Name
Title | ||
|---|---|---|
A comparative study of fruit detection and counting methods for yield mapping in apple orchards |
Abstract | ||
|---|---|---|
We present a modular end-to-end system for yield estimation in apple orchards. Our goal is to identify fruit detection and counting methods with the best performance for this task. We propose a novel semantic segmentation-based approach for fruit detection and counting and perform extensive comparative analysis against other state-of-the-art techniques. This is the first work comparing multiple fruit detection and counting methods head-to-head on the same data sets. Fruit detection results indicate that the semisupervised method, based on Gaussian Mixture Models, outperforms the deep learning-based methods in the majority of the data sets. For fruit counting though, the deep learning-based approach performs better for all of the data sets. Combining these two methods, we achieve yield estimation accuracies ranging from 95.56% to 97.83%. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1002/rob.21902 | JOURNAL OF FIELD ROBOTICS |
Keywords | Field | DocType |
agriculture,learning,perception | Computer vision,Yield mapping,Artificial intelligence,Engineering,Agricultural engineering | Journal |
Volume | Issue | ISSN |
37.0 | 2.0 | 1556-4959 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolai Häni | 1 | 4 | 2.75 |
| Pravakar Roy | 2 | 10 | 3.53 |
| Volkan Isler | 3 | 1222 | 107.38 |