MIFFNet: A multidimensional image feature fusion network for lettuce fresh weight estimation

• We introduce an innovative multidimensional image feature fusion (MIFF) network specifically designed for lettuce fresh weight estimation. The core component of the proposed MIFF Module effectively integrates image features with RGB image data. • The prediction results across the three experimental datasets demonstrated optimal performance, achieving R² values of 0.929, 0.940, and 0.943, respectively.These results significantly outperform baseline methods and all other comparative approaches. • Extensive comparative experiments also demonstrate that the proposed network offers advantages in terms of low computational cost, and lightweight architecture. The estimation of lettuce fresh weight is critical for assessing growth status and optimizing cultivation. Traditional methods are often inefficient, error-prone, and costly. Computer vision offers opportunities for image-based non-destructive fresh weight estimation. This paper introduces MIFFNet, an end-to-end network integrating RGB images, visible light vegetation indices, geometric features, and color features for lettuce fresh weight estimation. This model employs the Inception structure as the multi-scale feature extraction (MSFE) block, alternating with the proposed multidimensional image feature fusion (MIFF) module to form the network backbone. This design enhances the model’s ability to capture multiscale features while thoroughly integrating multidimensional image features. Comparative experiments were conducted with 10 competitors, including classical convolutional neural networks, and existing lettuce fresh weight estimation models, across three lettuce datasets. Experimental results demonstrated that MIFFNet outperforms others across all three datasets. On the self-built dataset, it achieved an R 2 of 0.929, with RMSE and MAE values of 28.544g and 14.446g, respectively. On two public datasets, the R 2 values reached 0.94 and 0.943, with lower RMSE and MAE than competitors. Furthermore, MIFFNet exhibited significant advantages in terms of model complexity and parameter efficiency. These results highlight MIFFNet’s superior capability of accurate and efficient lettuce fresh weight estimation.