Sensor Data of Mobile Sink for Precision Agriculture

Citation Author(s):
Indian Institute of Technology (BHU), India
Hari Prabhat
Indian Institute of Technology (BHU), India
Indian Institute of Technology (BHU), India
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Last updated:
Mon, 04/05/2021 - 15:12
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This dataset comprises sensory data of in and out miniature vehicle (mobile sink) movement in the agriculture fields. The dataset is collected from the miniature vehicle using a 9-axis Inertial Measurement Unit (IMU) sensor (MPU-9250) placed on the top of the vehicle. Though the vehicle is small but designed to handle all the hurdles of the agricultural land, such as rough and muddy surface. This dataset aims to facilitate appropriate path planning in the agricultural field for the automatic cultivation of seeds, manure spread, and nutrients insertion. In addition, this path planning helps in cheaper and convenient monitoring of farmland.


The dataset contains Multivariate Time Series (MTS) of the miniature vehicle’s in and out movement in the agricultural field. The miniature vehicle collects the sensory data of the Inertial Measurement Unit (IMU) sensor (MPU-9250) deployed on it. MPU-9250 is a 9-axis sensor used for recording the linear and angular motion of the vehicle in the jerking condition due to the uneven surface of the farmland. MPU-9250 comprises a 3-axis accelerometer, 3-axis gyroscope, and 3-axis magnetometer. These sensors are connected to a NodeMCU with an attached SD card, which stores the data. The sensory data is collected from sixteen different agricultural fields at a sampling rate of 5 Hz for 5 minutes each. Therefore, each field produces 1500 instances of tri-axial sensors (accelerometer, gyroscope, and magnetometer). Hence, the total instances we have collected is 1500 X 16 =24000.


Can I get the dataset ,

Submitted by Abdullah Mamun on Tue, 04/20/2021 - 16:59

Can i get tge dataset please ?

Submitted by MOUSSAOUI DHAYA... on Sat, 05/08/2021 - 04:23

Can i get the dataset please ?

Submitted by MOUSSAOUI DHAYA... on Sat, 05/08/2021 - 04:23