cle, and approved the final draft. Asuman Günay Yılmaz conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the article, and approved the final draft. Data Availability The following information was supplied regarding data availability: The data and code are available at GitHub and Zenodo: - https://github.com/ozgenurr/GAPNet.git .
Open resource ↗ozgenurr/GAPNet · lines:727-762Unverified paper record
GAPNet: Single and multiplant leaf disease classification method based on simplified SqueezeNet for grape, apple and potato plants.
PeerJ. Computer science · 16 Jun 2025 · 10.7717/peerj-cs.2941
Abstract
Humans need food to sustain their lives. Therefore, agriculture is one of the most important issues in nations. Agriculture also plays a major role in the economic development of countries by increasing economic income. Early diagnosis of plant diseases is crucial for agricultural productivity and continuity. Early disease detection directly impacts the quality and quantity of crops. For this reason, many studies have been carried out on plant leaf disease classification. In this study, a simple and effective leaf disease classification method was developed. Disease classification was performed using seven state-of-the-art pretrained convolutional neural network architectures: VGG16, ResNet50, SqueezeNet, Xception, ShuffleNet, DenseNet121 and MobileNetV2. A simplified SqueezeNet model, GAPNet, was subsequently proposed for grape, apple and potato leaf disease classification. GAPNet was designed to be a lightweight and fast model with 337.872 parameters. To address the data imbalance between classes, oversampling was carried out using the synthetic minority oversampling technique. The proposed model achieves accuracy rates of 99.72%, 99.53%, and 99.83% for grape, apple and potato leaf disease classification, respectively. A success rate of 99.64% was achieved in multiplant leaf disease classification when the grape, apple and potato datasets were combined. Compared with the state-of-the-art methods, the lightweight GAPNet model produces promising results for various plant species.
Plant phenotyping relevance
植物葉の病害状態を画像から分類する軽量CNN手法を開発・評価しており、病害表現型の抽出が研究の中心です。
abstractIn this study, a simple and effective leaf disease classification method was developed.
abstractA simplified SqueezeNet model, GAPNet, was subsequently proposed for grape, apple and potato leaf disease classification.
Code and data availability
Authors publicly release GAPNet implementation code via GitHub and Zenodo, and the paper's leaf image datasets (PlantVillage, New Plant Disease, Plant Pathology 2020) are publicly available at listed URLs.
- Ozge Ozaras. (2025). ozgenurr/GAPNet: GAPNET (GAPNET). Zenodo. https://doi.org/10.5281/zenodo.15163686 . The datasets are publicly available at: - Plant Village Dataset: https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color . - New Plant disease Dataset: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data . - Plant Pathology: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data . References Babalola, Kpai & Toygar (2023) Babalola FO, Kpai NI, Toygar Ö. Deep learning-based classification of apple leaf diseases u
Open resource ↗PlantVillage-Dataset · lines:763-789- Ozge Ozaras. (2025). ozgenurr/GAPNet: GAPNET (GAPNET). Zenodo. https://doi.org/10.5281/zenodo.15163686 . The datasets are publicly available at: - Plant Village Dataset: https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color . - New Plant disease Dataset: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data . - Plant Pathology: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data . References Babalola, Kpai & Toygar (2023) Babalola FO, Kpai NI, Toygar Ö. Deep learning-based classification of apple leaf diseases using AlexNet. Computer Science, IDAP-2023: International Artificial Intelligence and Data Processing Symp
Open resource ↗lines:763-789GAPNET (GAPNET). Zenodo. https://doi.org/10.5281/zenodo.15163686 . The datasets are publicly available at: - Plant Village Dataset: https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color . - New Plant disease Dataset: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data . - Plant Pathology: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data . References Babalola, Kpai & Toygar (2023) Babalola FO, Kpai NI, Toygar Ö. Deep learning-based classification of apple leaf diseases using AlexNet. Computer Science, IDAP-2023: International Artificial Intelligence and Data Processing Symposium (IDAP-2023) 2023:67–74. doi: 10.53070/bbd.1349566. Banja
Open resource ↗lines:763-789This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.