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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Rice Grain Moisture Sensing Based on UHF RFID Tag

2022-06-24 , Radzi A.S.M. , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Ndzi D.L.

One of the critical steps in the post-production of paddy rice is to be stored in conditions that need to be controlled, especially the moisture content (MC) of the grains. The ability to determine and control moisture is a very important aspect of maintaining grain quality. This study aims to detect the MC of rice grain using UHF RFID technology. In this paper, three experiments have been carried out to detect the MC of rice in full rice grain-filled containers involving two conditions: with metal and without metal containers. The samples used consist of four 2 kg bags with MC levels of 11.875%, 16%, 20%, and 24%. The Received Signal Strength Indicator (RSSI) values were measured using a UHF handheld reader with two RFID tags to predict the MC. The results show an increasing RSSI pattern as the MC increases.

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

2021 , Nurul Amira Mohd Ramli , Mohd Hafiz Fazalul Rahiman , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Anita Ahmad , Ruzairi Abdul Rahim

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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Novel approach using passive UHF RFID for grain moisture detection

2022-01-01 , Ainaa Syamim Mohd Radzi , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Ndzi D.L.

This paper proposes a novel method to detect moisture hotspots and irregularities in rice grain storage using low-cost passive UHF RFID technology. Experiments were designed with a UHF RFID handheld reader to test rice moisture levels of 12%, 16%, 20% and 24%. Two containers were used in the study where Container A was filled with grains with a 12% moisture level and container B is inserted into Container A and contained 2 kg rice samples of varying moisture contents. The RFID reader was positioned outside the container to measure the received signal strength indicator (RSSI) from the RFID tags placed within the containers. The attenuation of the signal is analyzed to obtain a correlation between moisture content (MC) and RSSI values. Results show a positive correlation between the RSSI and MC of rice which can be used to identify inconsistencies in moisture distribution in stored grain. An empirical model has been proposed which can be used to estimate RSSI values given the moisture content or vice versa for RFID operating at 915 MHz.

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Comparative study of vibration energy harvesting on home appliances using piezoelectric energy harvester

2023 , Muhammad Shakir Abd Yazib , Norshafinash Saudin , Mohd Azizi Mohamed , Nur Adyani Mohd Affendi , Latifah Mohamed , Hend Sayed Mohamed

This project was developed to harvest vibration energy using a piezoelectric energy harvester. The availability of home appliance vibration energy is a promising solution to get clean energy resources to manipulate wasted energy. When the appliances’ vibration hits the piezoelectric energy harvester surface, pressure is applied to the piezoelectric transducers and converts mechanical energy into electrical energy. The piezoelectric energy harvester’s efficiency depends on the availability of the home appliances’ vibration energy; thus, using multiple piezoelectric transducers in series generates more power. The piezoelectric’ alternating current (AC) output is fed to a Cockroft-Walton voltage multiplier (CWVM) to convert into direct current (DC) and boost the output. Four piezoelectric transducers connected in series have successfully produced a voltage of up to 4.7 V. Its output voltage can be harnessed to power low-voltage electronic devices.

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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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Publication

Rice Grain Moisture Sensing Based on UHF RFID Tag

2022-06-24 , Ainaa Syamim Mohd Radzi , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Ndzi D.L.

One of the critical steps in the post-production of paddy rice is to be stored in conditions that need to be controlled, especially the moisture content (MC) of the grains. The ability to determine and control moisture is a very important aspect of maintaining grain quality. This study aims to detect the MC of rice grain using UHF RFID technology. In this paper, three experiments have been carried out to detect the MC of rice in full rice grain-filled containers involving two conditions: with metal and without metal containers. The samples used consist of four 2 kg bags with MC levels of 11.875%, 16%, 20%, and 24%. The Received Signal Strength Indicator (RSSI) values were measured using a UHF handheld reader with two RFID tags to predict the MC. The results show an increasing RSSI pattern as the MC increases.

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Publication

Rice Grain Moisture Sensing Based on UHF RFID Tag

2022-06-24 , Ainaa Syamim Mohd Radzi , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Ndzi D.L.

One of the critical steps in the post-production of paddy rice is to be stored in conditions that need to be controlled, especially the moisture content (MC) of the grains. The ability to determine and control moisture is a very important aspect of maintaining grain quality. This study aims to detect the MC of rice grain using UHF RFID technology. In this paper, three experiments have been carried out to detect the MC of rice in full rice grain-filled containers involving two conditions: with metal and without metal containers. The samples used consist of four 2 kg bags with MC levels of 11.875%, 16%, 20%, and 24%. The Received Signal Strength Indicator (RSSI) values were measured using a UHF handheld reader with two RFID tags to predict the MC. The results show an increasing RSSI pattern as the MC increases.

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Publication

Novel Approach Using Passive UHF RFID for Grain Moisture Detection

2022-01-01 , Ainaa Syamim Mohd Radzi , Latifah Munirah Kamarudin , Latifah Mohamed , Ammar Zakaria , Mohd Hafiz Fazalul Rahiman , Syed Muhammad Mamduh Syed Zakaria , Ndzi D.L.

This paper proposes a novel method to detect moisture hotspots and irregularities in rice grain storage using low-cost passive UHF RFID technology. Experiments were designed with a UHF RFID handheld reader to test rice moisture levels of 12%, 16%, 20% and 24%. Two containers were used in the study where Container A was filled with grains with a 12% moisture level and container B is inserted into Container A and contained 2 kg rice samples of varying moisture contents. The RFID reader was positioned outside the container to measure the received signal strength indicator (RSSI) from the RFID tags placed within the containers. The attenuation of the signal is analyzed to obtain a correlation between moisture content (MC) and RSSI values. Results show a positive correlation between the RSSI and MC of rice which can be used to identify inconsistencies in moisture distribution in stored grain. An empirical model has been proposed which can be used to estimate RSSI values given the moisture content or vice versa for RFID operating at 915 MHz.

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Publication

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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