Towards Smart Agriculture: A Survey of Deep Learning Applications in Arecanut Image Analysis

Deep learning has ignited a revolution in arecanut image analysis, promising transformative accuracy, robustness, and automation in quality assessment. Yet, data scarcity, computational demands, and explainability gaps remain hurdles to achieving its full potential. This review dissects these strengths and limitations, charting a course for future research. We propose tackling data scarcity through domain adaptation and active learning, while unveiling deep learning’s decision-making through advanced explainability methods. Recognizing the complexities of arecanut analysis, we advocate for domain-specific architectures and prioritize interdisciplinary collaboration to address ethical considerations, sustainability, and integration with farm management systems. By illuminating these research gaps and charting a path forward, this review empowers deep learning to unlock the true potential of the arecanut industry.