Early Detection and Diagnosis of Coconut Leaf Diseases in Sri Lanka’s Coconut Triangle: A Systematic Review

Abstract

This study presents a Systematic Literature Review (SLR) on the early detection and diagnosis of coconut leaf diseases in Sri Lanka’s Coconut Triangle, a region critical to the nation’s agricultural economy. The review systematically evaluates published research on deep learning-based models, image classification techniques, and hybrid approaches that integrate environmental data for more reliable diagnosis. To ensure methodological rigor, the study followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Relevant studies were retrieved from scholarly databases including IEEE Xplore, ScienceDirect, Springer, Scopus, and Google Scholar using predefined search strings. Strict inclusion and exclusion criteria were applied to filter peer-reviewed articles published between 2016 and 2025, ensuring relevance and reproducibility. A total of 20 papers met the criteria for final review, covering a diverse range of approaches such as Convolutional Neural Networks (CNNs), transfer learning models (VGG16, ResNet, EfficientNet), ensemble methods (CNN–SVM), and IoT-assisted systems. The synthesis of findings highlights several insights: transfer learning-based CNNs achieved over 90% accuracy, even for subtle early-stage symptoms. Hybrid approaches combining CNNs with classifiers like SVMs improved precision, reducing false positives and enhancing robustness across coconut varieties. Integrating environmental data (humidity, rainfall, temperature) increased accuracy by 6– 10%, stressing the need for region-specific models in Sri Lanka’s diverse agro-climatic zones. However, most systems lack large-scale field validation, mobile deployment, and consideration of smallholder farmer resource constraints. Key research gaps include limited Sri Lanka-specific datasets, inadequate focus on multi-disease co-infections, and the absence of farmer-oriented mobile and IoT tools for real-time disease monitoring. Finally, the study highlights future directions such as building larger and varietal-specific datasets, experimenting with advanced architecture (EfficientNet, Vision Transformers), and developing lightweight, offline-capable mobile applications to ensure accessibility for rural farmers. The integration of IoT sensors and climate forecasting models is also recommended to move from disease detection towards predictive and preventive farming strategies. This review contributes to the body of knowledge by not only consolidating existing research but also providing actionable insights for researchers, policymakers, and agricultural technology developers to ensure sustainable disease management and resilience in Sri Lanka’s coconut sector.