Abstract: Accurate canopy area temperature prediction played a crucial role in intelligent agricultural production and climate regulation. As a key environmental factor, canopy temperature significantly influenced crop growth, physiological metabolism, and the interaction between plants and pathogens, ultimately affecting crop health, yield, and quality. However, traditional temperature forecasting methods struggled to capture multi-scale dependencies in long and short time sequences, leading to suboptimal predictive performance. To address these challenges, a fusion model based on the Informer architecture and long short-term memory neural networks (LSTM) was proposed for canopy temperature prediction by integrating multi-source environmental data. In the encoder layer, a sparse attention mechanism was employed to extract multi-scale information from the input features. This mechanism was particularly suited for long-term time series data, as it effectively captured the coupling relationships between environmental factors at different time scales. Sparse attention allowed the model to focus on the most relevant information, which was crucial for long-term temperature prediction, as it reduced noise and redundancy commonly present in traditional models. In the decoder layer, LSTM was used to extract short-term temporal dependencies, further enhancing the continuity and consistency within the time series data. The ability of LSTM to capture sequential information over short intervals enabled the model maintain prediction consistency by leveraging temporal patterns. This contributed to more reliable predictions, especially in dynamic environments where short-term fluctuations in temperature played a key role. Additionally, an improved residual feedforward network (IRFFN) was incorporated to further optimize the model structure. This module improved information flow through the network by allowing residual connections, alleviating the vanishing gradient problem often encountered during training. By enhancing the model's ability to learn complex non-linear relationships, IRFFN enabled improve prediction performance without significantly increasing computational complexity. The experiment was conducted in a kiwifruit-grape intercropping orchard, where a suspended inspection robot was deployed to collect canopy microenvironmental data from January 2021 to 2024, with measurements recorded at 30-minute intervals. Data preprocessing was performed using the isolation forest algorithm to eliminate outliers, followed by normalization to standardize feature values. A Spearman correlation analysis was conducted to identify key environmental variables affecting canopy temperature, such as canopy temperature, humidity, soil temperature, wind speed, atmospheric pressure, and soil moisture, which were selected as input features for the model. Experimental results demonstrated that the proposed Informer-LSTM model significantly outperformed mainstream models, including Times-Net, Autoformer, Informer, and Reformer, in terms of prediction accuracy. The model achieved a mean absolute error (MAE) of 0.166 ℃, a root mean square error (RMSE) of 0.224 ℃, and a coefficient of determination (R²) of 0.978. Compared to the baseline Informer model, the MAE and RMSE were reduced by 0.265 and 0.448 ℃, respectively. The stability of the model was further verified by testing predictions at different time steps (1, 6, 12hours). In the task of predicting the future one hour, the MAE and RMSE were 0.468 and 0.583 ℃, respectively, while in the task of predicting the future 12 hours, the MAE and RMSE increased to 1.368 and 1.527 ℃, respectively, and the average MAE and RMSE values were 31.75% and 29.62% lower than the baseline model Informer. The final validation experiment was conducted using actual canopy microenvironmental data from February 2024, confirming the model’s generalization capability and robustness, with an RMSE of 0.259 ℃, MAE of 0.178 ℃, and R² of 0.969. This study demonstrated that Informer-LSTM provided a high-precision and stable approach for short- and medium-term canopy temperature forecasting.