Brief Summary of Minutes of Annual Meeting

The annual meeting started (9/14/17) with a visit to Hollabaugh Orchards near Biglerville, PA and tour of high density apple and pear plantings. Next, the group visited Rice Fruit Company and witnessed the newest commercial technologies for fruit sorting, packing, and storage; discussion of engineering research needs was also undertaken. After lunch the group visited Mt. Ridge Farms and discussed with growers who successfully use GPS-guided planters, real-time weather/pest alert systems, and mechanical blossom thinning equipment. The last stop was the Penn State Fruit Research and Extension Center for an orchard tour and demonstration of rules-based approach to pruning tall spindle apple plantings. Discussion of implications for labor efficiency and automation systems concluded the working day.

The venue of the business meeting (9/15/17) was Penn State Fruit Research and Extension Center. The Members present are listed above, in the participants list, in alphabetical order. The meeting was called to order at 8:45 am by Stavros Vougioukas. Qin Zhang moved to start the business meeting before state reports; Paul Heineman seconded and the motion passed. Qin Zhang gave a brief introduction of the project and went over the main tasks that needed to be accomplished during the meeting. He also gave some guidelines on reporting. Renfu Lu made a brief announcement about an ASABE-IEEE symposium - planning conference regarding the area of Smart Agriculture. The event was planned to take place at Michigan State, Dec 3-5, 2017. Discussion followed and the consensus was that IEEE and other new entrants seem to be duplicating research and technologies developed over 20 years ago, and that agricultural and biosystems engineers need to inform IEEE leadership about the current frontiers of technology. Next, Annual Reporting requirements and specifications were discussed. Stavros Vougioukas will collect station reports and compile them into one report that will be sent to Qin Zhang; he will review and submit to NIFA. The next agenda item was the renewal of the proposal. Our committee is five years old and needs to submit a renewal proposal by the end of this year. A proposal writing committee was formed. Filip To volunteered to lead. Committee members are: Paul Heineman, Charlie (Changying) Li, Stavros Vougioukas, and Daeun Choi. Future project objectives were discussed and there was general agreement that the current objectives are acceptable. Some discussion was initiated on new objectives, and it will be continued during proposal drafting. New Committee Officers were elected. Next Chair will be Renfu Lu. He is not an active member, but Qin Zhang will make sure he is added so he can serve as chair. Joe Dvorak was voted as next Vice-Chair. Paul nominated Irwin Donis-Gonzalez for next Secretary; Jim seconded and the motion was passed unanimously. Next, the people who will head the station reports were determined and next venue was discussed. Filip To volunteered to host the next meeting in mid-September, 2018 at Mississippi State University, where sweet potato is the main specialty crop. A move to adjourn was made by Loren at 12:13 and seconded by Amy Tabb. The motion was passed and the meeting was adjourned.

Short-term Outcomes: 

(AZ)

Use of automated in-row weeding technologies was found to reduce hand weeding labor requirements by roughly 30%. Results from testing of novel weeding spray assembly showed that targeting accuracy for all treatments was better than ± 2.0 mm and ± 1.6 mm in the longitudinal and lateral directions respectively. Average percent coverage of the target areas was 59.3% and off-target drift was 4.9%. Results exceeded the established success criteria of delivering spray at the centimeter level scale of accuracy with < 5% off target spray. 

(CA)

By adopting a fully automated tomato juice inspection system developed at UC Davis, workers were no longer required to manually lift and invert 10-pound stainless steel containers of tomato juice for each truckload of tomatoes inspected. This represents a potential reduction of 1,000 repetitive motion hazards per day for those individuals. 

(FL)

A deep-learning machine vision system for evaluation of harvested citrus fruit quality yielded detection accuracies of 100%, 89.7%, 94.7%, and 88.9% for healthy, Huanglongbing, rust mite and wind scar, respectively. 

(HI)

The data showed that a small handheld NIR spectrometer could be used in the field to measure water stress in coffee. Also, a system that was developed to conduct small (100-1000 g) lot fermentation of cacao gave repeatable results in chocolate quality by tree without the need of large mass of cacao seed. 

(IA)

Investigations into the deployment of row covers for cucurbit pest exclusion and bacterial wilt spread reduction showed that burying the cover material was better at excluding insects than anchoring the cover material. Structurally, conduit hoops kept the material from sagging and resulted in the least amount of damage. 

(MI)

Major improvements were made in the bin filler design and automatic handling system for a new self-propelled apple harvest and infield sorting prototype machine. 

(PA)

The development of heuristic rules to accommodate robotic pruning showed that limb to trunk ratio worked well for setting severity determined using maximum limb diameter, and that removing next largest branch to threshold makes up for ¾ of the required pruning. A two-person N.M. Bartlett Chariot orchard platform equipped with a small harvest-assist device was evaluated to be better suited for hilly US orchards than the original platform without the unit. 

(WA)

Preliminary results from the evaluation of a robotic apple harvesting system showed that: 5DOF (instead of 8 DOF) don’t compromising the workspace of the robot; fruit picking and catching could be completed in about 5 s using a 5 degree-of-freedom (DOF) robotic system. Results from field tests of a targeted shake-and-catch apple harvesting system showed that fruit detachment and collection efficiency were higher with shorter branch length when the branch size is similar. Fruit detachment and collection rates of 90% or more could be achieved in modern, formally trained orchard. It was also shown that shake-and-catch harvesting showed promise for faster and potentially low cost harvesting of apples for fresh market consumption. However, the method tends to show varietal dependence with Fuji and Jazz showing higher removal efficiency and better quality fruit while varieties like gala and honey crisp suffering from either low removal efficiency, low fruit quality or both. Preliminary trajectory tracking tests for a robotic weed control machine showed that linear, sinusoidal and circular paths could be followed with a maximum position error of 2.2 cm at a driving speed of 0.10 m·s-1 which met the required performance for precise herbicide application on WA organic vegetable fields. Results from preliminary field evaluation tests of a red raspberry cane bundling and tying machine showed the developed bundling mechanism was successful for over 90% of times whereas the success percentage for combined bundling and tying mechanisms was about 84%. It was also found that the performance was variety dependent (Meeker showing a greater promise than Wakefield). Early results from multiple field trials of a preliminary bird identification and deterrence drone-based system showed that drones flown by hand could successfully deter birds at a very high rate. 

Outputs:

A total of fifty five (55) journal publications were produced by all stations in the past year. Hardware, software, data and report outputs are presented next for each participating station. 

(AZ)

The results of precision weeding research were disseminated (presentations, articles) by making presentations at meetings (3), hosting field days (1), giving demos (4) and working with journalist to publish popular press articles (3). An outreach effort was made to educate stakeholders about the feasibility of using commercially available robotic cultivators in U.S. vegetable production. The outreach effort comprised making presentations at meetings on the findings from trials conducted as part of this project (3), hosting field days (2), conducting on-farm demonstrations (5) and publishing popular press articles (2). 

(CA)

Three peer-reviewed journal papers were published and eight conference papers were presented. Data from forty digitized trees and fruits (pears and cling-peaches) were generated. An instrumented fruit picking bag was developed and calibrated (hardwar and sofware). A fully automated tomato juice inspection system developed (hardware and software). 

(FL)

A machine vision system (hardware and software) was developed to evaluate the quality of harvested citrus fruit. An image fusion method (software) was developed for color and thermal images to detect immature green citrus fruit on the tree. 

(HI)

Two publications were made. A thesis on leaf stress measurement and a journal article on small lot fermentation system for variety evaluation. A system (hardware) for small (100-1000 g) lot fermentation of cacao seed was developed. 

(IA)

A 3D machine vision system (hardware and software) was developed to recognize and determine the location of specialty crop plants, specifically, broccoli and lettuce. An intra-row weeding actuator (hardware) was built and a controller (software) was designed for it to follow a trajectory around vegetable crop plants. Test results (report) comparing spray coverage against changes in boom height and carrier application rate were generated for dynamic pulse width modulated nozzle systems.

(KY)

Computer software for routing tractors and UASs was developed. Simulations were verified with tractors in agricultural fields and UAS flying over fields (articles). 

(MI)

A multichannel hyperspectral imaging probe for measuring optical properties and quality attributes of tomatoes and peaches (hardware and software).

A structured-illumination reflectance imaging (SIRI) technique (software) for detection of defects in fruit. Quantitative comparison between conventional versus over-the-row harvest systems for tart cherries (data). 

(PA)

Heuristic rules for robotic pruning were developed and evaluated (articles). Harvest-assist device for small operations was developed (hardware). 

(WA)

A robotic apple harvesting system was developed that included a dual robot collaboration mechanism for fruit detachment and catching and an end-effector with novel smart soft material (hardware, software, articles). A targeted shake-and-catch apple harvesting system was designed and fabricated (hardware). A prototype robotic weed control machine for vegetable crops was designed and fabricated (hardware and software). A prototype robotic red raspberry cane bundling and tying machine was designed and fabricated (hardware and software). 

Activities:

(AZ)

Work continued to develop a precision weeding machine for controlling intra-row weeds at the centimeter level scale of accuracy. In FY17, a second spray assembly for precision weeding was developed. The unit comprises a high-speed solenoid valve and a custom-built nozzle body with a straight, thru-hole orifice. The assembly was evaluated at travel speeds ranging from 1-2 mph and nozzle tip to target distances of 3-7 inches. 

A significant effort was made to identify a solenoid valve/nozzle combination that would deliver fluorescent marking paint to lettuce seedlings. Application of marking paint facilitates crop/weed differentiation. Over 25 combinations were evaluated in the lab. The best performing assemblies were tested in the field. Although the assemblies provided spray patterns that measured roughly 2x2” with minimal off target spray, results showed, that improvements in targeting accuracy and spray coverage are needed in order for the imaging system to reliably detect marked crop plants. 

A project was initiated to develop a multispectral imaging system for detecting fecal matter on leafy greens. Study results showed that imaging using relatively inexpensive components could provide the basis for detection of fecal contamination in produce fields if surveys were conducted during dawn or dusk, or at night. 

(CA)

Engineering research design, development, deployment, and scientific assessment activities for the creation of new, novel smart machines for a reduction in menial labor requirements, and an increase in method objectivity and automation for quality characterization and food safety assessment of fruits and vegetables were conducted by the research team in the Slaughter lab. 

In the Vougioukas lab, cling peach and pear trees were digitized and 3D models were created. Simulation experiments were conducted using these 3D models to investigate the picking efficiency and throughput of multi-arm robotic harvesters. A Linear Mixed Model was developed and trained to predict the picking time in manual strawberry harvesting. The dataset consisted of 161 picking times from 18 workers and was collected in strawberry fields in Salinas, CA. A standard fruit-picking bag was retrofitted to measure the weight of manually harvested fruits in real time. Two load cells, electronics, and software running on a microcontroller were integrated in a custom-made enclosure. 

Research was conducted by the research team in the Donis-Gonzalez lab at the University of California, Davis to assess commercially available portable, non-invasive produce quality spectrometers.

(FL)

An image fusion method was developed for color and thermal images to detect immature green citrus fruit on the tree. The method utilized photogrammetry and bundle adjustment to calibrate relative orientations of the cameras. A machine vision system was developed to evaluate the quality of harvested citrus fruit. A graphical processing unit (GPU) and a deep learning technique were used to process videos of the fruit on a conveyer system. Another study was initiated to detect strawberry flowers in outdoor images under natural illumination conditions. 

(HI)

A scientific study was conducted to develop a calibration using near infrared spectroscopy and capacitance to determine coffee leaf water stress as measured by pressure bomb method. A system was developed to conduct small (100-1000 g) lot fermentation of cacao seed. 

(IA)

Robotic solutions were developed to automate the process of plant phenotyping traits extraction. 3D machine vision technology was developed to recognize and determine the location of specialty crop plants, specifically, broccoli and lettuce. This technology is being incorporated into an automated mechanical weeding system which will assist growers, particularly of organic crops, to efficiently tackle the problem of weed plant growing in crops. 

Research was initiated into automatic control of an intra-row weeding actuator. The controller is designed to guide the actuator to follow a trajectory around vegetable crop plants. Model predictive control (MPC) was investigated in as an alternative to classical control approaches. Field research was conducted that investigated the drift of larger spray droplets in-situ at varying wind speeds. Several investigations into the deployment of row covers were completed in summer of 2017 for cucurbit pest exclusion and bacterial wilt spread reduction. Various methods can be used to sealing row cover perimeters and prevent cucumber beetles from entering rows. Investigations into the effectiveness of four different methods of perimeter sealing were tested at three different locations. In addition, different methods of anchoring the row cover material as well as support structures were all investigated as well. Research was conducted that quantified the consistency of herbicide placement in dynamic pulse width modulated nozzle systems. This technology was investigated through replicated machine trials and on-farm research. Herbicide placement distribution was quantified using large ground targets and an automated digital image analysis tool which measured the uniformity of spray coverage. Testing compared spray coverage against changes in boom height as well as carrier application rate. 

(KY)

Computer simulations of machinery routing in hundreds of scenarios (field shape, vehicle to field size ratios) have been carried out. Simulations were verified with tractors in agricultural fields and UAS flying over fields. 

(MI) USDA/ARS

A new multichannel hyperspectral imaging probe was measured and evaluated for measuring optical properties and quality attributes of tomatoes and peaches. The new probe allows acquiring 30 spectra of 550-1,650 nm from samples of flat or curved surface. Calibration procedures were developed, which enable the probe to measure optical absorption and scattering properties.

Further progress was also made in the development of structured-illumination reflectance imaging (SIRI) technique for defect detection of fruit. Algorithms were developed for extracting direct, alternate and phase component images from the acquired SIRI images. Good progress was made on further development of apple harvest and infield sorting technology. The concept of canopy shaking harvest this year continued to be focused on fruit quality comparison between conventional versus over-the-row (OTR) harvest systems but with a more quantitative approach this year rather than a qualitative approach. For 2017, a small instrumented sphere, developed at the U. of Georgia BAE Dept., (W2009 Collaborator) capable of measuring impact forces was hung within tree systems and “harvested” along with fruit. Impacts in severity and number were measured under multiple harvester and production systems. Fruit and the instrumented sphere were also dropped at controlled heights and the fruit was assessed for damage to calibrate the impacts to damage occurring in the orchard harvests. Data is currently being analyzed from the 2017 season. Additionally, trial dwarfing plant growth plots being developed for the purpose of OTR production were evaluated for yield and canopy harvest compatibility. Harvest was conducted with a first generation commercial OTR canopy spindle shaker. This OTR system was compared against two conventional double-incline trunk shaking system in two locations. The OTR harvester was tested in multiple dwarfing training systems. A commercial equipment manufacturer continued collaboration with the harvest evaluation studies. Finally, Computed tomography (CT), often 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 current commercial technology. 

(PA)

Rules developed for robotic pruning based on heuristics. Limb to trunk ratio worked well for setting severity determined using maximum limb diameter. Removing next largest branch to threshold is ¾ of the required pruning. 

Harvest-assist device for small operations was redesigned by undergraduate student design team, to fit on N.M. Bartlett Chariot two-person platform. This platform was better suited for hilly US orchards than the original platform. The unit was field tested in apple orchards in Fall 2017. 

(WA)

A robotic apple harvesting system has been designed and fabricated. It included the development of a dual robot collaboration mechanism for fruit detachment and catching. The fruit picking end-effector has also been improved using a novel smart soft material. Laboratory and field test data have been acquired to evaluate the performance of the developed system in terms of fruit detachment efficiency and fruit harvesting speed. The parallel operation of various degrees of freedom for picking-and-catching was investigated to increase harvesting speed. 

A targeted shake-and-catch apple harvesting system has been designed and fabricated. A series of field tests was completed to evaluate fruit detachment efficiency, fruit collection efficiency and fruit damage percentage different pruning treatments. 

One field prototype robotic weed control machine for vegetable crops has been designed and fabricated. Also, one prototype robotic red raspberry cane bundling and tying machine has been designed and fabricated. A preliminary bird identification system was fielded which could be used to help autonomously control drones for bird deterrence from high-value fruit. 

Milestones:

(AZ) Spray assembly for targeted weeding exceeded the established success criteria of delivering spray at the centimeter level scale of accuracy with < 5% off target spray. 

(CA) Deployment of smart machines developed in the Slaughter lab for fully automated inspection of tomato juice for economic value, food quality, and food safety, reached 25% of the long-term target of full adoption by the processing tomato industry in 2017. Fourty trees were digitized during the summer of 2017. 

(FL) Vision-based yield detection accuracies achieved rates of 100%, 89.7%, 94.7%, and 88.9% for healthy, Huanglongbing, rust mite and wind scar, respectively. 

(HI) System for small (100-1000 g) lot fermentation of cacao was completed and produced repeatable results. 

(IA) 3D machine vision prototype for plant recognition and localization was developed and tested, resulting in a milestone toward automated mechanical weeding systems. Different methods of anchoring row cover material and support structures were investigated as milestone result for cucurbit pest exclusion. Herbicide placement distribution testing was performed as a major step in the study of consistency of herbicide placement in dynamic pulse width modulated nozzle systems. 

(KY) Field verification of optimal routing for field coverage by agricultural machinery was completed. 

(MI) A hyperspectral imaging probe was calibrated as a milestone in measuring optical properties and quality attributes of tomatoes and peaches. Algorithms for extracting direct, alternate and phase component images from structured-illumination reflectance images were developed as a milestone in detection of fruit defects. Tart cherry shake-and-catch experiments were performed using an instrumented sphere and new dwarfing plants as a milestone in developing advanced over-the-row harvesters. 

(PA) Development of harvest-assist device for small orchard platforms was completed thus enabling the testing of such platforms’ efficiency. Rules were developed for robotic pruning based on heuristics, as an important milestone to the further development of such robotic systems. 

(WA) One robotic bin-handling machine has driven fully autonomously in actually managing fruit bins for over 40 km in WA commercial orchards.

ARIZONA

Lefcourt, A.M. & Siemens, M.C. Interactions of insolation and shading on ability to use fluorescence imaging to detect fecal contaminated spinach. Applied Sci. 7 1041; doi:10.3390. 

CALIFORNIA

Shiu, J.W., D.C. Slaughter, L.E. Boyden, D.M. Barrett. 2016. Correlation of Descriptive Analysis and Instrumental Puncture Testing of Watermelon Cultivars. J. food Sci. 18(6):S1506-S1514. 

Doan, H.K., K. Perez, R.M. Davis, and D.C. Slaughter. 2016. Survey of Molds in California Processing Tomatoes. J. of Food Science. 81(11): 2785-2792. 

Xiaotuo, W., G.G. Atungulu, R. Khir, G. Zhenjiang, P. Zhongli, S.A. Wilson, G. Olatunde, and D. Slaughter. 2017. Sorting in-shell walnuts using near infrared spectroscopy for improved drying efficiency and product quality. International Agricultural Engineering Journal. 26(1): 165-172. 

Vougioukas, S.G., Arikapudi R., Munic, J. (2016). A Study of Fruit Reachability in Orchard Trees by Linear-Only Motion. Journal: IFAC-PapersOnLine, 49(16), pp.277-280. https://doi.org/10.1016/j.ifacol.2016.10.051. 

Arikapudi, R., Vougioukas, S.G., Jiménez- Jiménez, F., Farangis Khosro Anjom, F. (2016). Estimation of Fruit Locations in Orchard Tree Canopies Using Radio Signal Ranging and Trilateration. Computers and Electronics in Agriculture (125): 160-172. http://dx.doi.org/10.1016/j.compag.2016.05.004. 

Vougioukas, S.G., He, L., Arikapudi, R. (2016). Orchard Worker Localisation Relative to a Vehicle Using Radio Ranging and Trilateration. Biosystems Engineering (147): 1-16. http://dx.doi.org/10.1016/j.biosystemseng.2016.03.006 

FLORIDA

Pourreza, A., W. S. Lee, E. Czarnecka, L. Verner, and W. Gurley. 2017. Feasibility of using the optical sensing techniques for early detection of Huanglongbing in citrus seedlings. Robotics 6(11). Doi:10.3390/robotics6020011. 

Shuaibu, M., W. S. Lee, Y. K. Hong, and S. Kim. 2017. Detection of apple Marssonina blotch disease using particle swarm optimization. Trans. ASABE 60(2): 303-312. 

Khedher Agha, M. K., W. S. Lee, C. Wang, R. W. Mankin, A. R. Blount, R. A. Bucklin, and N. Bliznyuk. 2017. Detection and prediction of Sitophilus oryzae infestations in triticale via visible and near infrared spectral signatures. Journal of Stored Products Research 72: 1-10. 

Khedher Agha, M. K., R. A. Bucklin, W. S. Lee, R. W. Mankin, and A. R. Blount. 2017. Effect of drying conditions on triticale seed germination and weevil infestation. Trans. ASABE 60(2): 571-575. 

Barocco, R., W. S. Lee, and G. Hortman. 2017. Yield mapping hardware components for grains and cotton using on-the-go monitoring systems. UF/IFAS EDIS AE518. http://edis.ifas.ufl.edu/ae518. 

HAWAII

Provera, M. 2016. Determination of the Water Content of Coffee Leaves using Infrared Spectroscopy. University of Hawaii at Manoa, Biological Engineering, Honolulu, HI. 

Bittenbender, H. C., L. D. Gautz, E. Seguine, and J. L. Myers. 2017. Microfermentation of Cacao: The CTAHR Bag System. Horttechnology 27(5):5. 

IOWA

Fernandez, M. G. S., Bao, Y., Tang, L., & Schnable, P. S. (2017). A high-throughput, field-based phenotyping technology for tall biomass crops. Plant Physiology, pp-00707. 

Felizardo, K. R., H. V. Mercaldi, P. E. Cruvinel, V. A. Oliveira, and B. L. Steward. 2016. Modeling and model validation of a chemical injection sprayer system. Applied Engineering in Agriculture 32(3): 285-297. doi: 10.13031/aea.32.10606. 

Lu, H., L. Tang, S. A. Whitham, Y. Mei. 2017. A Robotic Platform for Corn Seedling Morphological Traits Characterization. Sensors 17(9), 2082. doi:10.3390/s17092082 

Li, J., L. Tang. 2017. Developing a low-cost 3D plant morphological traits characterization system. 2017. Computers and Electronics in Agriculture, 143:1-13. https://doi.org/10.1016/j.compag.2017.09.025. 

Li, J., L. Tang. 2017. Crop Recognition under weedy conditions based on 3D imaging for robotic weed control. Journal of Field Robotics. DOI: 10.1002/rob.21763. 

Bao, Y.*, L. Tang, D. Shah. 2017. Robotic 3D Plant Perception and Leaf Probing with Collision-Free Motion Planning for Automated Indoor Plant Phenotyping. ASABE Paper No. 1700369. St. Joseph, Mich.: ASABE. doi: 10.13031/aim.201700369 

Herzberg, R. L., H. M. Hanna, B.L. Steward, and K. A. Rosentrator. 2017. Assessment of the mechanization of row covers for cucurbit crops. ASABE Paper No. 162460814. St. Joseph, Mich.: ASABE. DOI: 10.13031/aim.201700686 

Villibor, G. P., B. L. Steward, G. R. Luecke, D. M. Queiroz, L. Tang, S. Kshetri. 2017. Vibrations levels assessment of a robotic intra-row weeder using low-cost data acquisition system. ASABE Paper No. 1700652. St. Joseph, Mich.: ASABE. DOI: 10.13031/aim.201700652 

Kshetri, S., J. Gai, L. Tang, and B. L. Steward. 2017. Trajectory controller design for precisely positioning a mechanical weeding mechanism. ASABE Annual International Meeting, Spokane, WA. July 16-19. 

Gai, J., S. Kshetri, L. Tang, and B. L. Steward. 2017. Robotic intra-row weed control using 3D computer vision. ASABE Annual International Meeting, Spokane, WA. July 16-19. 

Breitzman, M. W., Bao, Y., Tang, L., Schnable, P. S. & Fernandez, M. G. S. 2017. High-throughput architectural traits phenotyping for association mapping. Session of Physiological Traits for High Throughput Phenotyping of Abiotic Stress Tolerance at the ASA, CSSA and SSSA International Annual Meetings, Tampa, Florida. 

Jafni Johari Jiken. 2016. Experimental approach to determine the efficacy of a tine mechanism for auto weeding machine. M. S. Thesis. Iowa State University Parks Library. 

KENTUCKY

Seyyedhasani, H., Dvorak, J. (2017). Using the Vehicle Routing Problem to Reduce Field Completion Times with Multiple Machines. Computers and Electronics in Agriculture. 134. March 2017. 142-150. http://dx.doi.org/10.1016/j.compag.2016.11.010. 

Seyyedhasani, H., Dvorak, J. S., Sama, M., Aerial Validation of a Logistics Model for Area Coverage in Agriculture. Conference Presentation. 2017 ASABE Annual International Meeting, Spokane, WA, United States. July 16-19, 2017. 

MICHIGAN

Donis-González, I.R., Guyer, D.E., Lu, R. 2016. Postharvest assessment of undesirable fibrous tissue (choking hazard) in fresh processing carrots using Vis/NIR hyperspectral images. Proceedings for 3rd International Conference on Fresh-Cut Produce: Maintaining Quality and Safety Sept. 13-18, 2015. Acta Horticulturae 1141 ISHS 2016. DOI 10.17660/ActaHortic.2016.1141.21. 

Donis-Gonzalez, I.R., Jeong, S., Guyer, D.E., Fulbright, D. 2017. Microbial contamination in peeled chestnut and the efficiency of post-processing treatments for microbial spoilage management. J. Food Processing and Preservation. Vol. 41: http://dx.doi.org/10.1111/jfpp.12874. 

Lu, Y. and Lu, R. Histogram-based automatic thresholding for bruise detection of apples by structured-illumination reflectance imaging. Biosystems Engineering 160:30-41. 2017. 

Huang, Y., Lu, R., and K. Chen. Development of a multichannel hyperspectral imaging probe for food property and quality assessment. Postharvest Biology and Technology 133:88-97. 2017. 

Zhang, Z., Pothula, A. K. and Lu, R. Economic analysis of a self-propelled apple harvest and in-field sorting machine for the apple industry. ASABE Paper No. 2456644 (DOI: 10.13031/aim.202456644), 12 pp. 2016 (Proceedings) 

Lu, R., Zhang, Z., and Pothula, A. K. Innovative technology for apple harvest and in-field sorting. Fruit Quarterly 25(2):11-14. 2017. (Industry Publications) 

Huang, Y., Lu, R., and K. Chen. Development of a multichannel hyperspectral imaging probe for food property and quality assessment. In SPIE Proceedings Vol. 9864 - Sensing for Agriculture and Food Quality and Safety VII (edited by Kim, M. S. et al.), Paper No. 98640Q, 12 pp. SPIE (The International Society for Optical Engineering), Bellingham, WA. 2016. (Proceedings) 

Lu, Y. and Lu, R. Phase analysis of three-dimensional surface reconstruction of apples using structured-illumination reflectance imaging. SPIE Proceedings Vol. 9864 - Sensing for Agriculture and Food Quality and Safety VII (edited by Kim, M. S. et al.), Paper No. 98640Q, 12 pp. SPIE (The International Society for Optical Engineering), Bellingham, WA. 2017. (Proceedings) 

Zhang, Z., Pothula, A. K., and Lu, R. Development of a new bin filler for apple harvesting and infield sorting with a review of existing technologies. ASABE Paper #201700662, 16pp. DOI: 10.13031/aim.201700662. 2017. (Proceedings) 

Huang, Y., Lu, R. and Chen, K. Nondestructive measurement of tomato postharvest quality using a multichannel hyperspectral imaging probe. ASABE Paper #201700195, 11pp. DOI: 10.13031/aim.201700195. 2017. (Proceedings) 

Lu, Y. and Lu, R. Structured-illumination reflectance imaging coupled with spiral phase transform for bruise detection and three-dimensional geometry reconstruction of apples. ASABE Paper #201700584, 15pp. DOI: 10.13031/aim.20170584. 2017. (Proceedings) 

PENNSYLVANIA

Zhang, Z., P.H. Heinemann, J. Liu, J.R. Schupp, and T.A. Baugher. 2016. Design and field test of a low-cost apple harvest-assist unit. Transactions of ASABE. 59(5):1149-1156. 

Zhang, Z., P.H. Heinemann, J. Liu, T.A. Baugher and J. R. Schupp. 2016. Development of mechanical apple harvesting technology – a review. Transactions of ASABE. 59(5):1165-1180. 

Zhang, Z., P. H. Heinemann, J. Liu, J. R. Schupp, T. A. Baugher. 2017. Brush mechanism for distributing apples in a low-cost apple harvest unit. Applied Engineering in Agriculture 33(2): 195-201. 

Zhang, Z., and P.H. Heinemann. 2017. Economic analysis of a low-cost apple harvest-assist unit. HortTechnology. 27(2):240-247. 

Schupp, J. R., H. E. Winzeler, T. M. Kon, R. P. Marini, T. A. Baugher, L. F. Kime and M. A. Schupp. 2017. A method for quantifying whole-tree pruning severity in mature tall spindle apple plantings. HortScience 52 (accepted for publication 13 July 2017). 

Baugher, T., E. Dugan, M. Basedow, T. Jarvinen, J. Schupp, E. Winzeler and M. Schupp. 2017 Competitive orchard systems and technologies. Pennsylvania Fruit News 97(2):27. 

Schupp, J., E. Winzeler and M. Schupp. 2017. Evaluation of artificial spur extinction as a potential crop load management technique. Pennsylvania Fruit News 97(1):74-76. 

WASHINGTON

Fu, H., L. He, S. Ma, M. Karkee, D. Chen, Q. Zhang, and S. Wang. 2017. “Jazz” Apple Impact Bruise Responses to Different Cushioning Materials. Transactions of the ASABE. 60(2): 327-336. 

He, L., H. Fu, D. Sun, M. Karkee, and Q. Zhang. 2017. Shake and Catch Harvesting for Fresh Market Apples in Trellis Trained Trees. Transactions of the ASABE. 60(2): 353-360. 

He, L., H. Fu, M. Karkee, and Q. Zhang. 2017. An Effect of fruit location on apple detachment with mechanical shaking. Biosystems Engineering, 157: 63-171. 

Silwal, A., J. R. Davidson, M. Karkee, C. Mo, Q. Zhang, and K. Lewis. 2017. Design, integration, and field evaluation of a robotic apple harvester. Journal of Field Robotics. 34(6): 1140-1159. 

Silwal, A., M. Karkee, and Q. Zhang. 2016. A Hierarchical approach of apple identification for robotic harvesting. Transaction of the ASABE. 59(5): 1079-1086. 

Ye, Y., Z. Wang, D. Jones, L. He, M.E. Taylor, G.A. Hollinger, and Q. Zhang. Bin-Dog: A Robotic Platform for Bin Management in Orchards. Robotics, 6(2), 2017. 

Zhou, J., L. He, M. Whiting, S. Amatya, P. Larbi, M. Karkee, and Q. Zhang. 2016. Field evaluation of a mechanical-assist cherry harvesting system. Engineering in Agriculture, Environment and Food, 9(4): 324-331. 

WEST VIRGINIA

Tabb, A. and H. Medeiros. 2017. A robotic vision system to measure tree traits. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 

Tabb, A. and K. M. Ahmad Yousef. 2017. Solving the Robot-world, Hand-eye calibration problem with iterative methods. Machine Vision and Applications. 28(5-6): 569-590.