sigatoka and xanthomonas banana leaf disease detection via transfer learning

Plant diseases are a signi cant concern in agriculture, contributing to as much as 16% of global agricultural losses. this poses serious threats to food security, especially for crops like bananas, which are highly vulnerable to diseases such as xanthomonas wilt and sigatoka leaf spot. these diseases have the potential to cause complete yield losses, reaching up to 100%. addressing these challenges is crucial, and this study aims to do so by developing a robust disease detection model. leveraging convolutional neural network (cnn) algorithms, we have created a sophisticated system capable of accurately identifying and categorizing diseases in banana plants. to train our model e ectively, we have gathered a meticulously curated dataset of banana plant leaf images from regions heavily a ected by these diseases. this dataset has been carefully categorized into three groups: healthy, xanthomonas wilt infected, and sigatoka leaf spot infected. employing advanced techniques such as data augmentation and transfer learning, we have  netuned our model using various architectures including mobilenet, ecientnet, vgg16, vgg19, and inceptionv3. our research  ndings highlight the exceptional performance of the vgg16 model, achieving an impressive classi cation accuracy of 81.53% during rigoroustesting with independent datasets. looking to the future, we recognize the need for further improvements in model performance. this includes acquiring a more diverse and expansive dataset and implementing automatic hyperparameter selection methods.