Banana crop is highly susceptible to Fusarium wilt, a disease that can cause significant agricultural losses if not detected early. This study aimed to develop a classification model to detect Fusarium-infected banana plants using multispectral imagery. The dataset consisted of labeled images categorized into two classes: healthy and diseased (Fusarium). A convolutional neural network (CNN) was trained and evaluated, achieving an overall test accuracy of 81.25%. Class-wise evaluation showed a precision of 70%, recall of 100%, and F1-score of 82% for healthy plants, while the diseased class reached 100% precision, 67% recall, and 80% of F1-score. These results indicate strong performance in precision but highlight a need to improve recall for effective disease monitoring. Comparisons with recent studies show that higher accuracy can be achieved through larger datasets, data augmentation, and transfer learning. This research demonstrates the potential of using multispectral images and deep learning for banana disease detection, with future improvements focused on expanding data volume and applying advanced training techniques to boost recall and overall robustness.