# Machine Vision System for Early-stage Apple Flowers and Flower Clusters Detection for Precision Thinning and Pollination

arXiv: https://arxiv.org/abs/2304.09351 · HTML: https://mlsanity.com/paper/2304.09351/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 30 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 1 of 1)

- [Apple Flower Detection using Deep Convolutional Networks](https://mlsanity.com/paper/1809.06357/) (2018)

## Built on by (most cited first, 4 of 4)

- [YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series](https://mlsanity.com/paper/2406.19407/) (2024)
- [Comprehensive Performance Evaluation of YOLOv12, YOLO11, YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments](https://mlsanity.com/paper/2407.12040/) (2024)
- [Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development](https://mlsanity.com/paper/2411.11285/) (2024)
- [Improved YOLOv12 with LLM-Generated Synthetic Data for Enhanced Apple Detection and Benchmarking Against YOLOv11 and YOLOv10](https://mlsanity.com/paper/2503.00057/) (2025)

## Abstract (arXiv)

Early-stage identification of fruit flowers that are in both opened and unopened condition in an orchard environment is significant information to perform crop load management operations such as flower thinning and pollination using automated and robotic platforms. These operations are important in tree-fruit agriculture to enhance fruit quality, manage crop load, and enhance the overall profit. The recent development in agricultural automation suggests that this can be done using robotics which includes machine vision technology. In this article, we proposed a vision system that detects early-stage flowers in an unstructured orchard environment using YOLOv5 object detection algorithm. For the robotics implementation, the position of a cluster of the flower blossom is important to navigate the robot and the end effector. The centroid of individual flowers (both open and unopen) was identified and associated with flower clusters via K-means clustering. The accuracy of the opened and unopened flower detection is achieved up to mAP of 81.9% in commercial orchard images.