Author(s)

Mamadou Coulibaly, Konan Hyacinthe Kouassi, Silue Kolo, Olivier Asseu

Ecole Supérieure Africaine des TIC, LASTIC, Abidjan, Côte d’Ivoire.

Recent advances in diagnostics have made image analysis one of the main areas of research and development. Selecting and calculating these characteristics of a disease is a difficult task. Among deep learning techniques, deep convolutional neural networks are actively used for image analysis. This includes areas of application such as segmentation, anomaly detection, disease classification, computer-aided diagnosis. The objective which we aim in this article is to extract information in an effective way for a better diagnosis of the plants attending the disease of “swollen shoot”.

Drone, Convolutional Neural Networks, Image Recognition, Feature Detection

Coulibaly, M. , Kouassi, K. , Kolo, S. and Asseu, O. (2020) Detection of “Swollen Shoot” Disease in Ivorian Cocoa Trees via Convolutional Neural Networks. Engineering, 12, 166-176. doi: 10.4236/eng.2020.123014.