| Title |
Performance Analysis of Deep Learning Based CNN Architectures for Stone Fruits Disease Detection |
| Authors |
(Manju Bagga) ; (Sonali Goyal) |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.2.227 |
| Keywords |
CNN; Deep learning; Disease detection; Stone fruits; Image classification |
| Abstract |
Producing crops is a major contribution of agriculture to the economy of every nation. Among the most important components of preserving a nation with sophisticated agricultural economy is the diagnosis of plant diseases. AI allows for the automatic identification of plant diseases from raw images through the use of DL-based CNN models. Therefore, the problem we have tackled is a multi-class classification problem that seeks to identify and categorize leaf diseases of stone fruits (mango, olive, and peach). To achieve this, we have used a dataset of 36,600 images of healthy and diseased leaves that were gathered from three distinct datasets: PlantVillage, MangoLeafBD, and GitHub. To identify diseases or their absence, four pre-trained CNN models?MobileNetV2, DenseNet201, InceptionV3, and ResNet50?have been trained. ResNet50 performs better than the other three models, with an outstanding accuracy of 93.11% on the images from the various datasets. |