# A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards

arXiv: https://arxiv.org/abs/1810.09499 · HTML: https://mlsanity.com/paper/1810.09499/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2018.

## Built on by (most cited first, 13 of 13)

- [FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework](https://mlsanity.com/paper/2408.06190/) (2024)
- [Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review](https://mlsanity.com/paper/2303.02632/) (2023)
- [Localizing Small Apples in Complex Apple Orchard Environments](https://mlsanity.com/paper/2202.11372/) (2022)
- [FruitNeRF++: A Generalized Multi-Fruit Counting Method Utilizing Contrastive Learning and Neural Radiance Fields](https://mlsanity.com/paper/2505.19863/) (2025)
- [End-to-end deep learning for directly estimating grape yield from ground-based imagery](https://mlsanity.com/paper/2208.02394/) (2022)
- [A methodology for detection and localization of fruits in apples orchards from aerial images](https://mlsanity.com/paper/2110.12331/) (2021)
- [AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards](https://mlsanity.com/paper/2409.17400/) (2024)
- [Exploring the Effectiveness of Dataset Synthesis: An application of Apple Detection in Orchards](https://mlsanity.com/paper/2306.11763/) (2023)
- [ChatGPT and general-purpose AI count fruits in pictures surprisingly well](https://mlsanity.com/paper/2404.08515/) (2024)
- [Review of Fruit Tree Image Segmentation](https://mlsanity.com/paper/2412.14631/) (2024)
- [Accurate Crop Yield Estimation of Blueberries using Deep Learning and Smart Drones](https://mlsanity.com/paper/2501.02344/) (2025)
- [DATR: Diffusion-based 3D Apple Tree Reconstruction Framework with Sparse-View](https://mlsanity.com/paper/2508.19508/) (2025)
- [A Comparative Study of Modern Object Detectors for Robust Apple Detection in Orchard Imagery](https://mlsanity.com/paper/2604.09996/) (2026)

## Abstract (arXiv)

We present new methods for apple detection and counting based on recent deep learning approaches and compare them with state-of-the-art results based on classical methods. Our goal is to quantify performance improvements by neural network-based methods compared to methods based on classical approaches. Additionally, we introduce a complete system for counting apples in an entire row. This task is challenging as it requires tracking fruits in images from both sides of the row. We evaluate the performances of three fruit detection methods and two fruit counting methods on six datasets. Results indicate that the classical detection approach still outperforms the deep learning based methods in the majority of the datasets. For fruit counting though, the deep learning based approach performs better for all of the datasets. Combining the classical detection method together with the neural network based counting approach, we achieve remarkable yield accuracies ranging from 95.56% to 97.83%.