Deep Learning in Arecanut Research: A Comprehensive Survey

Arecanut (betel nut) is an important tropical crop grown mainly in India, which ranks second worldwide in its production and consumption. However, Arecanut plants face numerous diseases affecting their leaves, trunks, roots, and fruits, some of which are hard to detect visually. However, its production is severely threatened by various leaf and nut diseases, particularly Yellow Leaf Disease (YLD), which causes substantial economic losses. Early and accurate disease detection is therefore crucial for ensuring sustainable cultivation. Recent advancements in machine learning and deep learning have enabled automated approaches for identifying, classifying, and predicting Arecanut diseases through image-based analysis. This survey paper provides a comprehensive review of existing works ranging from conventional image processing techniques to modern (CNNs) and transfer learning models such as MobileNetV2, ResNet, and VGG-16. Studies have demonstrated the effectiveness of hybrid methods combining CNN with SVM, as well as region-specific assessments of disease severity. Additionally, research on causal agents of YLD and spatial disease pattern analysis highlights the growing role of AI in precision agriculture. By comparing methodologies, datasets, and performance metrics across multiple studies, this paper identifies current challenges such as limited annotated datasets, variations in environmental conditions, and the need for realtime mobile applications. Finally, it outlines potential research directions including multimodal disease prediction, lightweight deep learning models for deployment on edge devices, and integration with Internet of Things (IoT)-based monitoring systems to achieve scalable and farmer-friendly solutions.