Robotic Yield Estimation of Apple Orchards

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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”.

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%.

Counting Performance for Sunny Side of a Row


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%.

Counting Performance for Shady Side of the Same Row


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
4Z. Li, V. Isler
Large Scale Image Mosaic Construction for Agricultural Applications
IEEE Robotics and Automation Letters, 1(1): 295-302, 2016.
3P. 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.
2N. 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
1P. 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.
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