investigation in the field of fruit ripening due to their remote monitoring capability. Focused on fruit ripening monitoring, this paper demonstrates an Xbee-based wireless sensor nodes network. The role of the network architecture of the Xbee sensor node and sink end-node is discussed in detail regarding their ability to monitor the condition of all the required diagnosis parameters and stages of banana ripening. Furthermore, different features are extracted using the gas sensor, which is based on diverse values. These features are utilized for training in the Artificial Neural Network (ANN) through the Back Propagation (BP) algorithm for further data validation. The experimental results demonstrate that the projected WSN architecture can identify the banana condition in the storage area. The proposed Neural Network (NN) architectural design works well with selecting the feature data sets. It seems that the experimental and simulation outcomes and accuracy in banana ripening condition monitoring in the given feature vectors is attained and acceptable, through the classification performance, to make a better decision for effective monitoring of current fruit condition. View Full-Text
Keywords: wireless sensor network; fruit condition monitoring; artificial neural network; ethylene gas; banana ripening wireless sensor network; fruit condition monitoring; artificial neural network; ethylene gas; banana ripening
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MDPI and ACS Style

Altaf, S.; Ahmad, S.; Zaindin, M.; Soomro, M.W. Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique. Sensors 2020, 20, 4033. https://doi.org/10.3390/s20144033

AMA Style

Altaf S, Ahmad S, Zaindin M, Soomro MW. Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique. Sensors. 2020; 20(14):4033. https://doi.org/10.3390/s20144033

Chicago/Turabian Style

Altaf, Saud; Ahmad, Shafiq; Zaindin, Mazen; Soomro, Muhammad W. 2020. "Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique" Sensors 20, no. 14: 4033. https://doi.org/10.3390/s20144033

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