models, which affirms the ability of the model to handle diverse data in the real world and validates its potential for reliable use in practical agricultural applications. Data Availability The data underlying the results presented in the study are available from the following sources: (1) Kaggle: Cotton Leaf Disease Dataset ( https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset ); (2) Mendeley Data: Cotton Plant Disease Dataset ( https://data.mendeley.com/datasets/6hm6pc3y43/2 ); (3) Roboflow: Cotton Plant Disease Prediction Dataset ( https://universe.roboflow.com/national-college-of-ireland/cotton-plant-disease-prediction-igthk/dataset/3 ); (4) Roboflow: Cotton Plant Di
Open resource ↗Kaggle · lines:545-557Unverified paper record
Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm.
PloS one · 27 May 2025 · 10.1371/journal.pone.0324293
Abstract
Cotton is a major cash crop, and increasing its production is extremely important worldwide, especially in agriculture-led economies. The crop is susceptible to various diseases, leading to decreased yields. In recent years, advancements in deep learning methods have enabled researchers to develop automated methods for detecting diseases in cotton crops. Such automation not only assists farmers in mitigating the effects of the disease but also conserves resources in terms of labor and fertilizer costs. However, accurate classification of multiple diseases simultaneously in cotton remains challenging due to multiple factors, including class imbalance, variation in disease symptoms, and the need for real-time detection, as most existing datasets are acquired under controlled conditions. This research proposes a novel method for addressing these challenges and accurately classifying seven classes, including six diseases and a healthy class. We address the class imbalance issue through synthetic data generation using conventional methods like scaling, rotating, transforming, shearing, and zooming and propose a customized StyleGAN for synthetic data generation. After preprocessing, we combine features extracted from MobileNet and VGG16 to create a comprehensive feature vector, passed to three classifiers: Long Short Term Memory Units, Support Vector Machines, and Random Forest. We propose a StackNet-based ensemble classifier that takes the output probabilities of these three classifiers and predicts the class label among six diseases-Bacterial blight, Curl virus, Fusarium wilt, Alternaria, Cercospora, Greymildew-and a healthy class. We trained and tested our method on publicly available datasets, achieving an average accuracy of 97%. Our robust method outperforms state-of-the-art techniques to identify the six diseases and the healthy class.
Plant phenotyping relevance
綿花の病害症状を画像から分類する深層学習手法の開発・評価が研究の中心であり、植物の病害状態を直接推定しているため。
abstractThis research proposes a novel method for addressing these challenges and accurately classifying seven classes, including six diseases and a healthy class.
abstractWe propose a StackNet-based ensemble classifier that takes the output probabilities of these three classifiers and predicts the class label among six diseases-Bacterial blight, Curl virus, Fusarium wilt, Alternaria, Cercospora, Greymildew-and a healthy class.
abstractWe trained and tested our method on publicly available datasets, achieving an average accuracy of 97%.
Code and data availability
The paper's plant-phenotyping inputs are public cotton leaf disease image datasets. The Data Availability statement names four public datasets (Kaggle Serosh Karim, Mendeley Cotton Plant Disease, and two Roboflow datasets), and the external validation section cites a fifth public Mendeley dataset. No author analysis/tr
ble use in practical agricultural applications. Data Availability The data underlying the results presented in the study are available from the following sources: (1) Kaggle: Cotton Leaf Disease Dataset ( https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset ); (2) Mendeley Data: Cotton Plant Disease Dataset ( https://data.mendeley.com/datasets/6hm6pc3y43/2 ); (3) Roboflow: Cotton Plant Disease Prediction Dataset ( https://universe.roboflow.com/national-college-of-ireland/cotton-plant-disease-prediction-igthk/dataset/3 ); (4) Roboflow: Cotton Plant Disease Dataset ( https://universe.roboflow.com/roboflow-100/cotton-plant-disease/dataset/2) . Funding Statement This work was
Open resource ↗Mendeley Data · lines:545-557in the study are available from the following sources: (1) Kaggle: Cotton Leaf Disease Dataset ( https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset ); (2) Mendeley Data: Cotton Plant Disease Dataset ( https://data.mendeley.com/datasets/6hm6pc3y43/2 ); (3) Roboflow: Cotton Plant Disease Prediction Dataset ( https://universe.roboflow.com/national-college-of-ireland/cotton-plant-disease-prediction-igthk/dataset/3 ); (4) Roboflow: Cotton Plant Disease Dataset ( https://universe.roboflow.com/roboflow-100/cotton-plant-disease/dataset/2) . Funding Statement This work was supported by KUCARS, Department of Mechanical and Nuclear Engineering, Khalifa University under Award number
Open resource ↗Roboflow · lines:545-557h healthy and diseased cotton leaves across different conditions, including Bacterial blight (250 images), Cotton curl virus (431 images), Herbicide growth damage (280 images), Leaf hopper Jassids (225 images), Leaf reddening (578 images), Leaf variegation (116 images), and Healthy leaf (257 images). The dataset is available at https://data.mendeley.com/datasets/b3jy2p6k8w/2 Each image captures critical disease-specific features such as leaf discoloration, curling, wilting, necrosis, and other symptomatic indicators. The dataset is particularly valuable as it includes images collected from real field environments during different growth stages of the cotton plant. These were taken under vary
Open resource ↗lines:417-499This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.