Deep learning tool sets benchmark for accurate rice panicle counting across growth stages

By integrating large-kernel convolutional blocks and a novel loss function, LKNet effectively addresses challenges such as overlapping targets, annotation bias, and variability in panicle structure across growth stages. Tested on UAV imagery and multiple crop datasets, the model demonstrates superior performance and robustness, offering a high-throughput solution for precision agriculture and crop.