Back to articles
Volume: 32 | Article ID: art00003
Deep Learning based Fruit Freshness Classification and Detection with CMOS Image sensors and Edge processors
  DOI :  10.2352/ISSN.2470-1173.2020.12.FAIS-172  Published OnlineJanuary 2020

CMOS Image sensors play a vital role in the exponentially growing field of Artificial Intelligence (AI). Applications like image classification, object detection and tracking are just some of the many problems now solved with the help of AI, and specifically deep learning. In this work, we target image classification to discern between six categories of fruits — fresh/ rotten apples, fresh/ rotten oranges, fresh/ rotten bananas. Using images captured from high speed CMOS sensors along with lightweight CNN architectures, we show the results on various edge platforms. Specifically, we show results using ON Semiconductor’s global-shutter based, 12MP, 90 frame per second image sensor (XGS-12), and ON Semiconductor’s 13 MP AR1335 image sensor feeding into MobileNetV2, implemented on NVIDIA Jetson platforms. In addition to using the data captured with these sensors, we utilize an open-source fruits dataset to increase the number of training images. For image classification, we train our model on approximately 30,000 RGB images from the six categories of fruits. The model achieves an accuracy of 97% on edge platforms using ON Semiconductor’s 13 MP camera with AR1335 sensor. In addition to the image classification model, work is currently in progress to improve the accuracy of object detection using SSD and SSDLite with MobileNetV2 as the feature extractor. In this paper, we show preliminary results on the object detection model for the same six categories of fruits.

Subject Areas :
Views 85
Downloads 21
 articleview.views 85
 articleview.downloads 21
  Cite this article 

Tejaswini Ananthanarayana, Raymond Ptucha, Sean C. Kelly, "Deep Learning based Fruit Freshness Classification and Detection with CMOS Image sensors and Edge processorsin Proc. IS&T Int’l. Symp. on Electronic Imaging: Food and Agricultural Imaging Systems,  2020,  pp 172-1 - 172-7,

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2020
Electronic Imaging
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA