Brief Summary of Minutes of Annual Meeting

The meeting started at 8:30 am.
Professor Raj Khosla was acting president. In the beginning the attendees introduced themselves. Administrative Advisor Jim Moyer spoke first about the WSU College of Agriculture, Human, and Natural Resources and the Department of Biological Systems Engineering. He proposed that the committee communicate achievements with Deans, Directors of Ag Experimental Stations and NIFA. He also stressed the importance of administrative work, i.e., reporting and highlighting the impacts of projects so that research money is justified for. Raj Khosla concurred and added that papers and other types of impact should be included in the reports. Reports are due 60 days after the meeting in the same format as last year, but the President would like to have them in advance to work on them. Professor Clark Sievert commented on how some results on the economics of platforms came much earlier and it took 4-5 years for growers to adopt and on how they improved the life of workers, which is something that cannot be translated into monetary units. REPORTING
1. Renfu Lu
Research Update on: a) Nondestructive Quality Assessment for Horticultural Products. Three area of focus: Sensor development; Property Characterization; Model/Algorithm Development. B) Field sorting of apples in the field: bin handling and sorting based in machine vision.

2. Daniel Guyer (delivered by Renfu Lu)
Report covered the following topics: Optimization of Computer Tomography (CT) Imaging for quality of specialty crops. Detection of fiber in carrots and asparagus, late blight in potato tubers, cherries, chestnuts, pineapple and cucumber. Over-the-row tart cherry harvester development.

3. Mark Siemens
He reported on the progress done on a precision intra-row weeding machine (SCRI project 2014) and an automated intra-row cultivator.

4. Clark Sievert
He reported on the commercialization of precision farming technologies and talked about web-based ag economics tools (AgBiz Logic company); UAV Technologies; Telemetry of data to AgBiz Logic; and economic study of mechanical harvesting of tree fruits.

5. Paul Heinemann (delivered by Jude Liu)
The report covered the design, testing, and improvement of a low-cost harvest assist platform. Efficiency and ergonomics (RULA – Rapid Upper Limb Assessment) were also discussed.

6. Qin Zhang
Delivered a summary of the CPAAS presentations. Commented on some technologies that are ready for commercialization but due to small market size it has been challenging to find commercial partners. Also, the smaller size of the Departments dictates tight collaboration with complementary capabilities. Discussion included how we should include extension activities in our reporting as well as in future proposals.

7. Loren Gautz
Report on latest results on Kava juice preparation and Cacao seed micro-fermentation.

8. Filip To.
Reported on crops, poultry and wildlife at Mississippi State. Topics: Rice intermittent irrigation; Poultry hatching improvement; Feral Swine.

9. Mike Delwiche (presented by Stavros Vougioukas)
Wireless sensing and actuation technology. Company was formed for commercialization.

10. David Slaughter (presented by Stavros Vougioukas)
Hyperspectral imaging for fungi detection in almonds.

11. Stavros Vougioukas
Report on mechanization for specialty crops. Presentation covered an instrumented cart for yield mapping of strawberries during manual harvesting and new results on model-based design using tree geometries and fruit locations acquired via digitization.

BUSINESS ITEMS
Election of Secretary.
Renfu Lu was unanimously voted Provisional Secretary. Once he officially joins the Working Group he will become Secretary.

Selection of location for next year.
Arizona was unanimously voted as the location for the next meeting. Time will be scheduled at a later time.

The group will receive a reminder from Raj’s secretary about the reports.

Qin Zhang suggested the formation of common interest groups within the Working Group to form “Sub-Committees” with the aim of collaborative work on new proposals, etc.

ANNOUNCEMENTS
Qin Zhang announced the 2016 AgriControl IFAC conference, which will take place in Seattle and is organized by WSU.

The meeting ended at 1 pm.

Arizona
A multi-state SCRI project was initiated to develop a precision weeding machine for controlling intra-row weeds at the centimeter level scale of accuracy. The machine will use a machine vision system for plant detection and herbicidal spray to kill weeds. A test stand was developed to evaluate the suitability of various spray assemblies for delivering herbicidal materials. One spray assembly was identified that shows great promise for meeting the design criteria. At travel speeds of 2 mph, droplet size was less than 7 mm diameter and non-target drift was less than 10%. Future work includes researching how to improve system performance and identify other suitable assemblies.

A project was also initiated to determine the feasibility of using commercially available robotic cultivators in U.S. vegetable production. These cultivators are designed to remove both inter-row and intra-row weeds. Five studies were conducted in California and Arizona. Study results showed that in fields where weed pressure was high, weeding labor requirements and total weeding costs were reduced by 29-45% and 12-20% ($11-18 /acre) respectively. An extensive outreach effort about these findings and potential for this technology was made by making presentations at meetings (2), hosting field days (3) and conducting on-farm demonstrations (10).

California
Short-term Outcomes: Yield maps were created for several bed rows of a strawberry field using a novel instrumented picking cart. An expected outcome is the adoption of such carts by growers to understand yield spatial variability in their fields.

Outputs:
A paper was presented at the ASABE Intl. Meeting, at New Orleans about the instrumented cart.

Activities: A physics-based simulator was developed to model the drop of fruits and their interception in the context of shake-and-catch mechanical harvesting. An instrumented cart was designed, built and tested to monitor strawberry harvesting speed and produce spatial yield maps for manually harvested strawberries. A motion sensor was developed that fits in a strawberry picker’s glove and records picking motions. Milestones:
Fruit fall simulator debugged and functional – December 2015. Field-testing of strawberry yield-monitoring cart with glove motion sensor– November 2015.

Georgia:
A multimodal machine vision system integrating hyperspectral, 3D, and X-ray imaging sensors was developed to evaluate quality factors of onions holistically and nondestructively. A LabVIEW program was developed to acquire color images, spectral images, depth images, X-ray images of onions, and measure the weight of onions. With the multimodal data collected, algorithms were developed to accurately measure the weight (RMSE = 3.6 grams), diameter (RMSE = 1.7 mm), volume (RMSEP = 16.5 cm3), and density (RMSE = 0.03 gram/cm3) of onions, as well as to correctly classify 88.9% healthy and defective onions. This work demonstrated the efficacy of a multimodal imaging system to non-destructively evaluate both external and internal quality parameters of onions, which is also applicable to quality inspection of other agricultural products in packing houses.

The optical properties (absorption coefficient ?a, scattering coefficient ?s, and scattering anisotropy g) of onion tissues were investigated using both a single wavelength (633 nm) and a broad spectrum (550-1650 nm). Based upon the measured optical properties of healthy and diseased onion tissues, Monte Carlo simulation was conducted to model the light propagation in multi-layer onion tissues in healthy, Botrytis neck rot and sour skin infected onions. This work could help develop more effective spectroscopic imaging method for onion quality sensing.

A customized gas sensor array system consisting of seven Metal Oxide Semiconductor (MOS) sensors was developed to detect onion postharvest diseases in storage. These MOS sensors were enclosed in a specially designed Teflon chamber equipped with a gas delivery system to pump volatiles from the onion samples into the chamber. The electronic circuit comprised of a microcontroller, non-volatile memory chip, and trickle-charge real time clock chip, serial communication chip, and parallel LCD panel. User preferences are communicated with the on-board microcontroller through a graphical user interface developed using LabVIEW. Three features were extracted from the sensor responses and three baseline correction methods were employed to correct the sensors’ responses. The gas sensor array was tested in two separate experiments with two treatments (control and sour skin infected bulbs). The best performance (85%) was achieved by using the support vector machine model when the data collected from an independent experiment were used for validation.

Dr. Li received a major research grant from USDA NIFA competitive grant program Specialty Crop Research Initiative in September 2014. As the Project Director, Dr. Li leads a multidisciplinary team from 10 institutions to develop innovative sensing technologies and semi-mechanical harvesting technologies for fresh market blueberries. The team will develop a high-throughput phenotyping system using imaging techniques to help select blueberry cultivars for mechanical harvestability. An affordable and efficient mechanical harvest-aid system will be designed and fabricated as an alternative to over-the-row harvesters to replace hand harvesting. To adopt mechanical harvest-aid technology, the harvested fruit must have less bruising and better quality for the fresh-market and longer shelf life. Therefore, it is critical to use advanced sensor technologies to understand the mechanical impacts encountered by the fruit during the process of harvesting and postharvest handling and use the information to improve harvesters and packing lines. Finally, microbial contamination has been a top concern for growers and consumers. The team will investigate the potential microbial contamination in both blueberry fruit and mechanical harvesters and determine critical control points along the harvest and postharvest chain with the new harvest system.

The second generation BIRD sensor (BIRD II) was developed and tested in multiple packing lines in the US. The BIRD II sensor has a size of 21 mm in diameter and weight of 3.9 g, which was reduced by 17% in size and 50% in weight compared to BIRD I. The sensor was able to measure accelerations up to 346g at a maximum frequency of 2 KHz. Universal Serial Bus (USB) was used to directly connect the sensor with the computer, removing the interface box used previously. A LabVIEW based PC software was designed to configure the sensor, download, and process the data. The calibration tests showed that the accuracy of the sensor was between -1.76 to 2.17g and the precision was between 0.21 to 0.81g. Dynamic drop tests showed the BIRD II had smaller variance in measurements than BIRD I. In terms of size and weight, BIRD II is more similar to an average blueberry fruit than BIRD I, which leads to more accurate measurements of the impacts for blueberries.

Hawaii:
The Hawaii Experiment Station has concentrated its activities on cacao bean fermentation and kava beverage preparation.

The cacao bean fermentation is in support of small (less than 100 kg chocolate per year) tree to bar grower/processors and variety trials in Hawaii. Both require processing of cacao beans from a few pods at a time. Small (200g to 20kg) lots of beans have substantial edge effects for temperature and moisture loss. Microbial growth is affected by these edge effects results in low quality chocolate from highly variable fermentations. We have developed a system using single use plastic bags in a temperature and humidity controlled insulated box. Freezers, refrigerators, or coolers can serve as the insulated box. An electric heater with a ramp controller (a simple controller with set point adjusted every 8hr can also be used) brings the box to 48°C over a 48hr period and lowers it to ambient again in 72-96hr. Humidity is brought to saturation at the required temperature by bubbling air through at least 30cm water column at the box temperature.

Kava beverage is traditionally prepared by macerating below ground plant parts in water. Our studies using sequential fractional factorials to determine the gradient and line searches on the gradient found that we could substantially increase the amount of beverage prepared from a given quantity of below ground plant parts. On average 45% of the available active kavalactones can be extracted, a significant improvement over the method commonly used by commercial purveyors of a kava beverage. The recommended method for water extraction of kavalactones is to: use 1.5 ml of 37 °C water per gram of fresh or fresh frozen kava root and stump (Add 4 ml per gram of dried kava powder to first repetition); put in blender of sufficient power to maintain 16000 rpm under the load of the quantity of kava beverage being prepared; blend for 90 s; press liquid from blended infusion; repeat the preceding steps 3 times.

Iowa:
In the past year, we have been developing innovative solutions to address some of the technical challenges in sensing, automation, and mechanization of specialty crop production. Specifically, we have been developing new sensing systems for crop detection and characterization, a design of the actuation system for intra-row weed control, mechanism for cucurbit row cover establishment, and yield monitor technologies for specialty crops.

Cucurbit mechanized row cover establishment: During the past year, an ASABE paper was presented on field evaluation of multi-row cover structures for cucurbit crops. Undergraduate students did an economic comparison of row cover versus chemical pest management strategy for cucurbits and evaluated operation of a mechanized tunnel layer for row covers in two different soils.

Yield monitor technologies for specialty crops: Research activities have focused on the development of yield monitoring technologies for bulk harvested crops such as sugarcane, energy cane, and specialty crops which currently do not have any available yield monitoring tools. Basic research has been conducted on sensor characterization and in-field performance of yield monitoring systems in broad environmental conditions.

Robotic weed control: A new set of plant detection algorithms based on 3D and 2D morphology and color have been developed and are currently under evaluations using field images of different plant species of different growth stages. The design of our dual pivoting arm mechanism has been refined and both electric and hydraulic drives were incorporated for prototyping and further field testing.

High-throughput plant phenotyping using robotic technologies: Robotic vehicles that are equipped with auto-steer system, stereo cameras, NIR-converted RGB cameras, and ToF 3D sensors have been developed and deployed in field for collecting images from corn and sorghum plants. Image processing algorithms with GPU accelerated parallel computation have been under development.

Kentucky:
The autonomous diesel/electric hybrid tractor was tested in the field pulling a finger weeder for intra-row cultivation in growing vegetable corn.

Power measurements were made during this finger weeding operation to determine ultimate energy requirements for a machine. It consumed approximately 4 kW and maximum energy requirements based on 5 acres per single operation would be only 20 kWh.

The autonomous navigational accuracy of the system was also tested according to ISO 12188-2. Max cross-track (XTR) error was just under 25 cm, while mean and median XTR were between 10 and 13 cm depending on speed.

The system was also tested as an autonomous harvest aid. It was capable of autonomously traveling to a field, driving along the field and returning to a packing shed on an organic farm. Travel speeds could be controlled to 0.1 m/s or 1 m/s, and this navigation was possible even without RTK corrected GPS locations.

Michigan:
Toward multi-state project objectives 2, 5, and 6: Continued progress was made in moving forward on a project for over-the-row (OTR) systems for tart cherry production. The concept of canopy shaking for harvest has been demonstrated under this multi-state in previous years and the project this year focused on a harvester from a new commercial equipment manufacturing collaborator in addition to continued evaluation of plant structure systems and plant materials. Trial and research plant growth plots being developed and studied for dwarfing for the purpose of OTR production were evaluated for yield and canopy harvest compatibility. Harvest was conducted with an unmodified self-propelled OTR blueberry canopy spindle shaker harvester. Several replicated plant development/structure trials are in their third and fourth year after planting and this year produced encouraging yields, even with poor fruit set in some locations, as well as encouraging response to maintaining dwarfing size to accommodate the harvester over the life of the orchard.

Toward objective 6: A new commercial collaborator was identified and participated in the harvest evaluation studies as well as the collaboration on a multi-state USDA-NIFA-AFRI proposal, which is currently pending. We await more substantial funding for this sub-project to theoretically and empirically study fruit detachment dynamics, however, the harvester system as it currently exists (unmodified) quite successfully removes fruit with a high level of efficiency and quality.

Toward objectives 1 and somewhat 4: Computed tomography (CT), and in some cases coupled with parallel studies of hyperspectral imaging and spectroscopy, were implemented to study internal characteristics/defects of carrots, asparagus, and chestnuts which are not detectable by any current commercial technology. CT under this project, and as published in the noted associated articles, has demonstrated very encouraging results in detecting, and even sorting into classes, various defects and undesirable internal characteristics. Such have included woody and gelatinous fiber in carrots, fibrous/stringy material in asparagus, as well as physiological and microbial disorders in chestnuts. The basic research related to this study has gone very well, however, the establishment of collaboration with potential technology development remains a challenge and goal. Thus, the focus over the past has been on broadening the application potential and consequently improving the attractiveness for such development.

Toward objectives 1 and 4: A proposal was developed and submitted to and internal MSU program (Project GREEEN) to synergistically study the potential of CT as a tool in better quantifying, and overall understanding, the identification and development of the very detrimental and broadly impacting disease of potato late blight. The proposal was funded, however, the accompanying and more substantial partner proposal with USDA-ARS was not funded and thus the study did not move forward at this point.

Penn state:
Apple harvest assist: A low-cost harvest-assist device for apple orchard platforms was designed and fabricated. 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 testing utilizing a redesigned distributor reduced downgrading of apple quality to 5%. Ergonomic analysis showed that the time spent by pickers in awkward positions, which can lead to stress injuries, was significantly reduced by utilizing the harvest-assist unit. The most hazardous positions, high picking on a ladder, were completely eliminated, as well as all other operations associated with picking from ladders. Further funding from the College of Ag Sciences at Penn State and the Penn State Research Foundation helped to provide market analysis and IP development for the project. Grower surveys showed that small operations (50 acres or less) were more inclined to have interest in such a device if the platform and harvest-assist device total cost was less than $35,000. A patent application was submitted for the harvest-assist device, and discussions with potential licensees has continued.

Automated pruning: Studies with tall spindle apple canopies have indicated that pruning rules may not need to be overly complicated to adequately describe optimal pruning. Preliminary results suggest that the number of primary branches emanating from the trunk may be the most important factor, while including additional detail such as secondary, tertiary or quaternary branching patterns adds considerable complexity but may not add significant benefits. Research is continuing to determine the optimum severity of pruning, and these data will be used in algorithms to determine optimal pruning points. An additional study with apple trees trained to a vertical axis tree form indicated that pruning trees according to a set of rules had similar effects as pruning by orchard crews or researchers. The rural sociologists at Penn State made progress on completing the interviews that will inform the future survey design. Currently, they have traveled to 4 states to conduct interviews with apple and wine-grape growers:
NY Apple: 9
NY Grape: 6
PA Apple: 9
PA Grape: 5
WA Apple: 7
CA Apple: 7
CA Grape: 2+
Total: 45+

They also interviewed one agribusiness representative who serves fruit growers in Canada and the US, and conducted a small survey of 40 California wine-grape growers.

Although the exact operating characteristics of an autonomous apple pruner are unknown, the minimum operating characteristics of such a machine can be estimated. For example, ground speed can be estimated if additional assumptions about the length of the operating season are made. If it is assumed that such a machine could be operated for 12 weeks per season, the minimum speed required to justify the purchase of a $120,000 machine would vary from a low of 2.7 feet per minute to 12.6 feet per minute depending on the pruning cost per tree. If such a machine could be developed that could operate at ground speeds of 4-6 feet per minute, it would be economical for a wide range of high-density plantings.

Washington:
Development of mechanization and automation technologies for specialty crops production has been one of major focuses for the WSU research team. In the past year the team has focused on conducting the following researches on, but not limited to: (1) human-machine collaboration for robotic harvesting of fresh market apples; (2) shaking and catching harvesting of fresh market apples; (3) robotic bin management system in tree fruit orchards; (4) robotic weeding for vegetable production, and (5) heat treatment-based technologies for prolonging productivity of HLB-infected citrus trees. As those projects are all under different phases of studies, a few prototyping technologies or research platforms, ranged from robotic end-effectors to mobile platforms, have either been designed, fabricated or tested in field, with associated software and/or algorithms been developed, in the past reporting cycle from July 2014 to September 2015 for respective projects. In addition, the team has also completed a few project specific research activities, such as study of basic physics of apple hand-picking and robotic picking, experimental study of fruit bruising during harvest process, and conceptual study on developing “machine-friendly” tree training structures supporting mechanized production. A theoretical and empirical economic model to assess the impact of various factors on the net revenues of using mechanical harvesters and hand labor to harvest blueberries has also been developed. A few key outcomes from those research projects include, again not limited to, knowledge on (1) fruit grasping patterns and forces in harvesting apples, which is useful for scientists and engineers to design and develop effective end-effectors to achieve bruise-free grasping patterns for robotic picking, (2) optimal combination of shaking frequency and amplitude for harvesting different apple varieties, (3) type of catching mechanism and optimal catching angle which will be critically useful for developing bruising-free shake and catch harvesting system, and (4) a multi-robot bin management simulation tool which can be used to plan and control the implementation of bin management robots in orchards.

Other research activities performed by the team include automated irrigation management for specialty crops, wireless sensor network and/or clouds computing based in-orchard labor management systems research, development, extension education and commercialization supports.

What opportunities for training and professional development has the project provided?
The WSU team formed a trans-disciplinary research and extension team, consisting of engineers, computer scientists, horticulturists, economists and extension specialists, who are affiliated with the WSU Center for Precision and Automated Agricultural Systems (CPAAS). A total of 12 Ph.D. or M.S. graduate students in agricultural and biological engineering, and agricultural economics have worked under the supervision of WSU PIs. PIs, Post-docs, students and scholars interacted frequently to discuss the progresses, address challenges and lay out future tasks and activities. Students and scholars carried out most of the day-to-day research activities including data collection and analysis. Students were also supervised for research paper writing, presentation and publications. The team has also collaborate with local community colleges to provide their students opportunities to gain technology awareness, and even participating in research activities, in our specialty crop automation research. In addition, WSU CPAAS has hosted a few bilateral and multilateral research and education collaborations which allowed us to send more than a dozen person-times of graduate students and faculty members to universities in Australia, China, Germany, Italy, Japan, and New Zealand and to host over 20 person-times students and faculty members from Australia, Brazil, China, Finland, India and Nepal either for internship training, collaborative research, or academic exchanges.

How have the results been disseminated to the communities of interest?
Developed systems/devices were demonstrated at 2015 CPAAS open house and presented our research outcomes to over 200 growers, researchers, and other stakeholders in this event. The research team has also conducted more than two dozen field trials using developed technologies or prototypes were conducted in various collaborating commercial orchards/farms, allowing growers and field workers have an opportunity to closely observe the research activities conducted by our team. Many of those research results have been presented via journal and trade magazine articles, TV interviews and local newspaper converges since last report. The impact of all specialty crop automation research project is very significant. It could help specialty growers to achieve their production goal of increasing the yield through more efficient production management and implementation. For example, the shake and catch harvesting technology study could potentially help to reduce human labor dependency at least 50% through increasing the harvest productive while controlling the bruising rate of harvested fruit at an acceptable level for fresh consumption market. Similarly, the robotic harvest solution being studied in this project, if been fully developed and proved, has the potential to achieve a 25% cheaper than currently available industrial robotic arms while meeting necessary design specifications to harvest apples and other similar fruit crops. Human-machine collaboration in apple identification have led to identification accuracy of >95% in both day and night time operation, which is an acceptable level for commercial robotic harvesting. Integration of robotic arm, end-effector and vision system is underway. It could possibly achieve the goal of reducing the field labor force in apple harvesting by 80% while reducing or maintaining the harvest time and cost around the same level. Reduced labor use will also proportionally reduces the hazards to worker and insurance claims for the industry.

What do you plan to do during the next reporting period to accomplish your project goals?
WSU team will continue to work on all involved objectives of this multi-state project, through technology development, research prototype design, fabrication, and field validation tests, as well as the studies on technology adoption and economic models.

Arizona

None to report.

California:

Farangis Khosro, A., Rehal, R., Vougioukas, S. (2015). A Low-Cost, Efficient Strawberry Yield Monitoring System. ASABE Annual Intl. Meeting; Paper Number 152189408, New Orleans, USA.

Georgia:

Chugunov, S. and C. Li. 2015. Monte Carlo simulation of light propagation in healthy and diseased onion bulbs with multiple layers. Computers and Electronics in Agriculture. 117: 91-101. DOI:1016/j.compag.2015.07.015.

Xu, R., F. Takeda, G. Krewer, and C. Li. 2015. Measure of mechanical impacts in commercial blueberry packing lines and potential damage to blueberry fruit. Postharvest Biology and Technology. DOI: 10.1016/j.postharvbio.2015.07.013.

Wang, W. and C. Li. 2015. A multimodal machine vision system for quality inspection of onions. Journal of Food Engineering. DOI: 10.1016/j.jfoodeng.2015.06.027.

Konduru, T., G. Rains, and C. Li*. 2015. Detecting sour skin infected onions using a customized gas sensor array. Journal of Food Engineering. 160: 19-27. DOI: 10.1016/j.jfoodeng.2015.03.025.

Chugunov, S. and C. Li. 2015. Parallel implementation of inverse adding-doubling and Monte Carlo multi-layered programs for high performance computing systems with shared and distributed memory. Computer Physics Communications. DOI: 10.1016/j.cpc.2015.02.029.

Xu, Rui; Li, Changying. 2015. Development of the Second Generation Berry Impact Recording Device (BIRD II). Sensors 15, no. 2: 3688-3705.

Konduru, T., G. Rains, and C. Li. 2015. A customized metal oxide semiconductor-based gas sensor array for onion quality evaluation: system development and characterization. Sensors. 15, 1252-1273.

Hawaii:

Gautz, L. D.; Li, R and Bittenbender, H. C. Preparing Kava: Optimizing kavalactone extraction in water. Proceedings of Kava Science Conference, Jul 25-26, 2015, Chaminade University of Honolulu, 3140 Waialae Ave, Honolulu HI 96816

Iowa:

Darr, M. J., H. Herman, and D. Corbett. 2013. Yield Measurement and Base Cutter Height Control for a Harvester. Patent Application Serial No. 14/527,152. Publication No. US 2015/0124054 A1.

Hanna, H.M., B. L. Carlson, B. L. Steward, and K. A. Rosentrater. 2015. Evaluation of multi-row covers and support structure for cantaloupe and summer squash. ASABE Paper No. 152182687. St. Joseph, Mich.: ASABE.

Gai, J., L. Tang, and B. L. Steward. 2015. Plant recognition through the fusion of 2D and 3D images for robotic weeding. ASABE Paper No. 152181371. St. Joseph, Mich.: ASABE.

Schramm, M. W., H.M. Hanna, M.J. Darr, S.J. Hoff, and B. L. Steward. 2015. Measuring surface wind velocity changes. ASABE Paper No. 152182041. St. Joseph, Mich.: ASABE.

Polk, D.N., K. A. Rosentrater, H. M. Hanna, B. L. Steward. 2015. Factors affecting cucurbit production. ASABE Paper No. 152184825. St. Joseph, Mich.: ASABE.

Steward, B. L., L. Tang, and S. Han. 2015. A design framework for off-road equipment with automation. In Proc. of the 2015 Conference on Autonomous and Robotic Construction of Infrastructure, Ames, Iowa, June 2-3, pp. 180-196.

Han, S. F., B. L. Steward, and L. Tang. 2015. Intelligent Agricultural Machinery and Field Robots. In Precision Agriculture Technology - Past, Present, and Future. CRC Press: Boca Raton, Florida, USA.

Bao, Yin; Tang, Lie; Schnable, Patrick S.; and Salas Fernandez, Maria G. 2015. GPU-based Parallelization of a Sub-pixel Highresolution Stereo Matching Algorithm for Highthroughput Biomass Sorghum Phenotyping. ASABE Paper No. 152188089. St. Joseph, Mich.: ASABE

Lu, H., L. Tang, S. A. Whitham. 2015. Development of an automatic maize seedling phenotyping platform using 3D vision and industrial robot arm. ASABE Paper No. 152189844, St. Joseph, MI.

Lu, Hang. 2015. Development of a Robotic Platform for Maize Functional Genomics Research. Graduate Theses and Dissertations. Iowa State University.

Kentucky:

Precision in Practice. Successful Farming. April 2015. Research on the location accuracy of common mobile devices featured in a nationwide farm industry magazine. (Note: This is one of the largest farm magazines. Not a research publication.)

Michigan:

Donis-González, I.R., Guyer, D.E, Kavdir, I, Shahriari, D., and Pease, A. 2015. Development and applicability of an agarose-based tart cherry phantom for computer tomography imaging. J. Food Measurement and Characterization. 9:290-298. DOI 10.1007/s11694-015-9234-7.

Rady, A.M., Guyer, D.E. 2015. Evaluation of Sugar Content in Potatoes Using NIR Reflectance and Wavelength Selection Techniques. Postharvest Biology and Technology. 103:17-26.

Rady, A.M., Guyer, D.E., Lu, R. 2015. Evaluation of Sugar Content of Potatoes Using Hyperspectral Imaging. Journal of Food Bioprocess and Technology. 8(5):995-1010.

Donis-González, I.R., Guyer, D.E., Chen R., & Pease, A. 2015. Evaluation of undesirable fibrous tissue in processing carrots using Computed Tomography (CT) and structural fiber biochemistry. J. of Food Engineering. 153:108-116

Rady, A.M., Guyer, D.E. 2015. Utilization of Visible/Near-Infrared Spectroscopic and Wavelength Selection Methods in Sugar Prediction and Potatoes Classification. Journal of Food Measurement and Characterization. Vol 9:Issue1:20-34

Rady, A.M., Guyer, D.E. 2015. Rapid and/or Non-Destructive Quality Evaluation Methods for Potatoes: A Review. Computers and Electronics in Agriculture 117:31-48.

Rady, A.M. 2015. Evaluation of physiological status of potato tubers using spectroscopic and hyperspectral imaging systems. PhD Dissertation, Michigan State University.

Penn state:

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.

Washington:

US Patent: De Kleine, M., Ye, Y. and Karkee, M. (2015). Harvesting machine for formally trained orchards. US Patent. Application Filed.

Journal Articles

Ampatzidis, I., Vougioukas, S.G., Whiting, M.D., & Zhang, Q. (2014). Applying the machine repair model to improve efficiency of harvesting fruit. Biosystems Engineering. 120: 25-33.

Cui, D., Zhang, Q., Li, M., Slaminko, T., & Hartman, G.L. (2014). A method for determining the severity of sudden death syndrome in soybeans. Transactions of the ASABE. 57(2): 671-678.

Kang, F., Li, W., Pierce, F., & Zhang, Q. (2014). Investigation and improvement of targeted barrier application for cutworm control in vineyards. Acta Horticulturae. 57(2): 381-389.

Karkee, M., Adhikari, B., Amatya, S., & Zhang, Q. (2014). Identification of pruning branches in tall spindle apple trees for automated pruning. Computers and Electronics in Agriculture. 103: 127-135.

Li, L., Peters, R.T., Zhang, Q., Zhang, J., & Huang, D. (2014). Modeling apple surface temperature dynamics based on weather data. Sensors, 14: 20217-20234.

Ma, S. M. Karkee, P.A. Scharf, and Q. Zhang, 2014. Sugarcane harvester technology: a critical overview. Applied Engineering in Agriculture, 30(5): 727-739.

Shao, Y., Tan, L., Zeng, B., & Zhang, Q. (2014). Canopy pruning grade classification based on fast Fourier transform and artificial neural network. Transactions of the ASABE. 57(3): 963-971.

Silwal, A., Gongal, A., Karkee, M. (2014). Apple Identification in Field Environment with Over-The- Row Machine Vision System. Agricultural Engineering International: Agric Eng Intl (CIGR Journal), 16(4): 66-75.

Zhou, J., He, L., Zhang, Q., & Karkee, M. (2014). Effect of excitation position on fruit removal efficiency and damage in mechanical harvesting of sweet cherry. Biosystems Engineering. 125: 36-44.

Conference Papers

Davidson, J.R., Mo, C., Silwal, A., Karkee, M., Li, J., Xiao, K., Zhang, Q., Lewis, K. (2015). Human-Machine Collaboration for the Robotic Harvesting of Fresh Market Apples. IEEE International Conference on Robotics and Automation (ICRA) Workshop on Robotics in Agriculture. Seattle, WA.

Davidson, J.R., Mo, C. (2015). Mechanical Design and Initial Performance Testing of an Apple-Picking End-Effector. ASME International Mechanical Engineering Congress and Exposition. Houston, TX. Accepted for publication

Gongal, A., Amatya, S., Karkee, M., Zhang, Q., & Lewis, K.M. (2014). Identification of Repetitive Apples for Improved Crop-Load Estimation with Dual-Side Imaging. 19th World Congress of the International Federation of Automatic Control.

Silwal, A., Gongal, A., Karkee, M. (2014). Apple Identification in Field Environment with Over-The- Row Machine Vision System. Proceedings of the 6th Automation Technology for Off-road Equipment Conference (ATOE); 15-19 September; Beijing, China.

Silwal, A., Karkee, M., Zhang, Q. (2015). A hierarchical approach of apple identification for robotic harvesting. American Society of Agricultural and Biological Engineers (ASABE) annual international meeting, Paper #: 152167504; 26-29 July; New Orleans, USA.

Ye,Y., L. Yu, Q. Zhang. 2014. Wheel-slip control on an intelligent “bin-dog” system in natural orchard environments. In proc. of 6th Automation Technology for Off-road Equipment Conference (ATOE). September 16-19, 2014, Beijing, China.

Zhang, Y., Ye, Y., Wang, Z., Taylor, M.E., Hollinger, G.A. and Zhang, Q. (2015). Intelligent In-Orchard Bin-Managing System for Tree Fruit Production. In: Proceedings of the Robotics in Agriculture workshop (ICRA), May 2015, Seattle, WA.

Other Publications

Book Review: Zhang, Q. (2014). Book Review: Precision Agriculture for Grain Production Systems. Computers and Electronics in Agriculture, 100, 159.

Student Theses/Dissertations: Published

DeKleine, M. (2014). Semi-automated End-effector Concepts for Localized Removal and Catching of Fresh-market Apples in Fruiting Wall Orchards. PhD Dissertation, Washington State University.

Gongal, A. (2014). Improved Apple Crop-load Estimation with an Over-the-Row Machine Vision System. MS Thesis, Washington State University.

Zhang, J., (2014). Development and Application of a Novel System for Measuring Canopy Light Interception in Planar Orchards. Washington State University.

Zhou, J., (2014). Vibratory Harvesting Technology Research for Fresh Market Sweet Cherry. Washington State University.

Zhang, Y. (2015). Multi-Robot Coordination: Applications in Orchard Bin Management and Informative Path Planning. MS Thesis, Oregon State University.