Latifah Mohamed
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Preferred name
Latifah Mohamed
Official Name
Mohamed , Latifah
Alternative Name
Mohamed, Latifah
Mohamed, L.
Main Affiliation
Scopus Author ID
16943523200
Researcher ID
IAK-5403-2023

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A new method of rice moisture content determination using voxel weighting-based from radio tomography images

2021-06-01 , Ramli N.A.M. , Mohd Hafiz Fazalul Rahiman , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Anita Ahmad , Rahim R.A.

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.

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Rf-based moisture content determination in rice using machine learning techniques

2021-03-01 , Azmi N. , Latifah Munirah Kamarudin , Ammar Zakaria , Ndzi D.L. , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Latifah Mohamed

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.

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A Design and Development of a Wireless Sensor Network for Potential Monitoring and Localization

2020-11-01 , Ramli N.A.M. , Mohd Hafiz Fazalul Rahiman , Malik M.F.A. , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Abdullah M.S.M.

This paper presents an analysis of the received signal strength indicator (RSSI) from the radio frequency signals for human identification in an indoor wireless sensor network (WSN). Instead of using closed-circuit television as the existing security platform, this indoor safety system was improved with a convenient, cheap, and low-power solution. The system was developed using 20 of ESP8266-12F Wi-Fi modules transmitters and another 2 of ESP8266-12F as the access points located in 3 m × 3 m area of interest. With a suitable coordinate of sensor nodes, a WSN telemetry could be established to minimize the blind spot area and limit the movement of the intruder with a minimum area of 0.2 m2. The RSSI measurement was repeatedly conducted for three different conditions, in an empty room, with the presence of a single intruder and the presence of multiple intruders. Based on the RSSI values, we found that there are distinctive features of data that can be utilized as flags for classifying the three above conditions. Besides that, to justify the efficiency of system performance, we also examined the sensitivity of RSSI values towards the variation of temperature. Our results show that the RSSI average values for both morning and night were practically the same. However, during the afternoon, the RSSI signal strength fluctuated by − 1.0 dBm. These results motivate the development of an alarm system that only uses the RSSI statistics to detect human presence.

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Relative Localization Method of Wet Spot of Grain using Array of Passive RFID Tags

2021-12-01 , Azmi N. , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Ndzi D.L.

Radio Frequency Identification (RFID) enables a large number of object monitoring since semi/passive tags are independent of batteries. In our previous work, the possibility of using different wireless technologies such as Wireless Sensor Network (WSN), Wireless Local Area Network (WLAN) and Radio Frequency Identification (RFID) to determine the moisture content in rice was investigated. Finding from our previous work suggest that RFID can be used to determine the moisture content of rice. While numerous research have been conducted for moisture content of grain, however, to author's knowledge, there is only a few studies conducted on the localization of grain hostpot. Therefore, this study aims to investigate if the passive RFID array can be used to localize the location of the wet spot of grain. Prior, the experiment, a suitable setting for the RFID system were determined. In addition, a simple test was conducted to select a suitable operating frequency. From the investigation, the result indicates that only frequency channels 865, 866, 867, 868 and 869 MHz can detect all 30 tags. Meanwhile, frequency channel in the range 902 to 928 MHz detects 26 to 29 unique tags. Hence, 868 MHz was selected as the operating frequency throughout the experiment. The findings indicate that the RSSI value measured by the RFID reader decreased as the moisture of the sample increased when the tags were blocked by the sample placed at the designated location during the test.

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Rf-based moisture content determination in rice using machine learning techniques

2021-03-01 , Azmi N. , Latifah Munirah Kamarudin , Ammar Zakaria , Ndzi D.L. , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Latifah Mohamed

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.

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Simulation of Radio Tomographic Imaging for Measurement Rice Moisture Content

2020-08-01 , Mohammed Saeed Moqbel Abdullah , Latifah Munirah Kamarudin , Mohd Hafiz Fazalul Rahiman , Latifah Mohamed , Ammar Zakaria

Radio Tomographic Imaging (RTI) is an emerging technology for imaging the attenuation caused by physical objects in wireless networks that perform wireless receive signal strength (RSS) measurements obtain a reconstruction of objects inside an area of interest to know the different moisture content of rice in the silo. The simulation results analysis has been performed. The image of the phantoms was reconstructed by the selected image reconstruction algorithms, which are Linear Back Projection (LBP), Filtered Back Projection (FBP), and Gaussian. Evaluation of this work was assessed by using three image quality assessment techniques Mean Structural Similarity Index (MSSIM). MSSIM was used to analyze the reconstructed images. Among the three proposed images reconstruction algorithms linear back projection, filtered back projection, and Gaussian algorithm. Gaussian seems to be a more reliable option for reconstructing the image of moisture content of rice in a silo by using 20 RF nodes in the RTI system. This paper discusses in detail the use of shadowing losses on links between RF sensors in a wireless community to image the attenuation of moisture content inside the wi-fi network vicinity.

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