on-disease-dataset/data . The code is available at GitHub and Zenodo: - https://github.com/FrnazAkbar/Cotton-Lesion-Detection/tree/991640ddd25ad2fee85ee41f1bc92d1ea406a55b - FrnazAkbar. (2024). FrnazAkbar/Cotton-Lesion-Detection: Automated Lesion Detection in Cotton Leaf Visuals using Deep Learning: Code Release (v1.0). Zenodo. https://doi.org/10.5281/zenodo.13324708 . References Abdalla et al. (2024) Abdalla A, Wheeler TA, Dever J, Lin Z, Arce J, Guo W. Assessing fusarium oxysporum disease severity in cotton using unmanned aerial system images and a hybrid domain adaptation deep learning time series model. Biosystems Engineering. 2024;237:220–231. doi: 10.1016/j.biosystemseng.2023.12.014.
Open resource ↗Zenodo · 10.5281/zenodo.13324708 · lines:551-578Unverified paper record
Automated lesion detection in cotton leaf visuals using deep learning.
PeerJ. Computer science · 18 Oct 2024 · 10.7717/peerj-cs.2369
Abstract
Cotton is one of the major cash crop in the agriculture led economies across the world. Cotton leaf diseases affects its yield globally. Determining cotton lesions on leaves is difficult when the area is big and the size of lesions is varied. Automated cotton lesion detection is quite useful; however, it is challenging due to fewer disease class, limited size datasets, class imbalance problems, and need of comprehensive evaluation metrics. We propose a novel deep learning based method that augments the data using generative adversarial networks (GANs) to reduce the class imbalance issue and an ensemble-based method that combines the feature vector obtained from the three deep learning architectures including VGG16, Inception V3, and ResNet50. The proposed method offers a more precise, efficient and scalable method for automated detection of diseases of cotton crops. We have implemented the proposed method on publicly available dataset with seven disease and one health classes and have achieved highest accuracy of 95% and F-1 score of 98%. The proposed method performs better than existing state of the art methods.
Plant phenotyping relevance
綿花葉の病変・病害状態を画像から推定する深層学習手法の開発と評価が中心であり、植物病害フェノタイピングに該当する。
abstractWe propose a novel deep learning based method that augments the data using generative adversarial networks (GANs) to reduce the class imbalance issue and an ensemble-based method that combines the feature vector obtained from the three deep learning architectures including VGG16, Inception V3, and ResNet50.
abstractThe proposed method performs better than existing state of the art methods.
Code and data availability
The authors publicly released their analysis code (GitHub + Zenodo DOI) and used two publicly available Kaggle cotton leaf image datasets for their phenotyping/disease-detection analysis; all are paper-specific and actionable.
: The cotton plant disease data is available at Kaggle: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease/data , DOI: 10.34740/kaggle/dsv/5127834 . The Cotton Disease Dataset is available at Kaggle: https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset/data . The code is available at GitHub and Zenodo: - https://github.com/FrnazAkbar/Cotton-Lesion-Detection/tree/991640ddd25ad2fee85ee41f1bc92d1ea406a55b - FrnazAkbar. (2024). FrnazAkbar/Cotton-Lesion-Detection: Automated Lesion Detection in Cotton Leaf Visuals using Deep Learning: Code Release (v1.0). Zenodo. https://doi.org/10.5281/zenodo.13324708 . References Abdalla et al. (2024) Abdalla A, Wheeler TA, Dever J, Lin Z
Open resource ↗GitHub · FrnazAkbar/Cotton-Lesion-Detection · lines:551-578e dataset to conduct an analysis that involved the application of various deep learning models, namely Inception V3, ResNet50, VGG16, and a Transfer Learning approach. This led to the development of a comprehensive ensemble of pre-trained models through training procedures. Datasets used in this study are publicly available at: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease/data and https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset/data . By incorporating a diverse range of models, there is a potential to encompass a broader array of leaf attributes compared to relying solely on a singular paradigm. Inception V3, VGG 16 and ResNet 50 results are combined on the
Open resource ↗Kaggle · dhamur/cotton-plant-disease · lines:410-480ious deep learning models, namely Inception V3, ResNet50, VGG16, and a Transfer Learning approach. This led to the development of a comprehensive ensemble of pre-trained models through training procedures. Datasets used in this study are publicly available at: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease/data and https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset/data . By incorporating a diverse range of models, there is a potential to encompass a broader array of leaf attributes compared to relying solely on a singular paradigm. Inception V3, VGG 16 and ResNet 50 results are combined on the bases of voting in order to extract a wide range of leaf features fr
Open resource ↗Kaggle · janmejaybhoi/cotton-disease-dataset · lines:410-480This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.