Summary of Agregnet: a Deep Regression Network For Flower and Fruit Density Estimation, Localization, and Counting in Orchards, by Uddhav Bhattarai et al.
AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards
by Uddhav Bhattarai, Santosh Bhusal, Qin Zhang, Manoj Karkee
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The authors propose a deep regression-based network, AgRegNet, to estimate density, count, and location of flowers and fruits in tree fruit canopies without explicit object detection or polygon annotation. The model leverages segmentation information and attention modules to highlight relevant features while suppressing background noise. Experimental evaluation showed that AgRegNet achieved promising accuracy in estimating flower and fruit density, count, and centroid location using metrics such as SSIM, pMAE, and mAP. The proposed approach relies on point annotation and is suitable for sparsely and densely located objects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps farmers by creating a way to automatically count flowers and fruits in orchards without needing humans to do it manually. The AgRegNet model can also estimate the density of flowers and fruits, which can help farmers make decisions about how many chemicals or machines they need to use. The model uses deep learning techniques and is able to ignore background noise. It was tested on pictures of apple flowers and fruit and showed promising results. |
Keywords
» Artificial intelligence » Attention » Deep learning » Object detection » Regression
