University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University Rawalpindi, Rawalpindi 48312, Pakistan.
Department of Industrial Engineering, King Saud University, Riyadh 11451, Saudi Arabia.
Sensors (Basel). 2020 Jul 20;20(14):4033. doi: 10.3390/s20144033.
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.
实时监测水果在储存和物流过程中的成熟度，可以通过准确预测水果当前状况，最大限度地减少贸易商在储存过程中因财务损失的风险并提高水果的质量。在巴基斯坦，由于储存容器中的大气和管理不善，香蕉生产在生产、收获后管理和贸易营销方面面临着不同的困难。在最近的研究发展中，由于具有远程监测能力，无线传感器网络（WSN）在水果成熟领域的研究逐渐增多。本文专注于水果成熟监测，展示了一个基于 Xbee 的无线传感器节点网络。详细讨论了 Xbee 传感器节点和接收器端节点的网络架构的作用，以及它们监测所有所需诊断参数和香蕉成熟阶段的条件的能力。此外，使用基于不同值的气体传感器提取了不同的特征。这些特征通过反向传播（BP）算法在人工神经网络（ANN）中进行训练，以进一步验证数据。实验结果表明，所提出的 WSN 架构可以识别储存区域内的香蕉状况。所提出的神经网络（NN）架构设计在选择特征数据集方面效果良好。通过分类性能，可以看出，在给定特征向量中，香蕉成熟度监测的实验和模拟结果以及准确性是可接受的，从而可以做出更好的决策，实现对当前水果状况的有效监测。
