Resiplant-5: a Cnn Model for Disease Detection in Citrus Fruits and Leaves

This paper introduces ResiPlant-5, a 5-layer convolutional neural network (CNN) designed for precise plant disease diagnosis. Sequential CNN models suffer from vanishing gradient problems. To overcome vanishing gradients and improve 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, which contains images of orange fruit. The results indicate that the proposed model demonstrates an approximate increase in accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2 \%, 6 \%$</tex>, and 8 % on the sweet orange, citrus leaves, and citrus fruit datasets, respectively, compared to the VGG16, VGG19, and ResNet50 models.