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Detection of Cocoa Leaf Diseases Using the CNN-Based Feature Extractor and XGBOOST Classifier

Open Journal of Applied Sciences 2024年第10期14卷 2955-2972页

作者： kouassi Simeon kouassi Mamadou Diarra kouassi hilaire edi Brou Jean-Claude Koua Mechanics and Computer Science Laboratory Felix Houphout-Boigny University Abidjan Ivory Coast Mathematics and Computer Science Laboratory Nangui Abrogoua University Abidjan Ivory Coast

Among all the plagues threatening cocoa cultivation in general, and particularly in West Africa, the swollen shoot viral disease is currently the most dangerous. The greatest challenge in the fight to eradicate this pandemic remains its early detection. Traditional methods of swollen shoot detection are mostly based on visual observations, leading to late detection and/or diagnostic errors. The use of machine learning algorithms is now an alternative for effective plant disease detection. It is therefore crucial to provide efficient solutions to farmers’ cooperatives. In our study, we built a database of healthy and diseased cocoa leaves. We then explored the power of feature extractors based on convolutional neural networks such as VGG 19, Inception V3, DenseNet 201, and a custom CNN, combining their strengths with the XGBOOST classifier. The results of our experiments showed that this fusion of methods with XGBOOST yielded highly promising scores, outperforming the results of algorithms using the sigmoid function. These results were further consolidated by the use of evaluation metrics such as accuracy, mean squared error, F score, recall, and Matthews’s correlation coefficient. The proposed approach, combining state of the art feature extractors and the XGBOOST classifier, offers an efficient and reliable solution for the early detection of swollen shoot. Its implementation could significantly assist West African cocoa farmers in combating this devastating disease and preserving their crops.

关键词： Machine Learning Cocoa Leaf Diseases Deep Learning Convolutional Neural Network Feature Extraction Image Recognition XGBOOST

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Urogenital Trauma by Pelvic Impalement: A Case Reported at Bouake University Hospital

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Open Journal of Urology 2023年第2期13卷 64-72页

作者： N’Diamoi Akassimadou kouassi Patrice Avion Koffi Dje Brice Aguia edi Edmond Gowe Freddy Junior Zouan Dago Venance Alloka Sadia Kamara Roger Lebeau Urology Department Bouaké University Hospital Bouaké Ivory Coast Urology Department Abidjan Cocody University Hospital Abidjan Ivory Coast General and Digestive Surgery Department Bouaké University Hospital Bouaké Ivory Coast

Urogenital impalement trauma is a particular form of penetrating trauma to the pelvis. They are rare, spectacular or dramatic because they can be responsible for serious vascular and/or visceral lesions. This study reports a case of hypogastric impalement in a 19-year-old young man that caused musculoaponeurotic and urogenital lesions. Emergency surgical exploration was carried out urgently by a multidisciplinary team.

关键词： Urogenital Trauma Impalement Emergency Surgery Cystorrhaphy

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