Have a question?

Mass Harvesters :: Catch Frame/Recovery Rate Improvements

Annual Progress Report:


During citrus harvesting, two canopy shake and catch harvesting machines work in pairs, one on each side of the row of citrus trees necessitating synchronization of their travel speed and steering. Unreliable synchronization causes inefficiency in the catching system. This degrades the capability of the citrus harvesting system. To retrieve the fallen fruit, additional labor and time is required. This is one issue that lowers the canopy shaker’s harvesting efficiency.


In recent years, mass harvesting fruit removal efficiency has been reported up to 95%, while the catch frame recovery efficiency has been reported between 88% and 92%. In recent grower observations, noted by Roka, catch frame efficiencies have been dropping into the low 80%, and in some cases even below 80%. The potential reasons for catch frame losses are numerous.

  1. Misalignment of forward/reverse synchronization of left and right vehicles
  2. In/out gap between catch frame seal and tree trunks on either side
  3. Vertical misalignment of catch frame seals due to grove terrain, especially considering the bed-top and swell-bottom elevation differences.
  4. The effectiveness of the fish-scale system employed to seal tree trunk

Key Findings (Updated 05/10):

The specific accomplishments for 2009/2010 were the following:

  1. The design concepts developed in 08/09 have been further modeled and developed for a new catch frame closure system and the frame position control. A scaled prototype of the new catch frame system is being developed to demonstrate the concept. The concept consists of a new closure material which should be more compliant to the tree trunk, and a three section micro-adjustment which improve closures around the tree, while still protecting resets.
  2. Further advances in autonomous guidance were pursued to enable machinery synchronization and navigation in the citrus alleyway. We have developed and tested a migrateable auto-guidance control architecture that could be adapted to a broad range of applications. We have also worked on development of improved end of row turning approaches and will be proving performance by end of research year.

Activities Planned for 2010-2011

To some extent the next steps in this research will be dependent upon the available funding. Assuming that sufficient funding is available to continue research, we would propose the following next steps.

  1. The autonomous guidance systems has been developed for in-row navigation and end of row turning for single vehicle systems with good success at this stage. However, the next step is synchronized vehicle control. Past efforts have attempted to use under canopy laser sensor handshaking with moderate success. We believe that a better approach is to use RF transmitters over the top to establish master-slave along row following. We also propose a trunk and canopy sensing approach to maintain lateral vehicle position. In this next phase we propose to develop and test this approach. The final scope will be dependent on funding availability.
  2. We now propose to develop a full scale, fully functional prototype to demonstrate and test catch frame performance under laboratory conditions. This prototype will allow us to test various aspects of the design, in order to further demonstrate feasibility and test reliability and catch frame effectiveness under laboratory conditions. This effort will consist of development of the catch frame mechanical framework, actuators, and controls. Once fabricated and assembled, testing will be conducted using simulated harvesting conditions to evaluate the efficiency of the catch frame concepts. Design modifications will be made along the way to improve performance. At the end of this funding cycle, we plan to have a functional full scale prototype.

Publications and Extension Products


Annual Progress Report

Mehta, S.S., T. F. Burks, W. E. Dixon. 2010. Target Reconstruction Based Visual Servo Control for Autonomous Citrus Harvesting. Journal of Intelligent Service Robotics: Special Issue on Agricultural Robotics, submitted 2/7/10.

Bulanon, D M, Burks, T F, Alchanatis, V. 2010. A Multispectral Imaging Analysis for Enhancing Citrus Fruit Detection. Environmental Control in Biology (in press - to be published in Vol.48, No.2 June 2010)

Subramanian, V., T.F. Burks, and W.E. Dixon. 2009. Sensor Fusion Using Fuzzy Logic Enhanced Kalman Filter For Autonomous Vehicle Guidance in Citrus Groves. Transactions of ASABE 52(5) 1-12. Hannan M; Burks T F; Bulanon D M.2009. A Machine Vision Algorithm for Orange Fruit Detection. The CIGR Ejournal. Manuscript 1281. Vol. XI. December 2009.

Bulanon, D M; Burks T F; Alchanatis V. 2009. Fruit Visibility Analysis for Citrus Harvesting. Transactions of the ASABE 52(1): 277-283

Conference Papers and Presentations:

Subbiah, Sundar; Burks. T.F. 2010 Control Architecture for Autonomous Agricultural Machinery. 2010 ASABE Annual International Meeting, Pittsburg, PA, June 20-23, 2010. Conference Paper 1008885.

Han, Sanghoon; Burks. T.F. 2010 Multilayered Active Mesh Tracking for Grove Scene. 2010 ASABE Annual International Meeting, Pittsburg, PA, June 20-23, 2010. Conference Paper 1008886.

Burks, T. F. 2009. Development of Autonomous Navigation For Citrus Groves using Vision and Ladar. 2009 ASABE Annual International Meeting, Reno, NV. June 21-24, 2009. (Invited Panelist Presentation).

For more information

Tom Burks

Visits since 05/21/2014