On this mission we see how one can construct a tool that detects maturation levels based mostly on coloration with a neural community mannequin. As fruit and veggies ripen, they modify coloration as a result of 4 households of pigments: chlorophyll (inexperienced), carotenoids (yellow, pink, orange), flavonoids (pink, blue, purple), betalain (pink, yellow, purple).
These pigments are teams of molecular constructions that soak up a selected set of wavelengths and replicate the remainder. Unripe fruits are inexperienced as a result of chlorophyll of their cells. As they mature, the chlorophyll breaks down and is changed by orange carotenoids and pink anthocyanins. These compounds are antioxidants that stop the fruit from spoiling too rapidly within the air.
After performing some analysis on coloration change processes throughout fruit and vegetable ripening, we determined to construct a synthetic neural community (ANN) based mostly on the classification mannequin to interpret the colour of fruit and greens and predict ripening levels.
Earlier than constructing and testing the neural community mannequin, we developed an online utility in PHP (working on a Raspberry Pi 3B +) to gather the colour knowledge generated by the AS7341 seen gentle sensor and create a dataset on the maturation levels . We used an Arduino Nano 33 IoT to ship the produced knowledge to the online utility.
After finishing the dataset, we constructed the synthetic neural community (ANN) with TensorFlow.
