Abstract
CCN-51 cocoa, one of the two main varieties exported worldwide by Ecuador, due to the lack of technology and poor agronomic practices, is constantly attacked by a number of pests that affect its production, affecting the growth stages of the plant. Another factor damaging the plant is the frequent climate changes, mainly due to excessive rainfall increasing humidity levels. These conditions damage the flowering and fruit set, leading to Moniliasis as one of the primary diseases. Given that the crops are located far from urban areas, conducting analyses is time-consuming and costly. Consequently, many producers resort to excessive chemical use to manage pests and diseases. Where, this research project is proposed, consisting of developing a mobile application that by scanning images in a controlled environment allows the detection of diseases in the CCN-51 cocoa fruit. The mobile application will use its camera to scan the fruit and, using a trained image recognition model, predict a diagnosis of the disease present in the cocoa fruit.
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Morales, M., Morocho, J., López, X., Navas, P. (2024). Application of Convolutional Neural Networks for the Detection of Diseases in the CCN-51 Cocoa Fruit by Means of a Mobile Application. In: Meng, L. (eds) International Conference on Cloud Computing and Computer Networks. CCCN 2023. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-47100-1_1
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