Enhancing Disease Severity Classification: Unveiling the Complexity of Sugar Maple Tar Spot with CNN-GAN Framework

Sugar maple tar spot is caused by the water mold Rhytisma acerinum and is one of the unique systemic leaf pathogens affecting sugar maple (Acer saccharum) in North America and Europe. The exact tar spot of the sugar maple class fraction is paramount to developing effective control practices. As our task, we suggest a new Convolutional Neural Networks (CNN)- Generative Adversarial Networks (GAN)-based framework for increasing the accuracy of severity classification through a convolutional neural network-generative adversarial network architecture. The CNN-GAN infrastructure blends machine intelligence methods to make the process of automation and increase disease classification accuracy. We carried out the multistep process of collecting a comprehensive dataset of sugar maple leaf images representing different levels of degree of tar spot and finally trained the CNN-GAN network using a systematic methodology. Experiments conducted indicate successful implementation of the algorithm based on the CNN-GAN model as the network system has yielded the accuracies of classification of sugar maple tar spot severity being 95.62%. The tabular results with details and analysis as well as an indication of the model's performance among patients with different severity levels of this psychiatric disorder. This aspect expands the field of automated disease detection and management in agriculture and forestry which can be used in earlier screening and suitable interventions. The proposed method of a CNN-GAN framework entails great promise as it enhances the precision in diagnoses and instead provides more efficient disease-fighting strategies such as sugar maple tar sport and other plant diseases.