A Lightweight RepViT-M1 Framework for Tea Leaf Disease Classification: Benchmarking Against VGG16 for Efficient Edge Deployment
Exact and timely identification of tea (Camellia sinensis) leaf diseases is necessary for controlling yield and quality. However, field deployment is limited due to the large computational requirement of the standard deep convolutional networks. This paper presents a lightweight, deployment-oriented framework for class...