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Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

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

Saud Altaf¹, Shafiq Ahmad², Mazen Zaindin³

¹University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University Rawalpindi, Rawalpindi 48312, Pakistan.

Sensors (Basel, Switzerland)

|July 24, 2020

Summary

This study introduces an Xbee-based Wireless Sensor Network (WSN) for real-time banana ripeness monitoring. The system accurately predicts fruit condition using gas sensors and Artificial Neural Networks (ANNs), improving post-harvest management.

Keywords:

artificial neural network banana ripening ethylene gas fruit condition monitoring wireless sensor network

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Area of Science:

Agricultural Engineering
Sensor Networks
Artificial Intelligence

Background:

Fruit ripeness monitoring is crucial for minimizing post-harvest losses and ensuring quality.
Banana production in Pakistan faces challenges in post-harvest management and storage.
Wireless Sensor Networks (WSNs) offer remote monitoring capabilities for fruit ripening.

Purpose of the Study:

To demonstrate an Xbee-based WSN for real-time banana ripeness monitoring.
To analyze the network architecture for monitoring banana ripening parameters.
To validate the system's accuracy in predicting fruit condition.

Main Methods:

Developed an Xbee-based WSN architecture with sensor nodes and a sink end-node.
Utilized gas sensors to extract features related to banana ripening stages.
Trained an Artificial Neural Network (ANN) using the Back Propagation (BP) algorithm for data validation.

Main Results:

The proposed WSN architecture effectively identifies banana condition in storage.
The ANN model demonstrated good performance in selecting feature datasets.
Experimental and simulation results show acceptable accuracy in monitoring banana ripeness.

Conclusions:

The Xbee-based WSN system provides an effective solution for real-time banana ripeness monitoring.
The integration of gas sensors and ANNs enhances decision-making for fruit condition management.
This technology can significantly improve post-harvest management and reduce financial losses in the fruit trade.