In the context of smart agriculture, effective monitoring of crop growth requires a 3D point cloud compression method that preserves detailed features of the primary plant structures while suppressing less relevant background information. However, achieving this balance is challenging due to the uniform treatment of spatial regions in conventional compression schemes. To address this need, we propose a resource-efficient importance-aware adaptive compression framework for point clouds. Specifically, after performing semantic segmentation on the raw point cloud, we quantify the semantic importance of each segmented region and map the importance scores to adaptive compression ratios. These ratios are then embedded into the latent representation during encoding and parsed during decoding to guide the reconstruction precision. This enables a more efficient allocation of bits to semantically critical regions. We validate the effectiveness of the proposed method on both our self-collected crop dataset and the publicly available Pheno4D dataset, demonstrating its feasibility and improved performance in balancing compression rate and reconstruction accuracy.