Fine-grained plant disease classification requires separating classes of the same crop that often differ only in small, sparse lesion patterns. Looped Vision Transformers (LoopViTs) obtain depth by applying a small stack of weight-tied blocks repeatedly, yet each block rebuilds its attention graph from scratch at every iteration, although the same block has just computed a graph over the same tokens. We propose LoGA, a looped vision transformer with Loop-Carried Graph Attention, in which the attention graph of each block at iteration $t$ is biased by the graph the same block produced at iteration $t-1$, used as a tempered prior. In its log-prob form the update $A_t \propto \exp(QK^\top/\sqrt... More >