The demand for intelligent agriculture is increasing due to the continuous impact of world food and environmental crises. Focusing on fruit detection, with the rapid development of object detection technology, it is now possible to achieve high efficiency and high accuracy in fruit detection systems. However, detecting fruit with high precision in unstructured orchard environments remains particularly challenging. Such environments, which are composed of varying illumination conditions and degrees of occlusion, can be mitigated by certain strategies. To our knowledge, this is the first time that optimization strategies used in fruit detection have been reviewed. This review aims to explore methods for improving fruit detection in complex environments. First, we describe the common types of complex backgrounds found in outdoor orchard environments. Subsequently, we divide the improvement measures into two categories: optimization before and after image sampling. Next, we compare the test results obtained before and after the application of these improved methods. Finally, we describe the future development trends of fruit detection optimization technology in complex backgrounds. We hope that this review will inspire researchers to design their optimization strategies and help explore lower-cost and more robust fruit detection systems.

由于世界粮食和环境危机的持续影响，对智能农业的需求正在增加。专注于水果检测，随着物体检测技术的快速发展，现在水果检测系统已经可以实现高效率和高精度。然而，在非结构化果园环境中高精度检测水果仍然特别具有挑战性。这种由不同光照条件和遮挡程度组成的环境可以通过某些策略来缓解。据我们所知，这是第一次审查用于水果检测的优化策略。本综述旨在探索在复杂环境中改进水果检测的方法。首先，我们描述了室外果园环境中常见的复杂背景类型。随后，我们将改进措施分为两类：图像采样之前和之后的优化。接下来，我们比较应用这些改进方法前后获得的测试结果。最后，我们描述了复杂背景下水果检测优化技术的未来发展趋势。我们希望这篇综述能够启发研究人员设计他们的优化策略，并帮助探索成本更低、更强大的水果检测系统。