# Detecting Olives with Synthetic or Real Data? Olive the Above

arXiv: https://arxiv.org/abs/2308.08271 · HTML: https://mlsanity.com/paper/2308.08271/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 53 references that are in the map.
- First posted on arXiv 16 Aug 2023.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [State-Aware Tracker for Real-Time Video Object Segmentation](https://mlsanity.com/paper/2003.00482/) (2020)
- [MinneApple: A Benchmark Dataset for Apple Detection and Segmentation](https://mlsanity.com/paper/1909.06441/) (2019)
- [UDepth: Fast Monocular Depth Estimation for Visually-guided Underwater Robots](https://mlsanity.com/paper/2209.12358/) (2022)
- [Grape detection, segmentation and tracking using deep neural networks and three-dimensional association](https://mlsanity.com/paper/1907.11819/) (2019)
- [EVPropNet: Detecting Drones By Finding Propellers For Mid-Air Landing And Following](https://mlsanity.com/paper/2106.15045/) (2021)
- [Finding Berries: Segmentation and Counting of Cranberries using Point Supervision and Shape Priors](https://mlsanity.com/paper/2004.08501/) (2020)
- [Unpaired Image Translation via Vector Symbolic Architectures](https://mlsanity.com/paper/2209.02686/) (2022)
- [OysterNet: Enhanced Oyster Detection Using Simulation](https://mlsanity.com/paper/2209.08176/) (2022)
- [DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation](https://mlsanity.com/paper/2007.10521/) (2020)
- [Attention-driven Next-best-view Planning for Efficient Reconstruction of Plants and Targeted Plant Parts](https://mlsanity.com/paper/2206.10274/) (2022)
- [OysterSim: Underwater Simulation for Enhancing Oyster Reef Monitoring](https://mlsanity.com/paper/2209.09395/) (2022)
- [SeaDroneSim: Simulation of Aerial Images for Detection of Objects Above Water](https://mlsanity.com/paper/2210.16107/) (2022)
- [3D Reconstruction-Based Seed Counting of Sorghum Panicles for Agricultural Inspection](https://mlsanity.com/paper/2211.07748/) (2022)

## Built on by (most cited first, 2 of 2)

- [ODYSSEE: Oyster Detection Yielded by Sensor Systems on Edge Electronics](https://mlsanity.com/paper/2409.07003/) (2024)
- [Synthetic Data Augmentation for Enhanced Chicken Carcass Instance Segmentation](https://mlsanity.com/paper/2507.18558/) (2025)

## Abstract (arXiv)

Modern robotics has enabled the advancement in yield estimation for precision agriculture. However, when applied to the olive industry, the high variation of olive colors and their similarity to the background leaf canopy presents a challenge. Labeling several thousands of very dense olive grove images for segmentation is a labor-intensive task. This paper presents a novel approach to detecting olives without the need to manually label data. In this work, we present the world's first olive detection dataset comprised of synthetic and real olive tree images. This is accomplished by generating an auto-labeled photorealistic 3D model of an olive tree. Its geometry is then simplified for lightweight rendering purposes. In addition, experiments are conducted with a mix of synthetically generated and real images, yielding an improvement of up to 66% compared to when only using a small sample of real data. When access to real, human-labeled data is limited, a combination of mostly synthetic data and a small amount of real data can enhance olive detection.