Paddy rice moisture content meter

Ponglangka, Wirot

doi:10.1109/iccas.2015.7364632

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…The direct method involves measuring weight loss by removing water from a sample and an indirect method utilizes electronic instruments to measure electric characteristics from which MC is determined [16]. Recently, research has focused on non-destructive moisture measurements based on electrical resistance [17], capacitance [18], microwave [19], Nuclear Magnetic Resonance (NMR) and Near-infrared spectroscopy (NIR) [20] measurements. These methods provide reliable measurements and faster results.…”

Section: Related Workmentioning

confidence: 99%

Novel Approach Using Passive UHF RFID for Grain Moisture Detection

Radzi¹,

Kamarudin²,

Mohamed³

et al. 2022

Lecture Notes in Electrical Engineering

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Section: Related Workmentioning

confidence: 99%

Novel Approach Using Passive UHF RFID for Grain Moisture Detection

Radzi¹,

Kamarudin²,

Mohamed³

et al. 2022

Lecture Notes in Electrical Engineering

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“…These limitations have led to studies to develop alternative methods that are faster and cheaper. A cheaper, indirect and portable device for moisture content measurement generally called moisture metres have been developed [ 23 , 24 , 25 ]. Moisture metres operate based on the concept of dielectric constant.…”

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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“…Li et al [11] proposed a method for measuring grain moisture content (13-26%) based on dielectric loss tangent and the maximum error was less than 0.5%. Wirot et al [12] designed a paddy rice moisture content meter based on the principle of electrical capacitance properties and the detection accuracy reached 97.83%.…”

Section: Introductionmentioning

confidence: 99%

Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy

Lin

Xiao

et al. 2019

Applied Sciences

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Rice grain moisture has a great impact on th production and storage storage quality of rice. The main objective of this study was to design and develop a rapid-detection sensor for rice grain moisture based on the Near-infrared spectroscopy (NIR) characteristic band, aiming to realize its accurate and on-line measurement. In this paper, the NIR spectral information of grain samples with different moisture content was obtained using a portable NIR spectrometer. Then, the partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were applied to model and analyze the spectral data to find the rice grain moisture NIR spectroscopy. As a result, the 1450 nm band was sensitive to the rice grain moisture and a rapid-detection sensor was developed with a 1450 nm light emitting diode (LED) light source, InGaAs photodiode, lens and filter, whose basic principle is to establish the relationship between the rice grain moisture and the measured voltage signal. To evaluate the sensor performance, rice grain samples with 13–30% moisture content were detected, the coefficient of determination R2 was 0.936, and the sum of squares for error (SSE) was 23.44. It is concluded that this study provides a spectroscopic measuring method, as well as developing an effective and accurate sensor for the rapid determination of rice grain moisture, which is of great significance for monitoring the quality of rice grain during its production, transportation and storage process.

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Paddy rice moisture content meter

Cited by 5 publications

References 6 publications

Novel Approach Using Passive UHF RFID for Grain Moisture Detection

Novel Approach Using Passive UHF RFID for Grain Moisture Detection

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

Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy

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