Deep learning-based approach for tomato classification in complex scenes
Mikael A. Mousse Bethel C. A. R. K. Atohoun Cina Motamed
Tracking ripening tomatoes is time consuming and labor intensive. Artificial
intelligence technologies combined with those of computer vision can help users
optimize the process of monitoring the ripening status of plants. To this end,
we have proposed a tomato ripening monitoring approach based on deep learning
in complex scenes. The objective is to detect mature tomatoes and harvest them
in a timely manner. The proposed approach is declined in two parts. Firstly,
the images of the scene are transmitted to the pre-processing layer. This
process allows the detection of areas of interest (area of the image containing
tomatoes). Then, these images are used as input to the maturity detection
layer. This layer, based on a deep neural network learning algorithm,
classifies the tomato thumbnails provided to it in one of the following five
categories: green, brittle, pink, pale red, mature red. The experiments are
based on images collected from the internet gathered through searches using
tomato state across diverse languages including English, German, French, and
Spanish. The experimental results of the maturity detection layer on a dataset
composed of images of tomatoes taken under the extreme conditions, gave a good
classification rate.