How Deep Feature Embeddings and Euclidean Similarity Power Automatic Plant Leaf Recognition

Automatic plant leaf recognition is achieved by applying deep learning to extract numerical representations, known as embeddings, from leaf images. A pre-trained ResNet-50 convolutional neural network converts each image into a 2048-dimensional embedding vector that captures key features like shape, texture, and vein patterns. These high-dimensional vectors are then compared using Euclidean distance to measure similarity, enabling classification by finding the nearest neighbor in a database of known species. The entire pipeline, from image preprocessing to embedding extraction and matching, is demonstrated using the UCI One-Hundred Plant Species Leaves Dataset.