Developing an Identification System for Different Types of Edible Mushrooms in Sri Lanka using Machine Learning and Image Processing

Abstract

This study aims to develop an image processing-based approach to identify edible mushroom species from other mushrooms using CNN and identify edible mushrooms based on ANN and identification growth stage of edible mushrooms using CNN image processing techniques. The identification of mushrooms can be challenging, especially for non-experts, due to the morphological similarities between edible and poisonous species. Also there have similarities between edible mushroom species. Therefore, there is a need for an accurate and efficient method to differentiate between edible and non-edible mushrooms and edible mushroom species. In this study, we propose the use of image processing techniques, such as feature extraction, segmentation, and classification, to analyze images of mushrooms and distinguish between edible mushroom species. We will collect images of different mushroom species found in Sri Lanka and use them to train and test our image processing algorithm. we will collect images of edible mushrooms are divide in to growth stage. Our approach has the potential to improve the safety and accessibility of wild mushroom harvesting, promote the consumption of nutritious edible mushrooms, and prevent accidental ingestion of poisonous mushrooms and improve identify edible mushroom species and find the growth stage of edible mushrooms.