Seasonal crops require reliable storage conditions to protect the yield once harvested. For long term storage, controlling the moisture content level in grains is challenging because existing moisture measuring techniques are time-consuming and laborious as measurements are carried out manually. The measurements are carried out using a sample and moisture may be unevenly distributed inside the silo/bin. Numerous studies have been conducted to measure the moisture content in grains utilising dielectric properties. To the best of authors’ knowledge, the utilisation of low-cost wireless technology operating in the 2.4 GHz and 915 MHz ISM bands such as Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) have not been widely investigated. This study focuses on the characterisation of 2.4 GHz Radio Frequency (RF) transceivers using ZigBee Standard and 868 to 915 MHz UHF RFID transceiver for moisture content classification and prediction using Artificial Neural Network (ANN) models. The Received Signal Strength Indicator (RSSI) from the wireless transceivers is used for moisture content prediction in rice. Four samples (2 kg of rice each) were conditioned to 10%, 15%, 20%, and 25% moisture contents. The RSSI from both systems were obtained and processed. The processed data is used as input to different ANNs models such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest, and Multi-layer Perceptron (MLP). The results show that the Random Forest method with one input feature (RSSI_WSN) provides the highest accuracy of 87% compared to the other four models. All models show more than 98% accuracy when two input features (RSSI_WSN and RSSI_TAG2) are used. Hence, Random Forest is a reliable model that can be used to predict the moisture content level in rice as it gives a high accuracy even when only one input feature is used.

This manuscript presents a new method to monitor and localize the moisture distribution in a rice silo based on tomography images. Because the rice grain is naturally hygroscopic, the stored grains’ quality depends on their level of moisture content. Higher moisture content leads to fibre degradation, making the grains too frail and possibly milled. If the moisture is too low, the grains become brittle and are susceptible to higher breakage. At present, the single-point measurement method is unreliable because the moisture build-up inside the silo might be distributed unevenly. In addition, this method mostly applies gravimetric analysis, which is destructive. Thus, we proposed a radio tomographic imaging (RTI) system to address these problems. Four simulated phantom profiles at different percentages of moisture content were reconstructed using Newton’s One-Step Error Reconstruction and Tikhonov Regularization algorithms. This simulation study utilized the relationship between the maximum voxel weighting of the reconstructed RTI image and the percentage of moisture content. The outcomes demonstrated promising results, in which the weighting voxel linearly increased with the percentage of moisture content, with a correlation coefficient higher than 0.95 was obtained. Therefore, the results support the possibility of using the RTI approach for monitoring and localizing the moisture distribution inside the rice silo.

Governments globally have adopted various types of E-Taxation systems as part of E-Government plans in providing taxpayers a media to lodge tax claims at their ease. As this article is concerned with user satisfaction, a critical analysis of satisfaction literature related to Information Systems (IS), E-Taxation, E-Government, and technology in general, has been thoroughly done. For instance, technology such as Artificial Intelligence is being introduced in practically all aspects of society, including E-Taxation. This led to the understanding of how different researchers have theorized the ‘Satisfaction’ concept in the related areas. We focused on identifying the factors that constitute user satisfaction with the E-Taxation system. As a result, it is discovered that to date, limited studies have been devoted to evaluating user satisfaction with E-Taxation systems. Also, we found the emergence of several dimensions that require further investigation. Therefore, in this article, a conceptual model to evaluate E-Taxation satisfaction among taxpayers is proposed based on DeLone and McLean IS Success Model (D&M). Finally, we urged that the model should be empirically validated so that it will be practical to be adopted to guide local government to improve the quality of services delivered through E-Taxation systems.

Energy harvesting refers to the process of capturing and storing various external sources of energy in a wireless autonomous device. These sources of energy can include thermal energy, solar energy, salinity gradients, wind energy, and kinetic or ambient energy. Energy harvesting offers an alternative solution by powering devices in locations without traditional power sources, eliminating the need for batteries and wiring. In the context of radio frequency (RF) energy harvesting, the choice of antenna, such as the meander dipole antenna, plays a vital role in achieving high gain and optimal power transfer. The objective of this study is to propose the meander dipole antenna as the energy harvesting antenna at a frequency of 2.45 GHz using CST Studio Suite Software. FR-4 is chosen as the substrate with a permittivity of 4.8. The number of meander segments of the antenna design was varied from 2 to 5. The antenna performance also will be observed with difference in thickness substrate and ground. The results show that the thickness of the ground and substrate, the number of meanders and discrete port length does affect the antenna's frequency performance. The proposed meander dipole antenna operates effectively at 4 meanders with a substrate thickness of 2.3 mm and a ground layer thickness of 1 mm. This design yields a frequency of 2.4479 GHz, acceptable return loss value of - 15.204 dB, and a bandwidth of 28.079 MHz. The proposed antenna is suitable for use in RF energy harvesting, targeting ambient signals at 2.45 GHz.