A grain moisture detection method based on ESP32-CSI
（1. College of Information Science and Engineering, Henan University of Technology, Zhengzhou， Henan 450001, China； 2. Henan Key Laboratory of Grain Photoelectric Detection and Control， Henan University of Technology, Zhengzhou， Henan 450001, China； 3. Key Laboratory of Grain Information Processing and Control of Ministry of Education， Henan University of Technology， Zhengzhou， Henan 450001, China）
Abstract：To realize fast and accurate measurement of grain moisture with low cost, the miniaturized channel state information (CSI) acquisition equipment was used for grain moisture detection. Two feature selection algorithms of random forest and principal component analysis were adopted to extract the feature subcarriers of the CSI amplitude index, and the ten kinds of grain moisture were classified based on the selected feature subcarriers. Considering that the application in the mobility scene was limited by power consumption and arithmetic power, the breadth learning system with simple structure, fast operation speed and low arithmetic power requirement was selected for processing CSI data and was compared with the traditional convolutional neural network(CNN) in terms of accuracy and training time. The enhancement nodes of the broad learning system (BLS) were dynamically increased. The experimental results show that the principal component analysis(PCA) algorithm maximally eliminates the redundant information in the CSI data. Compared with the CNN, the BLS can achieve faster speed and better accuracy. Therefore, the PCA-BLS combination achieves the best classification results. Increasing the number of enhancement nodes can increase the training time, but the recognition accuracy is improved to some extent.
RAN Y X, YU J, CHANG J, et al. A method of fall detection based on CSI[J]. Journal of Yunnan University(Natural Sciences Edition), 2020,42(2):220-227.(in Chinese)
WANG Z, CHANG J. CSI cross-domain gesture recognition method based on 3D convolutional neural network[J]. Computer Science, 2021,48(8):322-327.(in Chinese)
［4］
YANG W D, WANG X Y, SONG A X, et al. Wi-Wheat: contact-free wheat moisture detection with commodity WiFi[C]∥IEEE International Conference on Communications, 2018:1-6.
SHAN S W, YANG W D, XIAO L, et al. Wi-Pest: a method for detecting stored grain pests based on CSI [J]. Journal of the Internet of Things, 2020,4(4):51-61.(in Chinese)
［6］
刘宏亮,王旺平. 电阻式粮食水分检测仪的研制[J]. 轻工科技, 2014,30(7):72-73
LIU H L, WANG W P. Development of resistance type grain moisture detector[J]. Light Industry Science and Technology, 2014,30(7):72-73.(in Chinese)
LI J X, LENG W X. Study on determination of grain moisture contet based on cylindrical capacitor[J]. Phy-sics and Engineering, 2019,29(2):100-105.(in Chinese)
LI X N, LIU R J, CHEN Z, et al. Research and design of grain moisture microwave detection system[J]. Science & Technology Information, 2017, 15(18):2.(in Chinese)
GAN F H, LIU C X, ZHANG W H, et al. Development of grain moisture detecting instrument based on friction resistance method[J]. Journal of Agricultural Mechani-zation Research, 2018, 40(1): 91-95.(in Chinese)
［10］
方建军. 粮食水分在线测量的声学方法[J]. 北方工业大学学报, 2000,12(1):57-60
FANG J J. Acoustic methods for online measurement of moisture content in grains[J]. Journal of North China University of Technology, 2000,12(1):57-60.(in Chinese)
YANG Y Q, WANG J P, WANG C Z. Study on on-line measurement of grain moisture content by neutron gauge[J]. Transactions of the Chinese Society of Agricultural Engineering, 2000,16(5):99-101.(in Chinese)
［12］
WANG J, CHANG L Q, AGGARWAL S, et al. Soil moisture sensing with commodity RFID systems[C]∥Proceedings of the 18th International Conference on Mobile Systems, 2020:273-285.
［13］
LIU Y T, JIANG L D, KONG L H, et al. Wi-Fruit: see through fruits with smart devices[J]. Proceedings of ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2021,5(4):1-29.
［14］
STEVEN M H, BULUT E. Lightweight and standalone IoT based WiFi sensing for active repositioning and mo-bility[C]∥21st International Symposium on a World of Wireless, 2020:277-286.
［15］
STEVEN M H, BULUT E. Adversarial occupancy monitoring using one-sided through-wall WiFi sensing[C]∥IEEE International Conference on Communications，2021:1-6.
［16］
STEVEN M H, ERDAG D, BULUT E. Towards dense and scalable soil sensing through low-cost WiFi sensing networks[C]∥2021 IEEE 46th Conference on Local Computer Networks, 2021:549-556.
［17］
CHOI H, FUJIMOTO M, MATSUI T, et al. Wi-CaL: WiFi sensing and machine learning based device-free crowd counting and localization[J]. IEEE Access, 2022,10:24395-24410.
［18］
MA H J, ZHANG Y G, HONG X Y, et al. A method of identifying water level information through WiFi signal[C]∥2021 Photonics & Electromagnetics Research Symposium, 2021:1739-1744.
［19］
YU G, ZHU Y N, LI J, et al. WiMate: location-independent material identification based on commercial WiFi devices[C]∥2021 IEEE Global Communications Conference, 2021:1-6.
［20］
AMARTANSH D, SOOD P, SANTOS J, et al. An enhanced approach to imaging the indoor environment using WiFi RSSI measurements[J]. IEEE Transactions on Vehicular Technology, 2021,70(9):8415-8430.
［21］
DANIEL H, HU W J, SHETH A, et al. Tool release: gathering 802.11n traces with channel state information[J]. Acm Sigcomm Computer Communication Review, 2011,41(1):53.
［22］
LI Z J, XIE Y X, LI O, et al. Recitation: rehearsing wireless packet reception in software[C]∥Proceedings of the 21st Annual International Conference on Mobile Computing and Networking， 2015:291-303.
YE R K, KONG Q C, LI D L, et al.Shrimp freshness detection method based on broad learning system[J]. Spectroscopy and Spectral Analysis, 2022,42(1):164-169.(in Chinese)
