Fruit Detection Methods Based on Deep Learning in Agricultural Planting: A Systematic Literature Review

With the continued advancement of agricultural intelligence, automation, and mechanization, artificial intelligence-driven fruit detection technology has developed rapidly. As an important task in agricultural computer vision, fruit target detection in real-world planting environments presents numerous technical challenges. This paper provides a systematic review of recent breakthroughs and representative studies in this field. Based on a comprehensive analysis of existing research, we classify deep learning-based fruit detection models into four application scenarios: few-shot detection (addressing limited data availability and high annotation costs), complex scene detection (resolving issues arising from object occlusion, overlapping, and variable illumination), small-target detection (improving performance on low-resolution and densely clustered objects), and real-time detection (designing lightweight algorithms for faster inference). The paper summarizes innovative technical approaches and evaluates detection performance across these scenarios, highlighting recent advancements in fruit detection for agricultural production and offering valuable insights for further technological innovation.