Training Strategy Optimization of a Tea Canopy Dataset for Variety Identification During the Harvest Period

Accurate identification of tea plant varieties during the harvest period is a critical prerequisite for developing intelligent multi-variety tea harvesting systems. Different tea varieties exhibit distinct chemical compositions and require specialized processing methods, making varietal purity a key factor in ensuring product quality. However, achieving reliable classification under real-world field conditions is challenging due to variable illumination, complex backgrounds, and subtle phenotypic differences among varieties. To address these challenges, this study constructed a diverse canopy image dataset and systematically evaluated 14 convolutional neural network models through transfer learning. The best-performing model was chosen as a baseline, and a comprehensive optimization of the training strategy was conducted. Experimental analysis demonstrated that the combination of Adamax optimizer, input size of 608 × 608, training and validation sets split ratio of 80:20, learning rate of 0.0001, batch size of 8, and 20 epochs produced the most stable and accurate results. The final optimized model achieved an accuracy of 99.32%, representing a 2.20% improvement over the baseline. This study demonstrates the feasibility of highly accurate tea variety identification from canopy imagery but also provides a transferable deep learning framework and optimized training pipeline for intelligent tea harvesting applications.