arXiv：cs.LG· Haimanot Bitew Alemayehu, Simachew Melaku Ayalew, Chilotie Desalew Mengesha, Ayisheshim Almaw Bogale, Anteneh Birhan Yenew, Abrham Tegegne Bogale·· 4 小时前
轻量级 CNN 用于马铃薯叶片病害检测，田间数据集准确率达 90.28%
Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks
AI 导读
一项研究开发了轻量级 CNN，将马铃薯叶片分为健康、早疫病和晚疫病三类。模型在 PlantVillage 数据集上训练，测试准确率 98.66%；在埃塞俄比亚 Banja 采集的 247 张独立田间图像上准确率为 90.28%。该模型含 446,787 个可训练参数，占用约 1.70 MB，每轮训练平均 5.58 秒，优于 VGG16 的 9.96 秒和 MobileNetV2 的 7.68 秒。
正文
Abstract: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 1.70 MB, and requires an average of 5.58 seconds per training epoch in the reported experimental setup, compared with 9.96 seconds for VGG16 and 7.68 seconds for MobileNetV2. The results demonstrate the potential of a compact CNN for potato leaf disease classification and highlight the importance of independent field evaluation, where image conditions are more variable than in controlled benchmark datasets. The proposed approach may support preliminary disease identification for smallholder farmers in Ethiopia and similar agricultural settings.
| Comments: | 9 pages, 4 figures, 4 tables. The study presents a lightweight CNN for three-class potato leaf disease classification and evaluates it on PlantVillage and an independent field dataset collected in Banja, Awi Zone, Ethiopia |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.20544 [cs.LG] |
| (or arXiv:2610.20544v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20544 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Haimanot Bitew Alemayehu [view email]
[v1]
Wed, 14 Oct 2026 17:36:33 UTC (279 KB)
来源：arXiv：cs.LG · arxiv.org