SAES-422 Multistate Research Activity Accomplishments Report

Sections

Basic Information
Additional Info

Status: Approved

Basic Information

Project No. and Title: W2009 : Integrated Systems Research and Development in Automation and Sensors for Sustainability of Specialty Crops
Period Covered: 10/01/2013 to 09/01/2014
Date of Report: 09/09/2014
Annual Meeting Dates: 07/23/2014 to 07/24/2014

Changying Li, UGA, cyli@uga.edu Filip To, Msstate, fto@abe.msstate.edu Qin Zhang, WSU, qinzhang@wsu.edu Stavros Vougioukas, UCDavis, svougioukas@ucdavis.edu Mark Siemens, U of Ariz, siemens@cals.arizona.edu Joseph Dvorack, Univ Kentucky, joe.dvorak@uky.edu Loren Gautz, u of Hawaii, lgoutz@hawaii.edu David Slaughter, UC Davis dcslaughter@ucdavis.edu Clark Seavert, Oregon State, Clark.Seavert@oregonstate.edu Reza Ehsani, U of Florida, ehsani@ufl.edu Raj Khosla, Colorado State, raj.khosla@colostate.edu David Lee, U of Florida, wslee@ufl.edu Ning Wang, Oklahoma State Univ, ning.wang@okstate.edu

Brief Summary of Minutes of Annual Meeting

Room Location: BIG SUR B
Start Time: 8:00 AM, July 24, 2014.
Meeting Adjourned at 3:30 PM
Chair: Changying Charlie Li, University of Georgia
Vice Chair: Raj Khosla, Colorado State
Secretary: Filip To, Mississippi State University

The technical tour was provided by the ICPA meeting in the afternoon (1-6 PM) of July 23rd. The participants were provided the opportunity to visit Orchard crops (Almonds and Walnuts) in Arbuckle and tomato field in Woodland, as well as the Robotics and Automation Lab in UC Davis/Bio. and Agr. Eng. Dept.

Announcements of Deadlines
Annual Report Due date: 60 days after the meeting. It must be submitted to regional leader (administrator) 30 days prior to due date, a deadline for members to submit reports to committee chair for compilation. Specific dates: Reports were to be sent to Charlie Li by August 11 for compilation; W-2009 reports to be sent to Jim Moyer (regional administrator) by August 25; W-2009 reports due September 24 to national administrator.

Settings
NCERA-180 and W-2009 Combined meeting was conducted in the morning for discussion of common interest for two hours and then separated for discussion specific to each committee. Video teleconference presentation by Dr. Daniel Schmoldt, National Program Leader of National Institute of Food & Agriculture (NIFA), USDA was conducted in the combined meeting. Contents included updated information from USDA and congressional appropriations of various mandatory and discretionary programs of 2014 and 2015. Electronic copies of presentation slides were distributed to members.

Joint Discussions
Arrangement of Technical Tour in conjunction with the meeting was determined to be an important component of the meeting and should be continued.

Update from members about ASABE events
A new group was formed for safety standard related to agricultural robotics. The following ASABE technical committees are involved in this effort: IET318 (mechatronics, PM-48 (specialty crop), PM 58 (ag. automation).

Next year ASABE will be in Louisiana.

CIGR congress in Beijing, Sept 2015, including Smart Agriculture Symposium, Automation Tech for Off Road Equipment. International forum for Precision Agriculture.

Separate meeting rooms were used for NCERA-180 and W-2009 at 10:30 AM.

W-2009 related events:

Aug 2014, International Horticulture Conference in Brisbane (Symposium of Mechanisation, Precision Horticulture, and Robotics in Fruit and Vegetable Production), Australia.
2nd international symposium on mechanical harvesting of specialty crops will be held in Washington State in September 2015.
9th International Symposium on Fruit, Nut and Vegetable Production Engineering (Frutic Italy 2015) will be held in Milan, Italy on May 19-22, 2015.
2015 European conference in Precision Agriculture will be in Israel.
AETC in Louisville KY 2014, farm industry show, 2015 will be on Feb 9-10 in Louisville, Kentucky again.

Election of Officers for 2015:
Chair Elect: Raj Khosla, Colorado State
Vice Chair: Filip To, Mississippi State University
Secretary: Stavros Vougioukas, U.C. Davis

Decision for 2015 W-2009 meeting:
The meeting will be held at Washington State University at Prosser, Date: Sept 13-16, in conjunction with the Mechanical Harvest Symposium. A Technical Tour will be arranged in the morning of September 16th (Wednesday) and business will be on Wednesday afternoon or Thursday Morning.

Station Reports:
The remainder of the time was used for oral presentations and updates from each station. All members in attendance presented updates and will submit annual reports to the Chair for compilation.

Individual State Reports can be found on the W-2009 Homepage (when NIMSS is fixed) at: http://lgu.umd.edu/lgu_v2/homepages/attachs.cfm?trackID=15436 Meeting was adjourned at 3:30PM.

Attached file:

W-2009---State-Reports-for-July-2014-Mtg.pdf

Accomplishments

Members of W2009 have produced several outcomes resulting from number of research activities. The research areas are broadly classified as: (1) specialty crop management (thinning, processing, disease detection & chemical application), (2) mechanical harvesting (including harvest assist), and (3) specialty crop postharvest handling, quality and safety.

(1) Specialty crop management
During 2014, an automated machine for thinning lettuce developed at the University of Arizona was further developed so that it could thin unwanted plants and spot apply insecticides to saved crop plants in the same pass. The automated thinning machine was also modified so that it could be used as an intra-row and inter-row weeding machine. Study results in lettuce showed that automated weeding followed by hand weeding significantly reduced overall labor requirements. In California, 3D spatial location data for more than 10,000 fruits (pears and clingstone peaches) while on the tree were collected. These data would help growers verify the effectiveness of their pruning and thinning strategies.

A collaborative research project was conducted at the University of Central Florida and citrus research and education center of the University of Florida to develop a robust ground and aerial crop health monitoring system that could detect and map crop stress in citrus and strawberry production systems. This project has accomplished the following goals: 1) Developed an innovative ground based robotic system with autonomous capabilities to help monitor and analyze a strawberry field for diseases in close-proximity by taking spectral imaging and collecting leaf samples throughout the field; 2) Developed an octocopter to help monitor and analyze a strawberry field rapidly and relay the suspected area coordinates to the ground robot; 3) Developed the first version of disease detection sensor, which could be applied on both the aerial- and ground-based platforms. In addition, the following projects were conducted in Florida: 1) A novel technique was developed to detect immature green citrus in tree canopy under natural outdoor conditions; 2) Using polarized filter and narrow band imaging technique, a portable machine vision system was developed to detect the citrus greening symptomatic leaves; 3) Another machine vision system was developed to detect dropped citrus fruit on the ground along with a GPS receiver. It can count the number of fruit and estimate mass of the fruit with an accuracy of 89%; 4) A prototype laser weeding system was developed to kill in-row weeds using machine vision and a set of lasers to demonstrate the concept.

In Iowa, a new set of plant detection algorithms based on 3D point cloud data analysis were developed for robotic non-chemical weeding. A new generation prototype of an automated intra-row mechanical weeder is under development. A robotic vehicle that is equipped with auto-steer system and a rig of six stereo camera heads has been developed for high-throughput plant phenotyping using robotic technologies.

A series hybrid drivetrain for agricultural machinery was tested in Kentucky. The drivetrain was constructed using a diesel engine, a generator, a battery pack and electric traction motors. The traction motors were connected to a dynamometer and the drivetrain was tested at various loads. Two small scale autonomous ground vehicle test platforms were constructed from 1/10th scale RC model vehicles. This system will be used to test control methods and multiple-vehicle coordination algorithms for accomplishing field operations. Testing was also performed on tablet-based location services. In Oklahoma, various projects were carried out to improve pecan production efficiency and sustainability. For instance, pecan yield measurement technique was established by using backscattered terrestrial microwave sensing. Wireless image sensor network was used for monitoring the population of pecan weevils. A 1/4-scale robot was fabricated and tested for selectively thinning peach blossoms in Pennsylvania. In the lab, using simulated peach blossoms at random positions, the heuristic thinning algorithm accurately controlled the robotic arm and end effector performance, reaching the goal of removing at least 95% of target blossoms. Automated pruning project was carried out as well. Pruning of both apples and grapes, once thought to be a mysterious blend of art and science, has been described by a set of rules. Apple pruning rules developed through this project have been published at http://extension.psu.edu/plants/tree-fruit/news/2013/renewal-pruning-for-high-density-apple-plantings.

In Washington, various research activities were performed including precision canopy and water management based on both light penetration and orchard micro-climate measurements, automated cane-berry canopy management, effective canopy or target zone chemical delivery technologies, and in-orchard sensing technologies for various automated agricultural operations.

(2) Mechanical harvesting (including harvest assist)
A low-cost harvest-assist device for apple orchard platforms was designed and fabricated in Pennsylvania. The device had four main components: receiver (where pickers placed the apples), two transport tubes, manifold, and distributor (which distributed the apples into a standard bin). Field tests will be performed in October 2014. Ergonomic and efficiency studies comparing the harvest assist device with standard ladder and basket picking began in 2014. Development of mechanical harvesting technologies for fresh market fruit has been one of major focuses for the WSU research team. A few generations of sweet cherry harvesting prototypes have been designed, fabricated and tested in both research and commercial orchards from 2009 to 2013. Repeated field trials verified that the tree training will play a big role in the effectiveness of mechanical harvest; harvesting efficiency varied from only 50% of hand picking on traditional tree canopy to 10 times faster from “Y-trellis” fruiting wall trees. The WSU team also worked on fresh market apple harvesting concepts. Pattern shaking, linear shaking and twisting mechanisms were evaluated to detach fruit from stem. Effectiveness of these systems depended on apple variety. Mechanical-assist technologies were also extensively studied to improve the harvesting efficiency in fresh market apple harvest. Four different platforms, including mobile carrier and pneumatic fruit conveyer, were tested in commercial orchards. Economic and safety analysis of using such platforms were also performed. The mechanical-assist technology developments also included the study of a robotic solution for bin handling in high density apple and cherry orchards to improve both the worker efficiency and safety in orchards.

(3) Specialty crop postharvest handling, quality and safety.
An optical flow cell and automated maturity measurement system using color were developed for use in official inspection by the California Processing Tomato Inspection Program. This system provides tomato growers and processors with new tools needed for precise and unbiased assessment of tomatoes at harvest to facilitate accurate determination of market value.

In Georgia, a multimodal machine vision system was developed for quality inspection of onions. The system integrates hyperspectral, color, 3D, and X-ray imaging technologies to evaluate multiple onion quality properties nondestructively, such as diameter, volume, and density. This multimodal machine vision system was also able to detect defective onions (Burkholderia cepacia and Pseudomonas viridiflava infected) more effectively (84.21%) than using a single sensor (78.95% and lower). Blueberry packing lines were evaluated using the Berry Impact Recording Device (BIRD). Six replicates were tested through the packing line before and after five transition points were padded using Poron padding sheet and impacts were recorded at the transition points. The biggest impacts occurred at the beginning and end of the line. The impact level was reduced significantly at these transition points after padding.

Green bean coffee heat treatment facility was reconfigured in Hawaii, which helps control the coffee berry borer (CBB). Dielectric spectroscopy and X-ray imaging were used to estimate quality of in-shell pecans. Optical sensors and algorithms were developed to predict plant N status for nitrogen management. Output:
The W2009 members collectively published about 65 research papers, the majority of which were published in well-recognized, peer reviewed journals. The research outputs have been summarized in these publications. In addition, significant contributions were made through workshops, conferences, field demonstrations, online materials, extension materials, etc. The growers and other stakeholders are involved in these projects with significant contribution in terms of knowledge and other inputs. Numerous students and researchers have been trained in these areas of research. The research stations will continue to work on their specific projects that contribute towards the goals of this project.

Impacts

The geo-referenced location data of fruits in tree canopies and the tree branch geometries will be used to perform model-based machine design for tree fruit mechanized harvesting. The potential impact is reduced dependence on manual labor for fresh market fruit harvest
The development of an accurate, automated system for determination of color and maturity in processing tomato will directly impact tomato growers and processors by providing a cost-effective and accurate system of characterizing the stage of development and subsequent quality of the harvested fruit. Well mature tomato fruit have superior flavor and lycopene content. By ensuring that harvested tomatoes are mature, the health benefits of adding lycopene to the diet and the pleasure of consuming flavorful food can be delivered to consumers in the final processed tomato product.
The multisensor-based system can evaluate both external and internal quality parameters of onions, and provided a base for the further development of fully automated robotic system for onion quality inspection. The system and methods developed from this project are also potentially applicable to quality inspection of other agricultural products.
The low operating cost ? approx. $20 per lot ? will encourage compliance with the coffee berry borer (CBB) quarantine of Hawaii County. Compliance should delay, if not prevent, the movement of CBB coffee farms on the other islands of the State of Hawaii. Savings and increased markets will be experienced for Hawaii County coffee producers shipping to the high population and high tourist activity islands of Oahu and Maui.
Automated non-chemical weeding could achieve good weed control efficacy mechanically at substantially lower power levels than previously considered. Such technology has potential for broad impact as it opens the way to the development of smaller autonomous, mechanical weeding systems. Small robotic weeding system for specialty crops may substantially lower energy costs for producers.
Development of a series hybrid drivetrain allows combining the simplicity, controllability and efficiency of electric motors with the energy density of liquid fuels. It also permits operation of the internal combustion (IC) engine at its most energy efficient point. A further benefit is the decoupling of the power source from the ground drive.
In Oklahoma, native pecan production accounts for about 90 percent of the state?s crop. Accurate estimates of pecan yields prior to harvest are critically important for both production management decisions and marketing. Producers continually seek methods to improve orchard management, including nitrogen fertilization. Rapidly escalating nitrogen (N) costs have made this a high priority among pecan producers nationwide.
A few devices have been validated in extensive field trials in commercial orchards/farms in Washington. It includes, but not limited to, hand-held mechanical blossom thinners, orchard harvest labor management systems, high-density orchard light penetration measurement system, robotic twining machine for hop production, and site-specific precision irrigation control system for apple orchard. Among them, the technology of hand-held blossom thinning device and orchard harvest labor management system has been made available to local equipment fabricators for promoting the commercialization of research outcomes as useful tools for growers.

Publications

1. Fennimore, S.A., B. D. Hanson, L. M. Sosnoskie, J. B. Samtani, A. Datta, S. Z. Knezevic, and M. C. Siemens. 2013. Chapter 9: Field Applications of Automated Weed Control: Western Hemisphere. In Automation: The Future of Weed Control in Cropping Systems, 151-169. S.L. Young and F.J. Pierce, eds. Dordrecht: Springer Science+Business Media.

2. Aksenov, A. A., A. Pasamontes, D. J. Peirano, W. Zhao, A.M. Dandekar, O. Fiehn, R. Ehsani and C. Davis. 2014. Detection of Huanglongbing Disease Using Differential Mobility Spectrometry. Analytical Chemistry. 86(5):2481-2488.

3. Arikapudi, R., Durand-Petiteville, A., Vougioukas, S. (2014). Model-based assessment of robotic fruit harvesting cycle times. ASABE Annual Intl. Meeting; Paper Number 1913999, Montreal, Quebec, Canada.

4. Bansal, R., W. S. Lee, and S. Satish. 2013. Green citrus detection using Fast Fourier Transform (FFT) leakage. Precision Agriculture 14(1): 59-70. http://dx.doi.org/10.1007/s11119-012-9292-3.

5. Bao, Y., A. D. Nakarmi, L. Tang. 2014. Develooment of a filed phenotyping robotic system for sorghum biomass yield component traits characterization. ASABE Paper No. 141901199, St. Joseph, MI: ASABE.'

6. Baugher, T.A., P.H. Heinemann, J.S. Schupp, and K.M. Lewis. 2013. Innovations in peach thinning. Compact Fruit. 46(3):23-25.

7. Blossom thinning device for fruit trees. Applied Engineering in Agriculture. 29(2): 155-160.

8. Caplan, S., B. Tilt, G. Hoheisel, T. Baugher. 2014. Specialty crop growers’ perspectives on adopting new technologies. HortTechnology 24: 81-87.

9. Choi, D., W. S. Lee, R. Ehsani, and A. Banerjee. 2013. Detecting and counting citrus fruit on the ground using machine vision. ASABE Paper No. 131591603. St. Joseph, Mich.: ASABE.

10. Du, X., Chen, D., Zhang, Q., Scharf, P.A., Whiting, M.D. (2013). Response of UFO (upright fruiting offshoots) on cherry trees to mechanical harvest by dynamic vibratory excitation. Transactions of the ASABE. 56(2): 345-354.

11. Dvorak, J., Hasani, H. 2014. Testing of Tablet-Based GPS Systems. ASABE Paper No. 141922411. St. Joseph, Mich.: ASABE Engineering.114(3): 344–350.

12. Farangis Khosro, A., Rehal, R., Fathallah, F., Wilken, K., Vougioukas, S. (2014). Sensor-based Stooped Work Monitoring in Robot-aided Strawberry Harvesting. ASABE Annual Intl. Meeting; Paper Number 1913911, Montreal, Quebec, Canada. FL. pp. 263-294.

13. Garcia-Ruiz, F., S. Sankaran, J. M. Maja, W. S. Lee, J. Rasmussen, and R. Ehsani. 2013. Comparison of two aerial imaging platforms for identification of Huanglongbing infected citrus trees. Computers and Electronics in Agriculture 91: 106-115. http://dx.doi.org/10.1016/j.compag.2012.12.002 14. Hanna, H. M., B. L. Steward, and K. A. Rosentrater. 2014. Evaluation of mechanized row cover establishment for cantaloupe and summer squash. ASABE Paper No. 141894433. St. Joseph, Mich.: ASABE.

15. Hardin, J. A., C. L. Jones, P. R. Weckler, N. O. Maness, J. W. Dillwith, and R. D. Madden. 2013. Rapid in situ Quantification of Leaf Cuticular Wax Using FTIR-ATR. Transactions of the ASABE, 56(1): 331-339.

16. Hardin, J. A., P. R. Weckler, and C. L. Jones. 2013. Microwave Backscatter Response of Pecan Tree Canopy Samples for Estimation of Pecan Yield in situ Using Terrestrial Radar. Computers and Electronics in Agriculture. 90 (2013): 54-62.

17. He, L., Arikapudi, R., Khosro Anjom, F., Vougioukas, S. (2014). Worker Position Tracking for Safe Navigation of Autonomous Orchard Vehicles Using Active Ranging. ASABE Annual Intl. Meeting; Paper Number 141913710, Montreal, Quebec, Canada.

18. He, L., Zhang, Q., Charvet, H. (2013). A knot-tying for robotic hop twining. Biosystems Engineering.114(3): 344–350

19. He, L., Zhou, J., Du, X., Chen, D., Zhang, Q., Karkee, M. (2013). Energy efficacy analysis of a mechanical shaker in sweet cherry harvest. Biosystems Engineering, 116(4): 309-315.

20. Jadhav, U., L. R. Khot, R. Ehsani, V. Jagdale, J. K. Schueller. 2014. Volumetric mass flow sensor for citrus mechanical harvesting machines. Computers and Electronics in Agriculture. 101: 93-101.

21. Jiang, Yu and C. Li. A Push-broom based Hyperspectral Imaging System for Cotton Trash Identification. ASABE Paper No: 141898244. Montreal, Quebec Canada. July 13-16, 2014.

22. Karkee, M., Steward, B. Kruckeberg, J. (2013). Agricultural infotronic systems. In: Zhang, Q.,

23. Katti, A. R., W. S. Lee, and C. Yang. 2013. Laser weeding system for elimination of in-row weeds. In Proceedings of the 5th Asian Conference on Precision Agriculture (ACPA), June 25-28, 2013, Jeju, Korea.

24. Khedher Agha, M. K., W. S. Lee, C. Wang, R. W. Mankin, N. Bliznyuk, and R. A. Bucklin. 2013. Determination degrees of insect infestation in triticale seed using NIR spectroscopy. ASABE Paper No. 131592957. St. Joseph, Mich.: ASABE.

25. Khedher Agha, M. K., W. S. Lee, R. A. Bucklin, A. A. Teixeira, and A. Blount. 2013. Equilibrium moisture content equation for triticale seed. ASABE Paper No. 131620333. St. Joseph, Mich.: ASABE.

26. Lee, W. S. 2013. Book review: N. Kondo, M. Monta, and N. Noguchi, Agricultural robots - mechanisms and practice, Corona Publishing Co., Ltd. Tokyo, Japan, 2011, xii + 348 pp., ISBN: 978-4-87698-553-1. Journal of Biosystems Engineering 38(2): i.

27. Li, C., P. Yu, F. Takeda, G. Krewer. 2013. A miniature instrumented sphere to understand impacts created by mechanical blueberry harvesters. HortTechnology. 23(4): 425-429.

28. Li, H., W. S. Lee, and K. Wang. 2013. Airborne hyperspectral imaging based citrus greening disease detection using different dimension reduction methods. ASABE Paper No. 131592802. St. Joseph, Mich.: ASABE.

29. Li, H., W. S. Lee, and K. Wang. 2013. Spectral mixture analysis based citrus greening disease detection using satellite image of Florida. In Proceedings of the 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). 25-28 June 2013, Gainesville, Florida, USA.

30. Li, H., W. S. Lee, K. Wang, R. Ehsani, and C. Yang. 2013. ‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture. http://dx.doi.org/10.1007/s11119-013-9325-6.

31. Li, J. and L. Tang. 2013. Machine Vision-based Indirect Estimation of the Position and Attitude of Mobile Robots. ASABE Paper No. 131620769, St. Joseph, MI: ASABE.

32. Li, J. and L. Tang. 2013. Real-time Plant Recognition for Robotic Weeding Using a 3D ToF Sensor. ASABE Paper No. 131620787, St. Joseph, MI: ASABE.

33. Li. H, W. S. Lee, K. Wang, R. Ehsani, and C. Yang. 2014. Extended spectral angle mapping (ESAM) for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture 15:162-183

34. Liaghat, S. *, Ehsani, R., Mansor, S.B., Shafri, H.Z.M., Meon, S., Azam, S.H.M.N. and Noh, N.M.2014. Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms. International Journal of Remote Sensing, 35:10, 3427-3439.

35. Liaghat, S., S. Mansor, R. Ehsani, H.Z.M. Shafri, S. Meon and S. Sankaran. 2014. Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm. Computers and Electronics in Agriculture 101: 48-54.

36. Mathanker, S. K., P. R. Weckler, N. Wang. 2013. Thz Applications In Food And Agriculture: A Review, Transactions of the ASABE. 56(3): 1213-1226.

37. He, L., Zhou, J., Du, X., Chen, D., Zhang, Q., Karkee, M. (2013). Energy efficacy analysis of a mechanical shaker in sweet cherry harvest. Biosystems Engineering, 116(4): 309-315.

38. Nakarmi, A. D. and L. Tang. 2014. Within-Row spacing sensing of maize plants using 3D computer vision. Biosystems Enngineering PP. 54-64 DOI Information: 10.1016/j.biosystemseng.2014.07.001

39. Pierce, F.J. (eds). Agricultural Automation Fundamentals and Practices, CRC Press, Boca Raton,

40. Pourreza, A., W. S. Lee, E. Raveh, R. Ehsani, and E. Etxeberri. 2014. Citrus greening disease detection using narrow band imaging and polarized illumination. Transactions of the ASABE. 57(1): 259-272.

41. Pourreza, A., W. S. Lee, E. Raveh, Y. K. Hong, and H. J. Kim. 2013. Identification of citrus greening disease using a visible band image analysis. ASABE Paper No. 131591910. St. Joseph, Mich.: ASABE.

42. Saber, M., W. S. Lee, T. F. Burks, G. E. MacDonald, and G. Salvador. 2013.An automated mechanical weed control system for organic row crop production. ASABE Paper No. 131593595. St. Joseph, Mich.: ASABE.

43. Schupp, J., T. Auxt Baugher, P. Heinemann, E. Winzeler, T. Kon and M. Schupp. 2013. Labor efficient apple and peach production. Compact Fruit 46(2):17-19.

44. Siemens, M.C. Robotic weed control. 2014. In Proc. 66th Annual California Weed Science Society 66: 76-80. Salinas, Calif.: California Weed Science Society.

45. Slaughter, D.C., (2014). Standardization and Automation in Official Maturity Grading of Processing Tomato. ASABE Annual Intl. Meeting; Paper Number 1900557, Montreal, Quebec, Canada.

46. Takeda, F., G. Krewer, C. Li, D. MacLean, and J. W. Olmstead. 2013. Techniques for increasing machine-harvest efficiency in southern highbush and rabbiteye blueberries. HortTechnology. 23(4): 430-436.

47. Taufik Ahmad, M., L. Tang, B. Steward. 2013. Chapter 7: Automated Mechanical Weeding. Automation: The Future of Weed Control in Cropping Systems. S. L. Young and F. J. Pierce (eds). Springer. P 125 – 138.

48. Toledo, O. M., B. L. Steward, L. Tang, and J. Gai. 2014. Techno-economic analysis of future precision field robots. ASABE Paper No. 141903313. St. Joseph, Mich.: ASABE.

49. Tu, X. Y. and L. Tang. 2013. Robust Navigation Control of an Autonomous Agricultural Robotic Vehicle. ASABE Paper No. 131620548, St. Joseph, MI: ASABE.

50. Wang, M. (2013). A Hand-Held Mechanical Device for Target Blossom Thinning in Sweet Cherry. Ph.D. Dissertation, Washington State University.

51. Wang, M., Wang, H., Zhang, Q., Lewis, K.M., Scharf, P.A. (2013). A hand-held mechanical blossom thinning device for fruit trees. Applied Engineering in Agriculture. 29(2): 155-160.

52. Wang, N. 2013. Challenges and opportunities of nondestructive sensing technology in food and agricultural applications. The 9th International Workshop on Nondestructive Quality Evaluation of Agricultural, Livestock, and Fishery Products. November 19-22, 2013. Taipei, Taiwan. (Invited)

53. Wang, W. and C. Li. 2014. Size estimation of sweet onions using consumer-grade RGB-depth sensor. Food Engineering. 142: 153–162.

54. Wang, Weilin and C. Li. A multimodal quality inspection system based on 3D, hyperspectral, and X-ray imaging for onions. ASABE Paper No: 141900673. Montreal, Quebec Canada. July 13-16, 2014.

55. Wang, Weilin. A multiple sensor system for quality inspection of onions and investigation of onion optical properties. Ph.D. Dissertation. University of Georgia. December, 2014. Athens, Georgia.

56. Wei-jiunn, J., Lewis, G., Hoachuck, J., Slaughter, D., Wilken, K., Vougioukas, S. (2014). Vibration-reducing Path Tracking Control for a Strawberry Transport Robot. ASABE Annual Intl. Meeting; Paper Number 1914011, Montreal, Quebec, Canada.

57. Xu, Rui, Changying Li, Fumiomi Takeda, and Gerard Krewer. Measuring Impacts of Blueberries during Transportation and Packing. ASABE Paper No: 141898243. Montreal, Quebec Canada. July 13-16, 2014.

58. Yang, C., W. S. Lee, and P. Gader. 2013. Band selection of hyperspectral imagery for the classification of blueberry fruit maturity stages and leaf. ASABE Paper No. 131593276. St. Joseph, Mich.: ASABE.

59. Yang, C., W. S. Lee, P. Gader, and H. Li. 2013. Hyperspectral band selection using Kullback-Leibler divergence for blueberry fruit detection. In Proceedings of the 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). 25-28 June 2013, Gainesville, Florida, USA.

60. Yu, P., C. Li, F. Takeda and G. Krewer. 2014. Visual bruise assessment and analysis of mechanical impact measurement in southern highbush blueberry. Applied Engineering in Agriculture. 30(1): 29-37.

61. Yu, P., C. Li, F. Takeda, G. Krewer, G. Rains, and T. Hamrita. 2014. Evaluation of rotary, slapper, and sway blueberry mechanical harvesters for potential fruit impact points using a miniature instrumented sphere. Comput. Electron. Agr. 101:84–92.

62. Zhang, Q., Pierce, F.J. (2013). Agricultural Automation Fundamentals and Practices. CRC Press, Boca Raton, FL. (397p).

63. Zhang, Q., Shao, Y., Pierce, F.J. (2013). Agricultural infotronic systems. In: Zhang, Q., Pierce, F.J. (eds). Agricultural Automation Fundamentals and Practices, CRC Press, Boca Raton, FL. pp. 41-62.

64. Zhao, Z., P.H. Heinemann, J. Liu, J.R. Schupp, and T.A. Baugher. 2014. Design, fabrication, and testing of a low-cost apple harvest-assist device. ASABE Paper No. 141839738. American Society of Agricultural and Biological Engineers. 13 pp.

65. Zhou, J., He, L., Zhang, Q., Du, X., Chen, D., Karkee, M. (2013). Evaluation of the Influence of Shaking Frequency and Duration in Mechanical Harvesting of Sweet Cherry. Applied Engineering in Agriculture, 29(5): 607-612.

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