Overview of our yield mapping work
Performance Evaluation of Yield Mapping
We developed algorithms for detecting apples, counting them and estimating their diameters from a video sequence.
For validation of our algorithms we hand labelled the apples both in the real world and in images. This allowed us to obtain ground truth to evaluate our algorithms.
Hand Labelled Apples
Propagation of Hand Labels Using Homography
In this video we show how our detection method is performing. The blue boxes are detection by the algorithm and the green boxes are the manually labelled ones. We detect 97.861% of the hand labelled apples which we call, “Hit Rate”.
In the next video, we show the performance of our counting algorithm from the sunny side of a row. The conservative and non-conservative tags are based on a supervised detection procedure. The detection process labels the pixels which has a large probability of belonging to apples as conservative, and ones with lesser probabilities as non-conservative. Median accuracy for counting with respect to visible apples from both sides is 93.86%.
Next we show the performance for the shady side of the same row. Median accuracy for counting with respect to visible Apples from both sides is 80.18%.
This project is funded by USDA NIFA MIN-98-G02.
Related Publications
| 2020 | |
|---|---|
| 10 | Nicolai Hani, Pravakar Roy, Volkan Isler MinneApple: A Benchmark Dataset for Apple Detection and Segmentation IEEE Robotics and Automation Letters, 2020. |
| 9 | Wenbo Dong, Pravakar Roy, Volkan Isler Semantic mapping for orchard environments by merging two-sides reconstructions of tree rows Journal of Field Robotics, 37(1): 97--121, 2020. |
| 2019 | |
| 8 | Nicolai Häni, Pravakar Roy, Volkan Isler A comparative study of fruit detection and counting methods for yield mapping in apple orchards Journal of Field Robotics, 2019. |
| 2018 | |
| 7 | Nicolai Häni, Pravakar Roy, Volkan Isler Apple Counting using Convolutional Neural Networks In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. |
| 6 | Pravakar Roy, Wenbo Dong, Volkan Isler Registering Reconstructions of the Two Sides of Fruit Tree Rows In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. |
| 2017 | |
| 5 | Pravakar Roy, Volkan Isler Active view planning for counting apples in orchards In Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on, 2017. |
| 2016 | |
| 4 | Z. Li, V. Isler Large Scale Image Mosaic Construction for Agricultural Applications IEEE Robotics and Automation Letters, 1(1): 295-302, 2016. |
| 3 | P. Tokekar, J. V. Hook, D. Mulla, V. Isler Sensor Planning for a Symbiotic UAV and UGV System for Precision Agriculture IEEE Transactions on Robotics, PP(99): 1-1, 2016. |
| 2 | N. Stefas, H. Bayram, V. Isler Vision-Based UAV Navigation in Orchards In 5th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture, 2016. |
| 2013 | |
| 1 | P. Tokekar, J. Vander Hook, D. Mulla, V. Isler Sensor Planning for a Symbiotic UAV and UGV system for Precision Agriculture In Proc. International Conference on Intelligent Robots and Systems (IROS), 2013. pdf,tech-report,.bib |