An accurate and rapid analysis of massive data can be one of the most important steps for the comprehensive utilization of agricultural big data, particularly with the advent of the era of big data. Among them, the knowledge graph can be expected to represent complex domain knowledge using data mining, information processing, knowledge statistics, and graph drawing. The dynamic development of knowledge can also be revealed to provide a practical and valuable reference for complicated research. Therefore, the knowledge graph has attracted much attention in recent years, due mainly to the heterogeneous semantic network. Efficient management can be achieved in the things and their relationships in the real world. The efficient capability of information retrieval can be attributed to the storage structure of the knowledge graph, namely the directed graph. The knowledge graph can be applied to intuitively display the complex data under the background of the increasing data volume and complex structure in the agricultural field. Agricultural big data can be systematically analyzed for the high utilization of data value, and the mining of agricultural data rules, in order to promote the development of smart agriculture. The key technology of knowledge graph construction can dominate the knowledge graph research in the agricultural field. The agricultural knowledge graph should follow the general technology specification of knowledge graph construction. This review aims to explore the theoretical support of knowledge graphs in agricultural application. Firstly, the construction models of the knowledge graph were divided into three types: top-down, bottom-up, top-down and bottom-up combination. Among them, the top-down and bottom-up combination of construction model was the most commonly used with the more completed and flexible structure suitable for the knowledge graph construction in specific fields. Secondly, the key technologies of agricultural knowledge graph construction were summarized from five aspects: ontology construction, knowledge extraction, knowledge fusion, knowledge reasoning, knowledge graph storage and visualization. The progress of each aspect was then compared, including the technical difficulties, technological evolution, innovation and application exploration. It was found that Protégé tools and semi-automatic construction were widely adopted to construct the knowledge graph in the agricultural field. Furthermore, one of the most concerned research hotspots was knowledge extraction as the premise of construction. The best performance was obtained in the BERT-BiLSTM-CRF among the various knowledge extraction. The difficulty of knowledge graph construction was focused mainly on the terms recognition among agricultural subfields. Particularly, there was the great influence of the natural environment and climate on agricultural knowledge. The application research was also reviewed, including agricultural thematic literature metrology research, agricultural knowledge question and answer, agricultural information resources recommendation, and agricultural information retrieval, as the knowledge graph was gradually applied to the agricultural field. The ontology construction, knowledge extraction, knowledge graph storage, and visualization technologies were commonly used in the above application scenarios, but knowledge fusion and knowledge reasoning were rarely used, indicating the nonstandard knowledge graph construction under specific application backgrounds. Therefore, the agricultural knowledge graph should pay more attention to the cutting-edge construction technologies in the future, in order to innovate in combination with the characteristics of agricultural data. Finally, the future research trends of the knowledge graph can be expected to serve as the e-commerce recommendation for agricultural products. Two research directions were then proposed in the dynamic updating on the construction and correlation of knowledge graphs across the domain.

Climate has posed serious impacts on the growth, development, and quality formation of wine grapes, particularly in conventional viticulture. Meteorological factors—including temperature, solar radiation, and precipitation—play a pivotal role in the physiological and metabolic processes of grape berries. There are also direct influences on the accumulation of the key secondary metabolites, such as flavonoids and aroma compounds, leading to the wine flavor, aroma, and overall quality. It is often required for accurate and reliable predictive models to clarify the relationships between meteorological parameters and grape metabolic responses against global warming and an increasing frequency of extreme weather events. Adaptive cultivation can be expected to advance precision viticulture. Existing prediction models are also limited to the hyperparameter sensitivity, generalization, and proneness to local optima. In this study, a domain-specific dataset was constructed with the meteorological indicators and metabolomic profiles of four wine grape varieties over multiple developmental stages. A forecasting framework (named IDBO-XGBoost) was also proposed to synergistically combine an improved dung beetle optimizer (IDBO) with the eXtreme gradient boosting (XGBoost) algorithm. Among them, the IDBO algorithm incorporated two enhancements: an osprey global exploration to strengthen the population diversity for less premature convergence, and another adaptive t-distribution mutation operator to balance global and local exploitation during the iterative process. The algorithm significantly improved the optimization efficiency, convergence speed, and solution quality. Extensive validation experiments were performed on nine benchmark test functions. The IDBO outperformed the standard Dung Beetle Optimizer in terms of precision and stability after optimization. Once applied to predict the accumulation of 12 key metabolite groups in wine grapes—including flavonols, flavanols, free and bound forms of terpenoids, norisoprenoids, and carbonyl compounds—the IDBO-XGBoost model demonstrated the superior predictive performance over all datasets. The better performance was achieved with an average increase of 8.5% in the coefficient of determination (R²), along with the average reductions of 9.4%, 7.7%, and 12.1% in the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), respectively, compared with the baseline XGBoost model. The prediction accuracy and robustness were significantly enhanced. Furthermore, the contribution rate of each meteorological feature was quantified to explore the underlying mechanisms using SHAP (Shapley Additive exPlanations) interpretability analysis. The predictions were obtained for the climatic variables' influence on the metabolic outputs. For instance, the moderate temperatures (DT_20-25) were positively correlated with the flavonol accumulation, whereas the high temperatures (DT₄₀) exhibited an inhibitory effect. Solar duration and effective accumulated temperature shared the divergent effects on the free and bound terpenoids, indicating the enzyme-mediated metabolic shifts. Additionally, the variety-specific effects were also observed on some influencing factors, such as the precipitation and temperature ranges, indicating the genetic dependency of the environmental responses. As such, an intelligent computational framework was provided to accurately predict the wine grape metabolic traits under varying climatic conditions. The ecophysiological mechanisms were determined to govern the grape quality. The interpretable machine learning can then bridge the gap between data-driven modeling and biological properties in the decision-making on the vineyard cultivation. The findings can hold substantial practical significance to mitigate the impacts of climate under grape cultivation, in order to enhance the resource use efficiency in the sustainable wine industry.