Early detection of illnesses and pest infestations in fruit cultivation is critical for maintaining yield quality and plant health. Computer vision and robotics are increasingly employed for the automatic detection of such issues, particularly using data-driven solutions. However, the rarity of these problems makes acquiring and processing the necessary data to train such algorithms a significant obstacle. One solution to this scarcity is the generation of synthetic high-quality anomalous samples. While numerous methods exist for this task, most require highly trained individuals for setup. This work addresses the challenge of generating synthetic anomalies in an automatic fashion that requires only an initial collection of normal and anomalous samples from the user - a task that is straightforward for farmers. We demonstrate the approach in the context of table grape cultivation. Specifically, based on the observation that normal berries present relatively smooth surfaces, while defects result in more complex textures, we introduce a Dual-Canny Edge Detection (DCED) filter. This filter emphasizes the additional texture indicative of diseases, pest infestations, or other defects. Using segmentation masks provided by the Segment Anything Model, we then select and seamlessly blend anomalous berries onto normal ones. We show that the proposed dataset augmentation technique improves the accuracy of an anomaly classifier for table grapes and that the approach can be generalized to other fruit types.

翻译：水果栽培中病害与虫害的早期检测对于维持产量品质和植株健康至关重要。计算机视觉与机器人技术正日益广泛地应用于此类问题的自动检测，特别是采用数据驱动的解决方案。然而，由于这些问题较为罕见，获取并处理训练此类算法所需数据成为重大障碍。解决数据稀缺的一种方案是生成高质量的合成异常样本。尽管存在多种生成方法，但大多数需要经过专业训练的人员进行配置。本研究致力于以自动化方式生成合成异常样本，仅需用户提供初始的正常与异常样本集——这对种植者而言是项简易任务。我们在鲜食葡萄栽培场景中验证了该方法。具体而言，基于正常浆果表面相对光滑而缺陷会导致纹理复杂化的观察，我们提出了双Canny边缘检测（DCED）滤波器。该滤波器能强化表征病害、虫害或其他缺陷的附加纹理特征。借助Segment Anything模型提供的分割掩码，我们进而筛选异常浆果并将其无缝融合至正常浆果上。实验表明，所提出的数据集增强技术提升了鲜食葡萄异常分类器的准确率，且该方法可推广至其他水果种类。