Intelligent prevention and control of crop diseases and pests is a critical link in safeguarding food security. However, agricultural practitioners often face fragmented information and low retrieval efficiency when seeking accurate, actionable knowledge. Furthermore, general-purpose large language models (LLMs) are prone to providing inaccurate or erroneous answers when applied to these specialized domains. To address these challenges, we assembled a large-scale corpus of knowledge on crop diseases and pests. Via entity and relation extraction, we constructed a multi-relational knowledge graph covering crops, diseases, pests, symptoms, and control measures. We subsequently designed Crop GraphRAG, a new framework that integrates knowledge graphs with retrieval-augmented generation (RAG). This system enables local knowledge-base question answering by retrieving adjacency subgraphs for relevant entities alongside summary-based passage retrieval. To evaluate performance, we curated a domain-specific test suite of question–answer pairs and conducted comparative and ablation experiments. Our experiments demonstrate that the Crop GraphRAG framework offers distinct advantages in answer accuracy and coverage compared to baselines. Crucially, the framework effectively suppresses hallucinated content, a common issue in generative models. These results verify the practical utility of the Crop GraphRAG framework for vertical-domain question answering. By mitigating the limitations of large language models in specialized agricultural contexts, this study provides a pragmatic tool for intelligent QA in the agricultural domain and advances the application of AI in crop protection.

The object of our research is to develop an ontology-based agricultural knowledge fusion method that can be used as a comprehensive basis on which to solve agricultural information inconsistencies, analyze data, and discover new knowledge. A recent survey has provided a detailed comparison of various fusion methods used with Deep Web data (Li, 2013). In this paper, we propose an effective agricultural ontology-based knowledge fusion method by leveraging recent advances in data fusion, such as the semantic web and big data technologies, that will enhance the identification and fusion of new and existing data sets to make big data analytics more possible. We provide a detailed fusion method that includes agricultural ontology building, fusion rule construction, an evaluation module, etc. Empirical results show that this knowledge fusion method is useful for knowledge discovery

In recent decades, shifts in the spatiotemporal patterns of precipitation and extreme temperatures have contributed to more frequent droughts. These changes impact not only agricultural production but also food security, ecological systems, and social stability. Advanced techniques such as machine learning and deep learning models outperform traditional models by improving meteorological drought prediction. Specifically, this study proposes a novel model named the multivariate feature aggregation-based temporal convolutional network for meteorological drought spatiotemporal prediction (STAT-LSTM). The method consists of three parts: a feature aggregation module, which aggregates multivariate features to extract initial features; a self-attention-temporal convolutional network (SA-TCN), which extracts time series features and uses the self-attention module’s weighting mechanism to automatically capture global dependencies in the sequential data; and a long short-term memory network (LSTM), which captures long-term dependencies. The performance of the STAT-LSTM model was assessed and compared via performance indicators (i.e., MAE, RMSE, and R\(^2\)). The results indicated that STAT-LSTM provided the most accurate SPEI prediction (MAE = 0.474, RMSE = 0.63, and R\(^2\) = 0.613 for SPEI-3; MAE = 0.356, RMSE = 0.468, and R\(^2\) = 0.748 for SPEI-6; MAE = 0.284, RMSE = 0.437, and R\(^2\) = 0.813 for SPEI-9; and MAE = 0.182, RMSE = 0.267, and R\(^2\) = 0.934 for SPEI-12).

The safety of animal products has always been the focus of people's attention, especially the frequent occurrence of food safety problems in recent years, which has caused a series of bad social impact, and the traceability system of the whole industry chain is a solution to the food safety problem. Based on the analysis of the problems existing in the application of traditional traceability system, we combine the advantages of blockchain and Internet of Things technology. This paper proposes a framework of livestock products traceability system based on blockchain and Internet of Things, and discusses the key technologies of system implementation to ensure the integrity, reliability and security of traceability information from the technical level. The system is an effective solution to enhance the reliability of traceability information and ensure the integrity of information.