A Review on Frequency Selection in Grain Moisture Content Detection

Ramli, Nurul Amira Mohd; Rahiman, Mohd Hafiz Fazalul; Kamarudin, Latifah Munirah; Zakaria, Ammar; Mohamed, Latifah

doi:10.1088/1757-899x/705/1/012002

Cited by 11 publications

(4 citation statements)

References 21 publications

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“…The recommended moisture level for rice storage to maintain optimum quality ranges from 14% to 16% [ 18 ]. In this research, the moisture content of rice samples was increased using the moistening method used in the previous research [ 19 , 20 , 21 , 22 ]. The samples with different moisture contents can be obtained by adding a predetermined amount of distilled water,

, as calculated from Equation (2).…”

Section: Methodsmentioning

confidence: 99%

Inline 3D Volumetric Measurement of Moisture Content in Rice Using Regression-Based ML of RF Tomographic Imaging

Almaleeh

Zakaria

Kamarudin

et al. 2022

Sensors

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The moisture content of stored rice is dependent on the surrounding and environmental factors which in turn affect the quality and economic value of the grains. Therefore, the moisture content of grains needs to be measured frequently to ensure that optimum conditions that preserve their quality are maintained. The current state of the art for moisture measurement of rice in a silo is based on grab sampling or relies on single rod sensors placed randomly into the grain. The sensors that are currently used are very localized and are, therefore, unable to provide continuous measurement of the moisture distribution in the silo. To the authors’ knowledge, there is no commercially available 3D volumetric measurement system for rice moisture content in a silo. Hence, this paper presents results of work carried out using low-cost wireless devices that can be placed around the silo to measure changes in the moisture content of rice. This paper proposes a novel technique based on radio frequency tomographic imaging using low-cost wireless devices and regression-based machine learning to provide contactless non-destructive 3D volumetric moisture content distribution in stored rice grain. This proposed technique can detect multiple levels of localized moisture distributions in the silo with accuracies greater than or equal to 83.7%, depending on the size and shape of the sample under test. Unlike other approaches proposed in open literature or employed in the sector, the proposed system can be deployed to provide continuous monitoring of the moisture distribution in silos.

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, as calculated from Equation (2).…”

Section: Methodsmentioning

confidence: 99%

Inline 3D Volumetric Measurement of Moisture Content in Rice Using Regression-Based ML of RF Tomographic Imaging

Almaleeh

Zakaria

Kamarudin

et al. 2022

Sensors

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“…Research studies on grain crops have been widely conducted on various aspects such as crops yield prediction [ 7 ], grain quality [ 8 ], soil moisture [ 9 , 10 ], and moisture content detection [ 11 , 12 , 13 , 14 , 15 ]. Numerous research studies have been conducted to find a reliable method to measure the moisture content in seasonal grain crops such as paddy, peanuts, and corn [ 16 , 17 , 18 ].…”

Section: Related Workmentioning

confidence: 99%

RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

Azmi

Kamarudin

Zakaria

et al. 2021

Sensors

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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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“…The grain moisture content generally is measured by direct-contact methods or indirect-noncontact methods [4]. The direct-contact methods usually employ desiccation or a chemical method to measure the moisture content [5], such as the oven-drying method and the Karl Fisher titration method, which have the advantages of simple steps and high accuracy of the detection result.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Measurement of Moisture Content of Paddy Rice Based on Microstrip Microwave Sensor Assisted by Machine Learning Strategies

2022

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Moisture content is extremely imoprtant to the processes of storage, packaging, and transportation of grains. In this study, a portable moisture measuring device was developed based on microwave microstrip sensors. The device is composed of three parts: a microwave circuit module, a real-time measurement module, and software to display the results. This work proposes an improvement measure by optimizing the thickness of paddy rice samples (8–13 cm) and adding the ambient temperatures and the moisture contents (13.66–27.02% w.b.) at a 3.00 GHz frequency. A random forest, decision tree, k-nearest neighbor, and support vector machine were applied to predict the moisture content in the paddy rice. Microwave characteristics, phase shift, and temperature compensation were selected as the input variables to the prediction models, which have achieved high accuracy. Among those prediction models, the random forest model yielded the best performance with highest accuracy and stability (R2 = 0.99, RMSE = 0.28, MAE = 0.26). The device showed a relatively stable performance (the maximum average absolute error was 0.55%, the minimum absolute error was 0.17%, the mean standard deviation was 0.18%, the maximum standard deviation was 0.41%, and the minimum standard deviation was 0.08%) within the moisture content range of 13–30%. The instrument has the advantages of real-time, simple structure, convenient operation, low cost, and portability. This work is expected to provide an important reference for the real-time in situ measurement of agricultural products, and to be of great significance for the development of intelligent agricultural equipment.

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A Review on Frequency Selection in Grain Moisture Content Detection

Cited by 11 publications

References 21 publications

Inline 3D Volumetric Measurement of Moisture Content in Rice Using Regression-Based ML of RF Tomographic Imaging

Inline 3D Volumetric Measurement of Moisture Content in Rice Using Regression-Based ML of RF Tomographic Imaging

RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques

Real-Time Measurement of Moisture Content of Paddy Rice Based on Microstrip Microwave Sensor Assisted by Machine Learning Strategies

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