@article{
 title = {Disease Detection in Grape Cultivation Using Strategically Placed Cameras and Machine Learning Algorithms with a Focus on Powdery Mildew and Blotches},
 type = {article},
 year = {2024},
 keywords = {Accuracy,Classification algorithms,Convolutional neural networks,Deep learning,Disease detection,Feature extraction,Grape cultivation,Image processing,Machine learning,Pipelines,Plant diseases},
 publisher = {Institute of Electrical and Electronics Engineers Inc.},
 id = {9624bf6b-36a8-308a-8ffb-edf76e788de9},
 created = {2024-09-16T07:16:13.077Z},
 accessed = {2024-09-16},
 file_attached = {true},
 profile_id = {f1f70cad-e32d-3de2-a3c0-be1736cb88be},
 group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
 last_modified = {2024-09-16T07:16:46.254Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 folder_uuids = {4dd63b9a-2e78-4e16-b45d-b692d8e0c5c3},
 private_publication = {false},
 abstract = {Grape cultivation faces various challenges, such as pests, management, fertilizer quality, and diseases caused by bacteria, fungi, and viruses. Notably, powdery mildew and blotches are significant diseases with different features, necessitating an accurate detection system to minimize crop losses. While traditional methods involve capturing images of diseased leaves, this research proposes a smart approach using deep learning and machine learning algorithms to analyze images taken by strategically placed cameras on farms. The research aims to design a system capable of detecting diseases that can provide information relevant to decisions, alert farmers, and allow authorized actions. Employing artificial intelligence algorithms such as support vector machines (SVM), convolutional neural networks (CNN), decision trees (DT), Naive Bayes (NB), and random forest (RF), the proposed model classifies datasets of powdery mildew, blotches, and healthy leaves when using augmented and histogram-oriented gradient (HOG) preprocessing. Following the classification of affected and healthy leaves, a stacking algorithm is used to select the optimal algorithm that provides the highest level of accuracy. The experimental results and analysis reveal that the CNN classifier outperforms others, achieving an accuracy of 96.1%. When transfer learning and fine tuning are applied to the CNN-based model, the accuracy of the model increases by 1.4% and 3.1%, respectively. SVM classification also provides a suitable level of accuracy of 96.0% for HOG augmented data.},
 bibtype = {article},
 author = {Khan, Kashif Hesham and Aljaedi, Amer and Ishtiaq, Muhammad Shakeel and Imam, Hassan and Bassfar, Zaid and Jamal, Sajjad Shaukat},
 doi = {10.1109/ACCESS.2024.3430190},
 journal = {IEEE Access}
}

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Notably, powdery mildew and blotches are significant diseases with different features, necessitating an accurate detection system to minimize crop losses. While traditional methods involve capturing images of diseased leaves, this research proposes a smart approach using deep learning and machine learning algorithms to analyze images taken by strategically placed cameras on farms. The research aims to design a system capable of detecting diseases that can provide information relevant to decisions, alert farmers, and allow authorized actions. Employing artificial intelligence algorithms such as support vector machines (SVM), convolutional neural networks (CNN), decision trees (DT), Naive Bayes (NB), and random forest (RF), the proposed model classifies datasets of powdery mildew, blotches, and healthy leaves when using augmented and histogram-oriented gradient (HOG) preprocessing. Following the classification of affected and healthy leaves, a stacking algorithm is used to select the optimal algorithm that provides the highest level of accuracy. The experimental results and analysis reveal that the CNN classifier outperforms others, achieving an accuracy of 96.1%. When transfer learning and fine tuning are applied to the CNN-based model, the accuracy of the model increases by 1.4% and 3.1%, respectively. SVM classification also provides a suitable level of accuracy of 96.0% for HOG augmented data.","bibtype":"article","author":"Khan, Kashif Hesham and Aljaedi, Amer and Ishtiaq, Muhammad Shakeel and Imam, Hassan and Bassfar, Zaid and Jamal, Sajjad Shaukat","doi":"10.1109/ACCESS.2024.3430190","journal":"IEEE Access","bibtex":"@article{\n title = {Disease Detection in Grape Cultivation Using Strategically Placed Cameras and Machine Learning Algorithms with a Focus on Powdery Mildew and Blotches},\n type = {article},\n year = {2024},\n keywords = {Accuracy,Classification algorithms,Convolutional neural networks,Deep learning,Disease detection,Feature extraction,Grape cultivation,Image processing,Machine learning,Pipelines,Plant diseases},\n publisher = {Institute of Electrical and Electronics Engineers Inc.},\n id = {9624bf6b-36a8-308a-8ffb-edf76e788de9},\n created = {2024-09-16T07:16:13.077Z},\n accessed = {2024-09-16},\n file_attached = {true},\n profile_id = {f1f70cad-e32d-3de2-a3c0-be1736cb88be},\n group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},\n last_modified = {2024-09-16T07:16:46.254Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n folder_uuids = {4dd63b9a-2e78-4e16-b45d-b692d8e0c5c3},\n private_publication = {false},\n abstract = {Grape cultivation faces various challenges, such as pests, management, fertilizer quality, and diseases caused by bacteria, fungi, and viruses. Notably, powdery mildew and blotches are significant diseases with different features, necessitating an accurate detection system to minimize crop losses. While traditional methods involve capturing images of diseased leaves, this research proposes a smart approach using deep learning and machine learning algorithms to analyze images taken by strategically placed cameras on farms. The research aims to design a system capable of detecting diseases that can provide information relevant to decisions, alert farmers, and allow authorized actions. Employing artificial intelligence algorithms such as support vector machines (SVM), convolutional neural networks (CNN), decision trees (DT), Naive Bayes (NB), and random forest (RF), the proposed model classifies datasets of powdery mildew, blotches, and healthy leaves when using augmented and histogram-oriented gradient (HOG) preprocessing. Following the classification of affected and healthy leaves, a stacking algorithm is used to select the optimal algorithm that provides the highest level of accuracy. The experimental results and analysis reveal that the CNN classifier outperforms others, achieving an accuracy of 96.1%. When transfer learning and fine tuning are applied to the CNN-based model, the accuracy of the model increases by 1.4% and 3.1%, respectively. 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