The coconut plant, a vital source of food, oil, and fiber in tropical and subtropical regions, faces significant challenges from various diseases that diminish both productivity and quality. Addressing this, a research was undertaken to develop a neural network model capable of accurately classifying coconut plant diseases based on leaf images. With a dataset of 7100 coconut plant leaf photos across nine disease categories, including bud rot, caterpillar attack, dry leaves, whitefly attack, flaccidity, gray spots, leaflets, rhinoceros attack, and yellowing. The model achieved an impressive accuracy of 98.81%. This research underscores the potential of CNN models as effective tools for aiding farmers in disease diagnosis and management, ultimately enhancing coconut yield and quality.