# AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards

arXiv: https://arxiv.org/abs/2409.17400 · HTML: https://mlsanity.com/paper/2409.17400/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 54 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 22 of 22)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://mlsanity.com/paper/1802.10062/) (2018)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [Learning from Synthetic Data for Crowd Counting in the Wild](https://mlsanity.com/paper/1903.03303/) (2019)
- [SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting](https://mlsanity.com/paper/1908.03716/) (2019)
- [Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards](https://mlsanity.com/paper/1610.08120/) (2016)
- [LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis](https://mlsanity.com/paper/2002.10100/) (2020)
- [Semantic Segmentation Refinement by Monte Carlo Region Growing of High Confidence Detections](https://mlsanity.com/paper/1802.07789/) (2018)
- [A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards](https://mlsanity.com/paper/1810.09499/) (2018)
- [A Robust Illumination-Invariant Camera System for Agricultural Applications](https://mlsanity.com/paper/2101.02190/) (2021)
- [Multispecies fruit flower detection using a refined semantic segmentation network](https://mlsanity.com/paper/1809.10080/) (2018)
- [Apple Counting using Convolutional Neural Networks](https://mlsanity.com/paper/2208.11566/) (2022)
- [Apple Flower Detection using Deep Convolutional Networks](https://mlsanity.com/paper/1809.06357/) (2018)

## Built on by (most cited first, 1 of 1)

- [BloomNet: Exploring Single vs. Multiple Object Annotation for Flower Recognition Using YOLO Variants](https://mlsanity.com/paper/2602.18585/) (2026)

## Abstract (arXiv)

One of the major challenges for the agricultural industry today is the uncertainty in manual labor availability and the associated cost. Automated flower and fruit density estimation, localization, and counting could help streamline harvesting, yield estimation, and crop-load management strategies such as flower and fruitlet thinning. This article proposes a deep regression-based network, AgRegNet, to estimate density, count, and location of flower and fruit in tree fruit canopies without explicit object detection or polygon annotation. Inspired by popular U-Net architecture, AgRegNet is a U-shaped network with an encoder-to-decoder skip connection and modified ConvNeXt-T as an encoder feature extractor. AgRegNet can be trained based on information from point annotation and leverages segmentation information and attention modules (spatial and channel) to highlight relevant flower and fruit features while suppressing non-relevant background features. Experimental evaluation in apple flower and fruit canopy images under an unstructured orchard environment showed that AgRegNet achieved promising accuracy as measured by Structural Similarity Index (SSIM), percentage Mean Absolute Error (pMAE) and mean Average Precision (mAP) to estimate flower and fruit density, count, and centroid location, respectively. Specifically, the SSIM, pMAE, and mAP values for flower images were 0.938, 13.7%, and 0.81, respectively. For fruit images, the corresponding values were 0.910, 5.6%, and 0.93. Since the proposed approach relies on information from point annotation, it is suitable for sparsely and densely located objects. This simplified technique will be highly applicable for growers to accurately estimate yields and decide on optimal chemical and mechanical flower thinning practices.