2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 19 , Issue 2 , PP: 278-287, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Dalya Anwar 1 *
Doi: https://doi.org/10.54216/FPA.190220
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.
Plant Disease Detection , Convolutional Neural Networks (CNN) , Fruit Leaves , Deep Learning in Agriculture , Real-Time Detection
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