cy needs to be improved by cascading various deep learning approaches with advanced fusion aware techniques. Data Availability The dataset utilized for this work was compiled from publicly available repository Kaggle as well as data extracted from published research articles. 1)Northan Leaf Blight 2)Common Rust 3)Grey Leaf Spot https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset 4)Eye spot 5)Goss’s bacterial wilt https://github.com/xtu502/maize-disease-identification 6)Downy mildew https://universe.roboflow.com/corn-leaf-disease/downy-mildew/dataset/1/download 7)Phaeosphaeria leaf spot https://www.kaggle.com/datasets/hamishcrazeai/maize-in-field-dataset Funding
Open resource ↗Kaggle · smaranjitghose/corn-or-maize-leaf-disease-dataset · lines:860-878Unverified paper record
Segmentation-based lightweight multi-class classification model for crop disease detection, classification, and severity assessment using DCNN
PLOS One · 14 May 2025 · 10.1371/journal.pone.0322705
Abstract
Leaf diseases in Zea mays crops have a significant impact on both the calibre and volume of maize yield, eventually impacting the market. Prior detection of the intensity of an infection would enable the efficient allocation of treatment resources and prevent the infection from spreading across the entire area. In this study, deep saliency map segmentation-based CNN is utilized for the detection, multi-class classification, and severity assessment of maize crop leaf diseases has been proposed. The proposed model involves seven different maize crop diseases such as Northern Leaf Blight Exserohilum turcicum , Eye Spot Oculimacula yallundae , Common Rust Puccinia sorghi , Goss’s Bacterial Wilt Clavibacter michiganensis subsp. nebraskensis , Downy Mildew Pseudoperonospora , Phaeosphaeria leaf spot Phaeosphaeria maydis , Gray Leaf Spot Cercospora zeae-maydis , and Healthy are selected from publicly available datasets obtained from PlantVillage. After the disease-affected regions are identified, the features are extracted by using the EffiecientNet-B7. To classify the maize infection, a hybrid harris hawks’ optimization (HHHO) is utilized for feature selection. Finally, from the optimized features obtained, classification and severity assessment are carried out with the help of Fuzzy SVM. Experimental analysis has been carried out to demonstrate the effectiveness of the proposed approach in detecting maize crop leaf diseases and assessing their severity. The proposed strategy was able to obtain an accuracy rate of around 99.47% on average. The work contributes to advancing automated disease diagnosis in agriculture, thereby supporting efforts for sustainable crop yield improvement and food security.
Plant phenotyping relevance
トウモロコシ葉の病変領域を画像から分割し、病害分類と感染重症度を推定する手法が研究の中心であり、植物の病害状態を直接評価しているため。
abstractIn this study, deep saliency map segmentation-based CNN is utilized for the detection, multi-class classification, and severity assessment of maize crop leaf diseases has been proposed.
abstractAfter the disease-affected regions are identified, the features are extracted by using the EffiecientNet-B7.
abstractExperimental analysis has been carried out to demonstrate the effectiveness of the proposed approach in detecting maize crop leaf diseases and assessing their severity.
Code and data availability
The paper's Data Availability statement lists four public image datasets used directly for its maize leaf disease classification and severity assessment experiments. No author analysis code or trained model checkpoints are disclosed.
ilability The dataset utilized for this work was compiled from publicly available repository Kaggle as well as data extracted from published research articles. 1)Northan Leaf Blight 2)Common Rust 3)Grey Leaf Spot https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset 4)Eye spot 5)Goss’s bacterial wilt https://github.com/xtu502/maize-disease-identification 6)Downy mildew https://universe.roboflow.com/corn-leaf-disease/downy-mildew/dataset/1/download 7)Phaeosphaeria leaf spot https://www.kaggle.com/datasets/hamishcrazeai/maize-in-field-dataset Funding Statement The author(s) received no specific funding for this work. References 1. United Nations, Department of Econo
Open resource ↗GitHub · xtu502/maize-disease-identification · lines:860-878y available repository Kaggle as well as data extracted from published research articles. 1)Northan Leaf Blight 2)Common Rust 3)Grey Leaf Spot https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset 4)Eye spot 5)Goss’s bacterial wilt https://github.com/xtu502/maize-disease-identification 6)Downy mildew https://universe.roboflow.com/corn-leaf-disease/downy-mildew/dataset/1/download 7)Phaeosphaeria leaf spot https://www.kaggle.com/datasets/hamishcrazeai/maize-in-field-dataset Funding Statement The author(s) received no specific funding for this work. References 1. United Nations, Department of Economic and Social Affairs, Population Division. World population prospect
Open resource ↗corn-leaf-disease/downy-mildew · lines:860-878This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.