Thesis
Over-the-row machine vision system for improved apple crop-load estimation
Washington State University
Master of Science (MS), Washington State University
2014
Handle:
https://hdl.handle.net/2376/103551
Abstract
Crop-load estimation is important for efficient management of pre- and post-harvest operations. Crop-load estimation (including counting and sizing) helps producers make a better management decisions to achieve high fruit quality and yield, and to manage labor and equipment required for harvesting and transporting of fruit from the orchard to the packinghouse. Fruit identification and size estimation based on machine vision system is also beneficial for selective robotic harvesting of fruits. Current machine vision-based techniques for crop-load estimation of apples has achieved only limited success mostly due to; i) occlusion of apples by branches, leaves and/or other apples; and ii) variable outdoor lighting conditions. In order to minimize the effect of these factors, a new sensor system was developed with an over-the-row platform: Tunnel Structure was created in the system and was used to capture images from two opposite sides of apple trees. The tunnel structure minimized illumination of apples with direct sunlight and reduced the variability in lighting condition. Images captured in a tall spindle orchard were processed for apple identification and accuracy of 79.8% was achieved. The location of apples in three-dimensional (3D) space was used to eliminate duplicate counting of apples that were visible to cameras from both sides of the tree canopy as well as to estimate the size of fruits. The error of identifying duplicate apples was found to be 21.1%. Overall, the method achieved an accuracy of 82.0% on counting apples with dual side imaging compared to 58.0% with single side imaging. Apple size was estimated by this machine vision system with an accuracy of 84.8%. Over-the-row machine vision system showed a promise for accurate and reliable crop-load estimation in the apple orchards.
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Details
- Title
- Over-the-row machine vision system for improved apple crop-load estimation
- Creators
- Aleana Gongal
- Contributors
- Manoj Karkee (Degree Supervisor)
- Awarding Institution
- Washington State University
- Academic Unit
- Biological Systems Engineering, Department of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University; [Pullman, Washington] :
- Identifiers
- 99900525139701842
- Language
- English
- Resource Type
- Thesis