• Corpus ID: 202713946

Arduino Based Tomato Ripening Stages Monitoring System View project

@inproceedings{Thengane2018ArduinoBT,
  title={Arduino Based Tomato Ripening Stages Monitoring System View project},
  author={Vishal G. Thengane and Mohit B. Gawande},
  year={2018}
}
Ripening and quality detection of mango using Arduino
Ripening is the methodology of maturing fruit to become more palatable. The ripening procedure of mango contains different stages in which a mango develops. There is a specific example in which the
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References

SHOWING 1-7 OF 7 REFERENCES
Arduino based supervision of banana ripening stages
TLDR
This paper is supervising these various ripening stages of banana using an Arduino system which will predict and display this complete process according to the colour changing stages of bananas.
Networked embedded greenhouse monitoring and control
TLDR
Hardware and software architecture of embedded web servers, 1-wire protocol for connecting sensors and actuators, and the experimental results of monitoring and control of laboratory greenhouse model are presented.
Design and test of tomatoes harvesting robot
TLDR
Based on HIS color model for image segmentation, the recognition accuracy was improved and sacs filled with constant pressure air were adopted as the grasping component of the picking end-effector, to prevent the fruits from being damaged.
A Zigbee based smart sensing platform for monitoring environmental parameters
TLDR
A Zigbee Based Smart Sensing Platform for Monitoring Environmental Parameters has been designed and developed and a smart weather station consisting of SiLab C8051F020 microcontroller based measuring units which collect the value of the temperature, relative humidity, pressure and sunlight.
The Effect of Colour Space on Image Sharpening Algorithms
TLDR
The effects of using different colour spaces on the application of image sharpening algorithms are explored to determine which colour space provides a result which does not differ immeasurably from the original with respect to chromaticity.
A zigbee-based home automation system
TLDR
The proposed ZigBee based home automation system and Wi-Fi network are integrated through a common home gateway and a dedicated virtual home is implemented to cater for the system's security and safety needs.
Comparing the Performance of L*A*B* and HSV Color Spaces with Respect to Color Image Segmentation
TLDR
A comparative analysis is performed between these two color spaces with respect to color image segmentation and it is found that HSV color space is performing better than L*A*B*.