# Few-Shot Object Detection in Real Life: Case Study on Auto-Harvest Source: [Few-Shot Object Detection in Real Life: Case Study on Auto-Harvest]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:42:59.203Z Authors: Kevin Riou, Jingwen Zhu, Suiyi Ling, Mathis Piquet, Vincent Truffault, Patrick Le Callet Published: 2020-11-05T09:24:33.000Z ## Abstract Confinement during COVID-19 has caused serious effects on agriculture all over the world. As one of the efficient solutions, mechanical harvest/auto-harvest that is based on object detection and robotic harvester becomes an urgent need. Within the auto-harvest system, robust few-shot object detection model is one of the bottlenecks, since the system is required to deal with new vegetable/fruit categories and the collection of large-scale annotated datasets for all the novel categories is expensive. There are many few-shot object detection models that were developed by the community. Yet whether they could be employed directly for real life agricultural applications is still questionable, as there is a context-gap between the commonly used training datasets and the images collected in real life agricultural scenarios. To this end, in this study, we present a novel cucumber dataset and propose two data augmentation strategies that help to bridge the context-gap. Experimental results show that 1) the state-of-the-art few-shot object detection model performs poorly on the novel \`cucumber' category; and 2) the proposed augmentation strategies outperform the commonly used ones. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()