{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:39:01Z","timestamp":1775540341495,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information &amp; communications Technology Planning &amp; Evaluation (IITP) grant funded by the Korea Government (MSIT)","award":["2020-0-00833"],"award-info":[{"award-number":["2020-0-00833"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In this paper, we apply the multi-class supervised machine learning techniques for classifying the agriculture farm machinery. The classification of farm machinery is important when performing the automatic authentication of field activity in a remote setup. In the absence of a sound machine recognition system, there is every possibility of a fraudulent activity taking place. To address this need, we classify the machinery using five machine learning techniques\u2014K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB). For training of the model, we use the vibration and tilt of machinery. The vibration and tilt of machinery are recorded using the accelerometer and gyroscope sensors, respectively. The machinery included the leveler, rotavator and cultivator. The preliminary analysis on the collected data revealed that the farm machinery (when in operation) showed big variations in vibration and tilt, but observed similar means. Additionally, the accuracies of vibration-based and tilt-based classifications of farm machinery show good accuracy when used alone (with vibration showing slightly better numbers than the tilt). However, the accuracies improve further when both (the tilt and vibration) are used together. Furthermore, all five machine learning algorithms used for classification have an accuracy of more than 82%, but random forest was the best performing. The gradient boosting and random forest show slight over-fitting (about 9%), but both algorithms produce high testing accuracy. In terms of execution time, the decision tree takes the least time to train, while the gradient boosting takes the most time.<\/jats:p>","DOI":"10.3390\/sym13030403","type":"journal-article","created":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T10:25:18Z","timestamp":1614594318000},"page":"403","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9770-6293","authenticated-orcid":false,"given":"Muhammad","family":"Waleed","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Korea"}]},{"given":"Tai-Won","family":"Um","sequence":"additional","affiliation":[{"name":"College of Science and Technology, Duksung Women\u2019s University, Seoul 01369, Korea"}]},{"given":"Tariq","family":"Kamal","sequence":"additional","affiliation":[{"name":"Department of Computer Systems Engineering, University of Engineering and Technology (UET), Peshawar 25120, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1900-3829","authenticated-orcid":false,"given":"Syed Muhammad","family":"Usman","sequence":"additional","affiliation":[{"name":"Department of Computer Systems Engineering, University of Engineering and Technology (UET), Peshawar 25120, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,1]]},"reference":[{"key":"ref_1","unstructured":"World Economic Forum (2016). 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