Written by Achyut Paudel, Manoj Karkee – Washington State University, Jostan Brown, Joe Davidson, Cindy Grimm, and Ashley Thompson – Oregon State University, Jan. 22, 2023
Traditional broad-acre orchard management practices do not result in targeted actions that are optimal for individual trees. This results in wasted resources and causes suboptimal fruit yield and quality. Recognizing the issue, our project aimed to enhance precision orchard fertilization practices by addressing individual tree nutrition requirements through a combination of automated sensing, machine learning algorithms, decision support tools, and variable rate technology.
In this project, we employed modern RGB-depth sensors to understand the unique characteristics of each tree. Over three years, the data was collected from 200 sample trees spread across 17 rows in a commercial orchard. Our outputs included canopy features like normalized area, tree trunk diameter, yellowness index, and yield. These features, crucial for assessing a tree’s nitrogen status, were selected based on feedback from experienced horticulturalists and growers.
We obtained these canopy features using machine vision and then used machine learning to develop a model predicting the needed nitrogen application level for each tree. The project also introduced a robotic method for nitrogen application, guided by real-time camera inputs. During our testing in October 2023, the robotic system showcased its ability to autonomously navigate the orchard, precisely localize itself within inches of each tree, and spray a test solution. The system achieved variable rate application by adjusting spray distance, ensuring that the root zones of the trees received the required treatment.
This project signifies a substantial advancement in orchard management, ensuring each tree receives tailored care for optimal growth and quality fruit production. By addressing the need for precision in nitrogen management, the project paves the way for efficient and sustainable orchard cultivation practices.
Contact
Achyut Paudel
Washington State University
achyut.paudel@wsu.edu
Jostan Brown
Oregon State University
browjost@oregonstate.edu
Manoj Karkee
Washington State University
manoj.karkee@wsu.edu
509-786-9321
Joseph R. Davidson
Oregon State University
joseph.davidson@oregonstate.edu
541-737-7171
Cindy Grimm
Oregon State University
cindy.grimm@oregonstate.edu
541-737-2600
Ashley Thompson
Oregon State University
ashley.thompson@oregonstate.edu
Project Report and Additional information
Publications from work:
- Paudel, A., Davidson, J. R., Grimm, C., & Karkee, M. (2023). Vision-based normalized canopy area estimation for variable nitrogen application in apple orchards. Smart Agricultural Technology, 5, 100309.
- Wang, T., Sankari, P., Brown, J., Paudel, A., He, L., Karkee, M., … & Todorovic, S. (2023). Automatic estimation of trunk cross sectional area using deep learning. 14th European Conference on Precision Agriculture, 2023.
- Paudel, A., Karkee, M., Davidson, J. R., & Grimm, C. (2022). Canopy Density Estimation of Apple Trees. IFAC-PapersOnLine, 55(32), 124-128.
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