Subasinghe, MPanditharathne, RPasanjith, RNadun, TSamarakoon, UTissera, W2026-09-202026-05-22M. Subasinghe, R. Panditharathne, R. Pasanjith, T. Nadun, U. Samarakoon and W. Tissera, "CocoSense: AI-Powered Drone-Based System for Comprehensive Coconut Tree Health Monitoring and Yield Prediction," 2026 6th International Conference on Computer Communication and Artificial Intelligence (CCAI), Nanjing, China, 2026, pp. 1057-1062, doi: 10.1109/CCAI69603.2026.11641885.979-833158248-7https://rda.sliit.lk/handle/123456789/5277Coconut cultivation is vital to Sri Lanka's agricultural economy, yet farmers face significant challenges in early pest detection, disease diagnosis, and yield prediction. This research presents CocoSense, an AI-powered mobile application integrated with IoT technology for automated coconut tree health monitoring using drone-captured imagery. The system comprises four modules: (1) pest detection using EfficientNetB0 (91.44% accuracy) and MobileNetV2 (96.08% accuracy) with a trilingual AI chatbot for treatment recommendations; (2) disease detection for leaf rot, leaf spot, and leaf dieback classification (98.69% accuracy); (3) health assessment for leaf (93.70%) and branch health (99.63%); and (4) coconut yield estimation (87.86% accuracy) using YOLOv8 with dual-view acquisition strategy. Additionally, a coconut bunch detection (88.96% accuracy) module is developed to support yield estimation by identifying fruit clusters within tree canopies. The system integrates IoT-based GPS tracking with Google Maps API for real-time plantation visualization. Experimental results demonstrate that CocoSense provides a robust, accessible solution for intelligent coconut plantation management in Sri Lanka.enDeep LearningDisease ClassificationPest DetectionPrecision AgricultureTransfer LearningConference PaperDOI: 10.1109/CCAI69603.2026.11641885