Recognition of mushrooms and classification of edible and toxic families using hardware implementation of CNN algorithms on an embedded system

In this work, new ideas in the realm of picture identification and classification are developed and implemented on hardware. This entails putting new algorithms into practice, whether for color, texture, or shape identification for AI (Artificial Intelligence) and picture recognition applications. We concentrate on identifying edible mushrooms in the harvesting and food manufacturing processes. Our proposal for an embedded system based on a Raspberry-Pi4 type microcomputer employing a combination of hardware and software components has helped with the recognition and classification of items in the image. Our object recognition system is built on a novel neighborhood topology and a cutting-edge kernel function that enables the effective embedding of image processing-related characteristics. We tested the suggested CNN-based object recognition system using a variety of challenging settings, including diverse fungus species, uncontrolled environments, and varying backdrop and illumination conditions. The outcomes were superior to various state-of-the-art outcomes. On the other hand, our contribution relating to the dynamic mode integrates a CNN network to accurately encode the temporal information with an attention mask allowing us to focus on the characteristics of an edible mushroom according to the state of the art, and guarantee the robustness of the recognition. We implemented our algorithm on a Raspberry Pi400-based embedded system connected to a CMOS camera-type image sensor plus an HMI human-machine interface for the instantaneous display of results for the rapid classification of edible and inedible mushrooms.