<p>Among the crops cultivated, the most important is a coconut that provides both economic and nutritional values; thus, early detection of the diseases will prevent this significant loss and ensure the sustainability of a coconut plantation. In precision agriculture, accurate identification and classification of diseases in coconut trees are vital to ensure good crop yield and sustainable farming. The paper provides a new approach to the segmentation of diseases on coconut trees through the EFCM algorithm. The classification model uses a Hybrid Deep Learning approach in which DeepLab is combined with a Recurrent Neural Network. EFCM extends traditional segmentation approaches by introducing the spatial context and using advanced image characteristics for, in the case of images of agriculture, better delimiting areas damaged by pests or diseases and so on. Furthermore, with the combination of both even the best feature extraction ability of DeepLab with firm roots and the excellent context temporal understanding by RNN, a solid and accurate classification of disease outbreaks can be made. The proposed model is implemented using Python and there was an improvement in the efficacy of this integrated approach to 98.16%. The method proffers a precious tool in analyzing early disease detection and management in the coconut plantation. This informed decision for the farmer subsequently results in better health and an increase in crop productivity because of an increase in precision in disease identification.</p>