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

Abstract

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.

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

“…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).…”

Recognition of Maize Phenology in Sentinel Images with Machine Learning

“…The model must find the k observations with the closest x i to x and average their responses [ 41 ]. Based on the distance, the analysed sample is classified in the class to which the majority of the neighbours belong [ 42 ].…”

“…The model must find the k observations with the closest x i to x and average their responses [41]. Based on the distance, the analysed sample is classified in the class to which the majority of the neighbours belong [42]. This classifier has shown important results in the classification of the land cover from data extracted from Sentinel 2 satellite images, reaching classification accuracies of 94% [43].…”

RFID and Drones: The Next Generation of Plant Inventory

“…RFID was only used for position and tracking while the drone was used for counting animals using cameras. A study reported using RFID and wireless technology to predict the moisture content of rice [18]. Both the Received Signal Strength Indicator (RSSI) from the two wireless transceivers were used for predicting the moisture content in rice using Artificial Neural Network.…”