Drones for Intelligent Agricultural Management

Mustafi, Subhranil; Ghosh, Pritam; Roy, Kunal; Dan, Sanket; Mukherjee, Kaushik; Mandal, Satyendra Nath

doi:10.1007/978-3-030-71172-6_4

Cited by 5 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation 1, 2, 3, 4 is a math equation that represents the movement of the quadcopter propeller, which is then calculated at the time of the quadcopter [30,31,32,33,34,35,36,37,38,40]. And the matrix of these parameters can be analyzed as in equation 5.…”

Section: Figure 7 Angular Velocity Rotation and Translation Linear Ve...mentioning

confidence: 99%

Drone simulation for agriculture and LoRa based approach

Adi¹,

Mustamu

Siregar

et al. 2021

IOTA

View full text Add to dashboard Cite

Spraying appropriately and regularly will help develop rice plants' growth and development to produce superior rice. These pesticides' spraying is sometimes uneven because of the vast land, limited human labor, and several other factors. that appropriate technology is needed that helps in the process of spraying rice pesticides using drones. Drones are deemed appropriate in spraying its advantages, among others, more effective, reducing the involvement of humans in work. Drones help track consistently and in detail the part of agricultural land that will be sprayed with pesticides, unlike humans. It is more automatic in monitoring, with the camera used on the drone can see the growth of rice plants directly and do recording or real-time connecting to the application server or IoT. Besides spraying pesticides, regular monitoring of plants can be done with drones. This study uses a UAV simulation for mapping the location of pesticide spraying, the results of contributions to large areas, and analysis of drone power consumption, which means allocating Drones to the area of land being managed.

show abstract

Section: Figure 7 Angular Velocity Rotation and Translation Linear Ve...mentioning

confidence: 99%

Drone simulation for agriculture and LoRa based approach

Adi¹,

Mustamu

Siregar

et al. 2021

IOTA

View full text Add to dashboard Cite

show abstract

Drone Aided Thermal Mapping for Selective Irrigation of Localized Dry Spots

et al. 2023

View full text Add to dashboard Cite

We report a smart irrigation system that allows selective irrigation of localized dry spots in an agricultural field. The proposed irrigation system uses a quadcopter drone equipped with a Thermal Infrared (TIR) camera and a GPS module to generate georeferenced thermal images that indicate the area and location of the dry spots in a survey area. Drones navigate and acquire aerial thermal images, which are then processed by an onboard edge intelligence module along with flight data (GPS coordinates, altitude, and drone direction). Smart sprinklers deployed on the field are able to wirelessly receive the coordinates of dry spots so they can be irrigated selectively. A terrestrial edge unit generates an irrigation pattern for the smart sprinklers using a pre-trained machine learning (ML) model to generate an irrigation pattern by varying the head rotation angle (θ) and the water flow control valve rotation angle(∅) of the smart sprinkler.

show abstract