Detection of “Cocoa Swollen Shoot Disease” in Ghanaian Cocoa Trees Based on Convolutional Neural Network (CNN) and Deep Learning Technique

The application of Convolutional Neural Networks and Deep Learning Techniques in the detection of "Cocoa Swollen Shoot" disease in Ghanaian cocoa trees have demonstrated its effectiveness and reliability. This approach provides a valuable tool for cocoa farmers and agricultural authorities to promptly identify and manage the disease, contributing to the sustainable production of cocoa and the preservation of Ghana's cocoa industry. 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, and computer-aided diagnosis. The objective, which we aim for in this article, is to extract information in an effective way for a better diagnosis of the plants attending the disease of “swollen shoot”.

The application of Convolutional Neural Networks and Deep Learning Techniques in the detection of "Cocoa Swollen Shoot" disease in Ghanaian cocoa trees have demonstrated its effectiveness and reliability. This approach provides a valuable tool for cocoa farmers and agricultural authorities to promptly identify and manage the disease, contributing to the sustainable production of cocoa and the preservation of Ghana's cocoa industry. 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, and computer-aided diagnosis. The objective, which we aim for in this article, is to extract information in an effective way for a better diagnosis of the plants attending the disease of “swollen shoot”.