Classification of Lettuce Leaf Variants Using Transfer Learning

The classification of leaves is important for both plant identification and agricultural practices. In this study, we suggest a deep learning-based method for classifying different lettuce leaf varieties. The work uses pre-trained weights from ImageN et and focuses on two well-known deep learning architectures, VGG16 and Inception V3. For model training and evaluation, a dataset made up of several lettuce leaf variations was used. The performance of the model is evaluated using 10-cross-validation testing. The experiments showed that the Inception V3 model outperformed VGG16 with an accuracy of 98.0 percent, achieving the maximum accuracy of 98.0 percent. The collected results show that deep learning approaches can reliably categorize different lettuce leaf varieties, offering useful insights for managing lettuce crops.