Stale Fruit Detection through image processing and considering gas emissions

Project Title

Stale Fruit Detection through image processing and considering gas emissions

Project Area of Specialization Artificial IntelligenceProject Summary

People Nowadays are busy and they don't bother keeping care for less important thing in their life.

Same for this problem when people keep their fruits in refrigerators and usually forget about them and they get stale.

This project helps cater this problem by scanning and proper detection of stale fruits in a refrigerator and notifying the user so they can remove them.

Project Objectives

1) Machine Learning of Stale Fruits through image processing
2) Gas sensor calibration to detect ethanol vapor emission from fruits.
3) Programming controller to send notification to phone through communication medium

Project Implementation Method

Project can be implemented by using both:

1) Machine Learning through image processing
2) Gas Sensor (detecting gas emission)

Using Arduino or Raspberry, and programming them to calibrate and use both capabilities perfectly.

Benefits of the Project

Users don't need to keep track of fruits when a fruits goes stale, as the software will notify about the fruit to the user. The User also is spared from cleaning of the refrigerator again and again.

Technical Details of Final Deliverable

The Hardware will be fit into a refrigerator and will scan and detect fruit through image recognition and gas emission. It will help identify 5 types of fruits initially. The type of fruits which are included are those with soft peels that are easy to detect when using imagery. It will scan on a timely basis with a programmable frequency, and will notify whenever there's a detection

Final Deliverable of the Project HW/SW integrated systemType of Industry Food , Health Technologies Artificial Intelligence(AI)Sustainable Development Goals Good Health and Well-Being for PeopleRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	21900
Raspberry pi	Equipment	2	6500	13000
mq-3 gas sensor	Equipment	3	600	1800
Camera module	Equipment	2	2000	4000
Adapter	Equipment	2	1000	2000
Documentation	Miscellaneous	1	500	500
Presentation Material	Miscellaneous	1	600	600