t to disclose. Acknowledgment: The authors are sincerely grateful to their institutions for their continued support and trust, which greatly contributed to the completion of this research. Data availability The dataset used and analyzed during the current study, “New Plant Diseases Dataset”, is publicly available on Kaggle at: (https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset?select=New+Plant+Diseases+Dataset%28Augmented%29) References [1] P. Arputharaj and K. Karunanithy, "A review on machine learning and deep learning techniques for plant leaf disease detection and classification with IoT in agriculture industry," Journal of Industrial Information Integration, vol. 50,
Open resource ↗Kaggle · vipoooool/new-plant-diseases-dataset · pdf-raw-page:11 lines:1-49Paper record
Leaf-by-Leaf Diagnosis: A Custom CNN with Pyramidal Feature Extraction for Plant Disease Classification
EDRAAK · 10 Jul 2026 · 10.70470/edraak/2026/007
Abstract
Plant diseases threaten global agriculture, causing 20–40% yield losses and food insecurity. Current diagnostic methods are costly and lack scalability. While deep learning advances plant disease detection, there remains a need for CNNs with simpler architectures, better generalizability, and lower computational cost. This study presents a novel CNN for multi-class classification of 38 diseases. Trained on a public dataset of over 87,000 RGB images, the architecture comprises five convolutional blocks (filters 32–512) with max pooling and dropout (0.25, 0.4), followed by a 1,500-unit dense layer and SoftMax output. Optimized with Adam (lr=0.0001) and categorical cross-entropy, the model achieved 98% training and 96% validation accuracy with approximately 28.7 million parameters significantly fewer than transfer learning architectures. These results demonstrate an effective balance between predictive performance and computational efficiency, positioning the model as a promising tool for real-world agricultural deployment.
Code and data availability
The paper's sole qualifying asset is the plant disease image dataset used for all its CNN training/validation measurements: the publicly available New Plant Diseases Dataset (Augmented) on Kaggle, explicitly declared in the Data availability statement. No author code, trained model checkpoints, or other paper-specific
t to disclose. Acknowledgment: The authors are sincerely grateful to their institutions for their continued support and trust, which greatly contributed to the completion of this research. Data availability The dataset used and analyzed during the current study, “New Plant Diseases Dataset”, is publicly available on Kaggle at: (https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset?select=New+Plant+Diseases+Dataset%28Augmented%29) References [1] P. Arputharaj and K. Karunanithy, "A review on machine learning and deep learning techniques for plant leaf disease detection and classification with IoT in agriculture industry," Journal of Industrial Information Integration, vol. 50,
Open resource ↗Kaggle · vipoooool/new-plant-diseases-dataset · pdf-raw-page:11 lines:1-49