Grape Leaf Cultivar Identification in Complex Backgrounds with an Improved MobileNetV3-Small Model

Accurate identification of grape leaf varieties is an important prerequisite for effective viticulture management, contributing to breeding programs, cultivation strategies, and precision field operations. However, reliable recognition in complex field environments remains challenging. Subtle interclass morphological variations among leaves, background interference under natural conditions, and the need to balance recognition accuracy with computational efficiency for mobile applications represent key obstacles that limit practical deployment. This study proposes an improved lightweight convolutional neural network, termed ICS-MobileNetV3-Small (ICS-MS), specifically designed for grape leaf variety recognition. The model's core innovations, detailed in Key Innovations of the Proposed ICS-MS Model section, include three key components: First, a coordinate attention mechanism is embedded to enhance the network's ability to capture spatially distributed features while suppressing irrelevant background noise. Second, a multi-branch ICS-Inception structure is integrated to accomplish excellent multi-scale feature fusion, allowing the model to discern minute textural variations among types. Moreover, the feature representation is further optimized by adopting a joint loss function, which improves feature space distribution and enhances classification robustness. Experimental evaluations were conducted on a dataset comprising eleven grape leaf varieties. The proposed ICS-MS model achieves a recognition accuracy of 96.53% with only 1.17 M parameters. Experimental results demonstrate that, compared with the baseline MobileNetV3-Small model, the standalone integration of the Coordinate Attention (CA) mechanism improves accuracy by 0.17% while reducing the number of parameters by 10.4%. Furthermore, incorporating the ICS-Inception structure leads to an additional 4.78% accuracy improvement with only a marginal increase in parameter count. Finally, the introduction of a joint loss function provides an extra 0.23% gain in accuracy, resulting in an overall parameter reduction of approximately 23.5% compared with the baseline model. Three core contributions are highlighted as follows: (1) the construction of an integrated technical framework of "spatial feature enhancement-multi-scale fusion-feature distribution optimization" to systematically address the key issues of insufficient fine-grained feature extraction and the balance between lightweight design and accuracy; (2) the design of a lightweight CA-Block module that reduces parameters by 18.7% while enhancing spatial feature discrimination; (3) the achievement of superior performance with fewer parameters, providing a practical solution for mobile deployment in precision viticulture. Values for precision, recall, and F1-score were continuously near 96%, suggesting a good trade-off between efficiency and accuracy. These findings suggest that ICS-MS provides a practical and reliable approach for grape leaf identification and may serve as a useful tool to support intelligent management in precision viticulture.