Step1: make a standardized template library (all fruit, 51x51).step2: extract individual object from a image and generate a single line edge (contour).step3: standardize the image (normalized, single line, 51x51).step4: use the central point as fix point, clockwise scan the two template images (contour projection).step5: choose a tolerance value (3 or 5 pixels) to evaluate the image with each template, and get a score (contour matching).step6: decide what kind of fruit it is by lowest score.relational template library.Skills:,See more:,.

Aug 03, 2016  2. Related Work/Background. Although many researchers have tackled the problem of fruit detection, such as the works presented in 8,9,10,11,12,13, the problem of creating a fast and reliable fruit detection system persists, as found in the survey by.This is due to high variation in the appearance of the fruits in field settings, including colour, shape, size.

Goal:

• To implement a Convolutional Neural Network in Tensorflow which can accurately disntinguish fruits from each other
• To undergo an incremental devleopment cycle in building the model, starting with a few classes and building upwards

Results:

• Final model trained to classify 40 fruits. Successful with 92.47% Accuracy
• Structure of model and statistics about it's success during each step of the design process are in changelog.txt

Data set used: fruits-360 dataset from Horea Muresan, Mihai Oltean, Fruit recognition from images using deep learning, Technical Report, Babes-Bolyai University, 2017

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