The growing global population and the increasing demand for food have made food production a critical concern. To meet this challenge, techniques like vertical farming and aquaponics have been proposed to maximize output while conserving resources and space. However, there is still room for improvement in crop care processes. Deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as a leading approach for addressing various agricultural issues. This paper explores the use of Siamese CNNs to classify the front and back faces of six peach leaf varieties, a crucial step in detecting bacteriosis, a common disease in peach farming. Building on prior work in Siamese CNNs for peach leaf classification, this study examines several enhancements, including increased convolutional layers, three activation functions, and the application of pre-trained CNN models. The proposed architecture, combined with the ReLU activation function, improved the accuracy of the reference model by 3.48%. Among the pre-trained models, ResNet performed best, reaching an accuracy of 0.9841, which is 7.83% higher than the benchmark model’s top result. These findings indicate that the benchmark model had significant room for improvement, which was effectively addressed through the experiments conducted.