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

Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3678 2018 2018 A Novel CNN Model for Fruit Leaf Disease Detection: A Lightweight Solution for Grapes, Figs, and Oranges Department of Computer Science, Salahaddin University, Erbil, Iraq Dalya Dalya 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 2025 278 287 10.54216/FPA.190220 https://www.americaspg.com/articleinfo/3/show/3678