# A methodology for detection and localization of fruits in apples orchards from aerial images

arXiv: https://arxiv.org/abs/2110.12331 · HTML: https://mlsanity.com/paper/2110.12331/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 24 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Deep Fruit Detection in Orchards](https://mlsanity.com/paper/1610.03677/) (2016)
- [Grape detection, segmentation and tracking using deep neural networks and three-dimensional association](https://mlsanity.com/paper/1907.11819/) (2019)
- [A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards](https://mlsanity.com/paper/1810.09499/) (2018)
- [Monocular Camera Based Fruit Counting and Mapping with Semantic Data Association](https://mlsanity.com/paper/1811.01417/) (2018)

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

- [Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models](https://mlsanity.com/paper/2208.02707/) (2022)
- [WalnutData: A UAV Remote Sensing Dataset of Green Walnuts and Model Evaluation](https://mlsanity.com/paper/2502.20092/) (2025)

## Abstract (arXiv)

Computer vision methods based on convolutional neural networks (CNNs) have presented promising results on image-based fruit detection at ground-level for different crops. However, the integration of the detections found in different images, allowing accurate fruit counting and yield prediction, have received less attention. This work presents a methodology for automated fruit counting employing aerial-images. It includes algorithms based on multiple view geometry to perform fruits tracking, not just avoiding double counting but also locating the fruits in the 3-D space. Preliminary assessments show correlations above 0.8 between fruit counting and true yield for apples. The annotated dataset employed on CNN training is publicly available.