Agri-LLM: Prompt-Based Large Language Model for Emission Data Analytics in Smart Agriculture

Le Fang, Wei Xiang, Jiong Jin, Kewen Liao, Chang Liu, Yu Han, Flora D. Salim, Yi-Ping Phoebe Chen

Abstract: Massive emissions of greenhouse gases (GHGs) have a negative impact on the development of sustainable agriculture. While techniques of imputation and forecasting facilitate the observation of GHG emissions with improved accuracy, there is a lack of an integrated model for both GHG emission data imputation and forecasting, particularly in few-shot learning scenarios. To address this issue, this article proposes a pretrained large language model dubbed Agri-LLM for GHG emission data imputation and forecasting in smart agriculture. Notably, this model develops an information fusion embedding layer that fuses missing patterns, temporal irregularities and incomplete time series into multilevel patched tokens. A global temporal similarity informed prompting module is further elaborated on to generate suitable prompts for target time series, based on similar temporal characteristics captured from other nodes. Finally, the model aligns the pretrained knowledge language with multilevel integrated tokens directly without altering the large language model’s backbone. The experimental studies demonstrate that our model outperforms state-of-the-art (SOTA) baselines in both tasks of imputation and forecasting using full-sample training. Extensive experiments also confirm that the Agri-LLM exhibits superior performance in few-shot learning scenarios and the effectiveness of each proposed model component.

External IDs:dblp:journals/iotj/FangXJLLHSC25