# Apple Counting using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2208.11566 · HTML: https://mlsanity.com/paper/2208.11566/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [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)
- [Improving Deep Learning using Generic Data Augmentation](https://mlsanity.com/paper/1708.06020/) (2017)
- [Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards](https://mlsanity.com/paper/1610.08120/) (2016)

## Built on by (most cited first, 7 of 7)

- [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)
- [S3AD: Semi-supervised Small Apple Detection in Orchard Environments](https://mlsanity.com/paper/2311.05029/) (2023)
- [NTrack: A Multiple-Object Tracker and Dataset for Infield Cotton Boll Counting](https://mlsanity.com/paper/2312.10922/) (2023)
- [AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards](https://mlsanity.com/paper/2409.17400/) (2024)
- [Computer vision system to count crustacean larvae](https://mlsanity.com/paper/2209.05834/) (2022)
- [CropNeRF: A Neural Radiance Field-Based Framework for Crop Counting](https://mlsanity.com/paper/2601.00207/) (2026)

## Abstract (arXiv)

Estimating accurate and reliable fruit and vegetable counts from images in real-world settings, such as orchards, is a challenging problem that has received significant recent attention. Estimating fruit counts before harvest provides useful information for logistics planning. While considerable progress has been made toward fruit detection, estimating the actual counts remains challenging. In practice, fruits are often clustered together. Therefore, methods that only detect fruits fail to offer general solutions to estimate accurate fruit counts. Furthermore, in horticultural studies, rather than a single yield estimate, finer information such as the distribution of the number of apples per cluster is desirable. In this work, we formulate fruit counting from images as a multi-class classification problem and solve it by training a Convolutional Neural Network. We first evaluate the per-image accuracy of our method and compare it with a state-of-the-art method based on Gaussian Mixture Models over four test datasets. Even though the parameters of the Gaussian Mixture Model-based method are specifically tuned for each dataset, our network outperforms it in three out of four datasets with a maximum of 94% accuracy. Next, we use the method to estimate the yield for two datasets for which we have ground truth. Our method achieved 96-97% accuracies. For additional details please see our video here: https://www.youtube.com/watch?v=Le0mb5P-SYchttps://www.youtube.com/watch?v=Le0mb5P-SYc.