<p>Coconut is a vital crop for many regions, providing significant economic benefits and raw materials for various industries. However, the coconut industry faces numerous challenges, including disease management, which impacts productivity and sustainability. This study explores the use of advanced remote sensing technologies and machine learning, particularly convolutional neural networks (CNN), to improve the early detection and segmentation of coconut diseases. By integrating multispectral and hyperspectral imaging with deep learning algorithms, we aim to enhance the accuracy and efficiency of disease identification in coconut plantations. Our approach involves the development of a robust disease segmentation model using a pre-trained CNN. We focus on processing remote sensing images to classify and segment various coconut diseases. The methodology includes data acquisition, preprocessing, model training, and evaluation. To address dataset imbalances, we apply data augmentation techniques, synthetic data generation, class weighting, and resampling. Experimental results demonstrate high model performance in terms of precision, recall, and F1-scores across different disease classes. The proposed model achieves significant accuracy in identifying diseases such as bud root dropping, bud rot, gray leaf spot, leaf rot, and stem bleeding. Field validations confirm the model’s effectiveness in real-world conditions, proving its potential for practical applications in precision agriculture. This research highlights the importance of integrating modern technologies in agriculture to overcome production challenges. The findings suggest that the developed model can significantly improve disease management in coconut farming, leading to increased productivity and sustainability. Furthermore, the model’s applicability extends to other crops, making it a versatile tool for enhancing agricultural practices globally.</p>