Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks
[Uczenie maszynowe] Autorzy: Haimanot Bitew Alemayehu, Simachew Melaku Ayalew, Chilotie Desalew Mengesha i 3 więcej
Potato is an important food-security crop in Ethiopia, but its production is affected by foliar diseases, particularly early blight and late blight. This study develops a lightweight convolutional neural network (CNN) to classify potato leaves into three categories: healthy, early blight, and late blight. The proposed model was trained using 2,215 images from the PlantVillage dataset and evaluated on an independent field dataset containing 247 images collected from potato farms in Banja, Awi Zone, Ethiopia. On PlantVillage, the model achieved 99.04% training accuracy, 98.21% validation accuracy, and 98.66% test accuracy. On the field dataset, it achieved 90.28% accuracy. The study also compares the proposed CNN with VGG16 and MobileNetV2 in terms of classification performance and computational efficiency. The proposed model contains 446,787 trainable parameters, occupies approximately 1…
- Kategoria arxiv
- cs.LG
- Kategoria
- Uczenie maszynowe