Fruit Freshness Detection Using Raspberry Pi


  • Rohit Piske
  • Aparna Nanaware
  • Vishakha Sawant
  • Komal More


Raspberry Pi, AI, Machine Learning, CNN, Python.


The quality of fruits or vegetables performs an essential role in customer consumption. This project explains the importance of system for detecting the degradation of fruits that may be required at industrial level, for packaging, etc. This proposed paper sheds light on the system which is a low cost yet effective fruit quality detecting system in terms of freshness and rottenness using Raspberry Pi. The developed method can recognize the fresh fruits from rotten fruits and is an advantage over the current traditional method of identifying the fruits quality which is more time consuming and has reasonably less rate for classifying fruits. Besides, hand sorting of rotten fruits from fresh fruits is more time consuming, tiring and tedious work. This project focuses on creating a smart AI camera using Raspberry Pi for effective sorting of fresh fruits from rotten fruits. Fruit quality detection system involves steps like image capturing, image processing, feature extraction, training, image classification and then recognition. Here, CNN algorithm (Convolutional Neural Network) is used to train the Machine learning model. A Raspberry Pi camera is used to capture an image of particular fruits then the trained model detects the quality of fruits and sorts them accordingly as fresh fruit or rotten fruit. In this project, software which includes Python code is used.


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How to Cite

Rohit Piske, Aparna Nanaware, Vishakha Sawant, & Komal More. (2023). Fruit Freshness Detection Using Raspberry Pi. International Journal of Progressive Research in Science and Engineering, 4(5), 70–72. Retrieved from