Agriculture robotic vehicle based pesticide sprayer with efficiency optimization

Umayal, C.

doi:10.1109/tiar.2015.7358532

Cited by 24 publications

(8 citation statements)

References 11 publications

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“…Smart agriculture conserves water, fertilizer, and labor while reducing waste. An automated harvesting system monitors agrarian land and reduces human mistakes (37). So, the resource is used precisely when the crops require it.…”

Section: Water Irrigationmentioning

confidence: 99%

An in-Depth Study of Smart Agriculture Based on Internet of Things and Wireless Sensor Networks

Prasath¹,

Navaneethan²

2022

ECS Trans.

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Most governments monitor and prioritize agriculture. Subsidies and loans to farmers to promote food production are common in many countries. They are pushing farmers to employ modern agrarian technology to boost grain quality and farming. Smart farming relies on Wireless Sensor Networks and the Internet of Things. Using the above technology, farmers may monitor their property remotely and manage their farms at any time sensors nodes installed on fields capture agricultural growth data using GPS. Sensor nodes collect data on soil nutrient levels, temperature, and humidity. The sensor delivers data to a wireless gateway, which sends it to a remote server. Researchers and scientists have been working hard to improve farmers' productivity, quantity, and quality. This study examines the impact of new technologies and sensors used in smart farming on food security and how such devices are transforming agricultural sectors to meet rising population demands.

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Section: Water Irrigationmentioning

confidence: 99%

An in-Depth Study of Smart Agriculture Based on Internet of Things and Wireless Sensor Networks

Prasath¹,

Navaneethan²

2022

ECS Trans.

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“…The results are then converted into rotation speed ( ) in units of rotations per minute (RPM) using equation (11).…”

Section: Inverse Kinematics Based Control Developmentmentioning

confidence: 99%

Implementation of a Camera Sensor Pixy 2 CMUcam5 to A Two Wheeled Robot to Follow Colored Object

Perkasa

Megantoro²,

Winarno

2021

jrc

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This article discusses the design of a colored object follower robot. The colored object used has a simple shape. For the detection process, a wheeled robot that uses sensors based on digital images of Pixy 2. Pixy2 can learn to detect objects that you teach it, just by pressing a button. Additionally, Pixy2 has new algorithms that detect and track lines for use with line-following robots. Pixy2 camera is able to recognize and track all objects whose color has been memorized. In maneuvering, this robot has 2 wheels on the right and left. Movement control is carried out by the Arduino Uno microcontroller board. This robot moves according to the direction of movement of the object. The conclusion obtained in this research is that this wheeled robot can be examined from the left, front and right side objects properly, then it follows the direction of the detected object.

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“…The proposed solution for estimating the ground truth for the altitude was a competitive system [27]. This method consists of considering each sensor as an agent voting for an altitude.…”

Section: Ground Truthmentioning

confidence: 99%

“…This method consists of considering each sensor as an agent voting for an altitude. This technique is used in AI ( [27]) and is usually based on discreet outputs from which each agent can choose. To be able to use this policy, it was modified to accept continuous values.…”

Section: Ground Truthmentioning

confidence: 99%

Evaluation of Altitude Sensors for a Crop Spraying Drone

Hentschke

Freitas

Hennig³

et al. 2018

Drones

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Abstract:This work aims to study and compare different range finders applied to altitude sensing on a rotating wings UAV. The specific application is the altitude maintenance for the fluid deployment valve aperture control in an unmanned pulverization aircraft used in precision agriculture. The influence of a variety of parameters are analyzed, including the tolerance for crop inconsistencies, density variations and intrinsic factors to the process, such as the pulverization fluid interference in the sensor's readings, as well as their vulnerability to harsh conditions of the operation environment. Filtering and data extraction techniques were applied and analyzed in order to enhance the measurement reliability. As a result, a wide study was performed, enabling better decision making about choosing the most appropriate sensor for each situation under analysis. The performed data analysis was able to provide a reliable baseline to compare the sensors. With a baseline set, it was possible to counterweight the sensors errors and other factors such as the MSE for each environment to provide a summarized score of the sensors. The sensors which provided the best performance in the used metrics and tested environment were Lightware SF11-C and LeddarTech M16.

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Agriculture robotic vehicle based pesticide sprayer with efficiency optimization

Cited by 24 publications

References 11 publications

An in-Depth Study of Smart Agriculture Based on Internet of Things and Wireless Sensor Networks

An in-Depth Study of Smart Agriculture Based on Internet of Things and Wireless Sensor Networks

Implementation of a Camera Sensor Pixy 2 CMUcam5 to A Two Wheeled Robot to Follow Colored Object

Evaluation of Altitude Sensors for a Crop Spraying Drone

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