Classification of Cacao Pod if Healthy or Attack by Pest or Black Pod Disease Using Deep Learning Algorithm

Cacao farming is a worldwide industry and a vital resource for some businesses. But it is constantly threatened by diseases and pest attacks that can cause significant loss to cacao farmers. Using Artificial Intelligence and Deep Learning Algorithm, an automated recognition of these attacks can help the farmers respond immediately to control this event. This paper used Deep Learning Algorithm to address the automatic classification of a cacao pod condition. An experimental research design method is utilized, and a convolutional neural network is used for training. The model can classify three conditions of a given cacao pod image: healthy, black pod disease attack, and pest attack. Under controlled conditions, the model correctly classifies the cacao pod condition with an accuracy of 94 Thus, using the trained lightweight model, it is possible to accurately and automate the classification of cacao pod conditions. Further study is recommended to integrate it with hardware monitoring/surveillance devices to perform real-time classification of the cacao pod condition on the actual field. With this in place, it can then support fast and immediate responses mitigating the loss of production.