A Novel CNN Model for Fruit Leaf Disease Detection: A Lightweight Solution for Grapes, Figs, and Oranges

aspg-3-3678-1791417744 20261008000224 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 19 2 A Novel CNN Model for Fruit Leaf Disease Detection: A Lightweight Solution for Grapes, Figs, and Oranges Dalya Anwar Department of Computer Science, Salahaddin University, Erbil, Iraq Plant diseases are considered a real threat to food security due to the losses incurred by individuals and countries. Early detection is one of the real solutions that can help reduce the size of these losses, but early detection is still bleeding. This study presents the development of a Convolutional Neural Network (CNN) model for classification with a new architecture and optimal performance suitable for real-time applications for the detection of fruit diseases (figs, oranges, grapes). The developed CNN model balanced accuracy and FLOPs using Squeeze-Excitation (SE) and adaptive-average pool layers. After implementing new data developed from Iraqi farms, the CNN model achieved optimal performance compared to the most famous models such as VGG16, ResNet, EfficientNet, and AlexNet. 2025 278 287 3678 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.190220 https://www.americaspg.com/journal/3/article/3678 https://www.americaspg.com/storage/41742379094.pdf