S S symmetry Article Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things Muhammad Waleed 1 , Tai-Won Um 2,*, Tariq Kamal 3 and Syed Muhammad Usman 3 1 Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Korea;
[email protected] 2 College of Science and Technology, Duksung Women’s University, Seoul 01369, Korea 3 Department of Computer Systems Engineering, University of Engineering and Technology (UET), Peshawar 25120, Pakistan;
[email protected] (T.K.);
[email protected] (S.M.U.) * Correspondence:
[email protected]; Tel.: +82-2-901-8643 Abstract: In this paper, we apply the multi-class supervised machine learning techniques for clas- sifying 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—K-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, respec- tively. 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 Citation: Waleed, M.; Um, T.-W.; tilt-based classifications of farm machinery show good accuracy when used alone (with vibration Kamal, T.; Muhammad Usman, S.