This paper introduces ResiPlant-5, a 5-layer con-
volutional neural network (CNN) designed for precise plant
disease diagnosis. Sequential CNN model suffer form vanishing
gradient problem. To overcome vanishing gradients and im-
prove deep model learning, the design uses skip connections,
inspired by Residual Networks (ResNets). Skip connections
provide connections between non-adjacent layers, improving
gradient propagation and feature retention. This approach lets
the model maintain important properties from previous layers
while training deeper networks without sacrificing speed. Using
deep learning and residual connections, ResiPlant-5 successfully
addresses difficult image classification challenges, hence making
it feasible to identify the disease in the plants. The model has been
trained and tested using two publicly available datasets. The first
dataset is the citrus dataset, which contains images of citrus leaves
and citrus fruit. The second dataset is the sweet orange dataset.
The results indicate that the proposed model demonstrates an
approximate increase in accuracy of 2%, 6%, and 8% on the
Sweet Orange, citrus leaves, and citrus fruit datasets, respectively,
compared to the VGG16, VGG19, and ResNet50 models