Computer Vision

Computer Vision – Fruits Recognition

The solution created by APRO is able to detect strawberries on the image taken by camera on a daily basis and recognize different states of each berry starting from flower and till a ripe berry. This solution can be adapted to detection and recognition of any type of berry. The solution uses artificial intelligence (AI) technologies and utilizes modern neural networks.

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Aim of the Project

● Create an AI model to detect and recognize strawberries on an image from a camera
● Develop a web service & API for the AI model integration and usage

Results Achieved

● Accuracy 84%
● Image processing time <0.5 sec


Main Technologies

● AI Architecture: Faster RCNN (backbone resnet 50)
● For ML: TensorFlow, Keras, Python.
● For Backend: Flask, Python.
● For Environment: Docker.

The development was made from scratch using 2500 marked up images with defined 6 states of berries. During R&D phase we have compared Deeplabv3+ Gaussian and Faster RCNN (backbone resnet 50), the last approach gave a bit better results.


Model Features

The model is able to work with the following defects on images:
– partly blurred images
– bright glares from the sun

The model can detect 6 states of a berry:
– flower
– small green berry
– green berry
– white berry
– start colouring berry
– ripe berry

Initial model accuracy was increased by increasing the number of learning dataset and quality of markup.

fruites berry strawberries image recognition

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