Pre2Post-EcoLLM: Smart Multimodal Sensing for Pre- and Postharvest Fruit Quality and Economic Prediction

Fruit quality is jointly influenced by preharvest growing conditions, harvest status, and postharvest storage environments, whereas existing approaches generally focus on individual stages and therefore remain limited in simultaneously supporting cross-stage quality prediction, economic value assessment, and practical operational decision-making. To address the requirements of smart orchard management and postharvest quality control, Pre2Post-EcoLLM is proposed as a unified framework integrating fruit and canopy RGB images, environmental and soil sensing data, postharvest temperature and relative humidity, carbon dioxide and oxygen (CO2/O2), volatile organic compound (VOC) measurements, management records, and economic information. First, the Growth-to-Harvest Multimodal Representation module constructs a Harvest Quality State through growth-stage-aware cross-modal interaction. Subsequently, the Cross-Stage Quality–Economic Co-Evolution module continuously injects the harvest state into the postharvest temporal evolution process to jointly predict soluble solids content (SSC), firmness, weight loss, decay risk, and remaining shelf life, while further estimating commercial grade, saleable rate, economic loss, and net return. Finally, the Evidence-Grounded Agricultural Large Language Model (LLM) QA module combines structured model evidence, retrieval-augmented generation (RAG)-retrieved agricultural knowledge, and outputs from the Economic Calculator to provide traceable natural-language explanations for storage and selling decisions. Experimental results demonstrate that Pre2Post-EcoLLM achieved an overall normalized mean absolute error (MAE) of 0.247, a root mean square error (RMSE) of 0.394, a mean absolute percentage error (MAPE) of 7.29%, and an R2 of 0.916 for harvest and postharvest quality prediction, with disaggregated MAEs of 0.42 °Brix for SSC, 0.38kg/cm2 for firmness, and 0.31% for weight loss rate, while the decay-risk F1-score and area under the receiver operating characteristic curve (AUC) reached 0.907 and 0.954, respectively. The remaining shelf-life MAE was reduced to 1.68days, while the Economic MAE and Economic R2 reached 0.066(normalizedratio,equivalentto561CNY/ton) and 0.921, respectively. Storage–sale Decision Accuracy and Decision F1 reached 91.7% and 90.9%, respectively. For agricultural question answering, Answer Accuracy and Faithfulness reached 92.4% and 95.1%, respectively, while the Hallucination Rate was reduced to 4.9%. Ablation experiments further confirmed the effectiveness of growth-stage-aware fusion, cross-stage state injection, temporal quality evolution, economic coupling, and evidence-grounded question answering. These results indicate that the proposed framework can effectively connect preharvest sensing, postharvest quality evolution, and economic decision-making, providing a practical intelligent solution for lifecycle fruit quality assessment and smart storage management.