Brief Summary of Minutes of Annual Meeting

The annual meeting was hosted by Mississippi State University (MSU) (local host: Dr. Fillip To) on September 20-21, 2018. The first day of the group’s activities mainly included field tours. The group first visited the USDA/ARS and MSU research facilities in Poplarville, where the station’s directors introduced their research programs in small fruits, and a grower gave a talk about his blue berry and tea production and processing operations. The group then toured the laboratories at the station, followed by a tour to the greenhouse facilities and the station’s blue berry breeding field. Thereafter, the group visited Lazy Magnolia brewery in Kiln, MS, touring the brewing facilities and had lunch there. In the afternoon, the group visited MSU research facilities in McNeill, MS, where the station administrator introduced the station’s research and extension programs and a horticulturist also introduced her research and extension work in urban horticulture.

In the morning of September 21, the group had an annual meeting in IP Casino Resort in Biloxi, MS, chaired by Renfu Lu. Each station (AL, CA, KY, HI, MI, MI-ARS, MS, PA, WA) first gave a brief report on their research activities and accomplishments for the past year. After the station reports, the group began discussing collaboration ideas. One concern was raised about low attendance in recent annual meetings by participating stations. It was agreed that we should encourage all project members to participate in the annual meeting and each station should send at least one member to the annual meeting. Qin Zhang agreed to provide a complete list of all station members for the new project W3009, so that the chair can reach out to all stations for next year’s annual meeting in advance. In addition, it was also mentioned that in the past several annual meetings, no NIFA program leader was present. Qin Zhang agreed to reach out to NIFA and invite a program leader to participate in the group meeting next year. The group also discussed on how to make concerted effects to acquire funding for large projects, which has not been quite successful for the group in recent years. The group then discussed the issue of data repository. There have been huge amounts of data generated in sensors and automation for specialty crops over the years by the group, but they are not shared and publicly available. It was proposed that this group needs to take leadership for creating such data repository. Dvorack (Chair for 2018-2019) agreed to work with NIFA program leader to get their support. A committee was formed, consisting of To, Karkee, Cheng, Choi and Zhang, to work on the data repository project. The group then discussed about providing independent evaluation of different commercial sensors (e.g., NIR sensors). However, concerns were raised on whether these commercial equipment manufacturers would be willing to work with us and in doing so, it may have legal implications and other concerns for such project. However, the group agreed that it would be a good idea to evaluate and test some sensors and algorithms developed by different stations. But no final recommendation was reached on this topic. In addition, it was also suggested that to develop better collaboration among the participating stations, the group may want to consider having additional activities or meetings during the year, such as holding quarterly webinars to share research progress, new topic, and discuss collaboration opportunities.

New officers for 2018-2019: Joseph Dvorak – Chair, Irwin Donis-Gonzalez – Vice Chair, Long He – Secretary. The 2019 annual meeting will be hosted by University of Kentucky (local host: Joseph Dvorak) and specific dates are yet to be decided. The meeting adjourned at noon.

Short-term Outcomes/Outputs:

(AZ)

Work continued on a multi-state SCRI project to develop a precision weeding machine for controlling intra-row weeds at the centimeter level scale of accuracy. The machine utilizes a machine vision system for plant detection and herbicidal spray to kill weeds. In FY18, a third spray assembly for precision weeding was developed. The unit comprises a high-speed solenoid valve and a custom-built nozzle body with two straight, thru-hole orifices. The design improves precision to 0.5 cm.  Formal performance trials and integration with the machine vision system are planned for the coming year. The results of this research were disseminated by making presentations at meetings (1), giving demos to various professional and student groups (3) and working with journalist to publish popular press articles (4).

A project was initiated to identify parameters for optical detection of bird excrements in leafy green produce fields.  Results showed that reflectance imaging at around 500-525 nm or 600-620 nm could be used to reliably detect excrements from the three bird species tested.  Fluorescence responses to UV illumination at 525 nm were also found to be an effective method for detecting bird droppings.  The significance of these findings it that based on the parameters identified, inexpensive imaging systems could be developed and mounted on drones or ground based vehicles and used to help prevent the fecal contaminant from entering the food supply.

(CA)

An instrumented strawberry picking cart was developed and used for yield mapping.
A prototype fruit-catching system was developed and tested.
A simulator for strawberry harvesting was developed and calibrated.

Scientific knowledge, on the feasibility and performance of new, novel instrumental methods of objective characterization of product quality, phenotypes for plant breeding, and sensing technologies for food safety and process control optimization were reported in industry meetings and scientific conferences.

Scientific knowledge, applied instrumental methods to objectively characterize product quality and digestibility.

Five peer-reviewed journal papers were published and four conference papers were presented. One Ph.D. and one M.Sc. thesis were completed.

(GA)

1) The processing pipeline of 3D point cloud data developed in this study is an effective tool for blueberry breeding programs (e.g., for mechanical harvesting) and farm management. 2) The fundamental optical properties of blueberry flesh and skin help researchers better understand light interaction with fruit tissues. The results revealed that the near infrared spectral region is an effective spectral range for inspecting bruised blueberries using either reflectance or transmittance method. These findings would provide guidance to develop non-destructive sensing methods for blueberry internal bruising detection.

(HI)

Designed downdraft, recirculating dehumidified air, 3-layer dryer for two coffee growers and a cacao grower.

(IA)

Robotic plant phenotyping: How to generate high-quality plant phenotypic data in a high-throughput fashion represents a bottleneck problem in plant breeding and genetic research. We have been developing automated robotic solutions to automate the process of plant phenotyping traits extraction.

Sensing of crop plants: Deep neural network based machine vision technology has been 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. 

Assessing mechanical weeding actuators in disturbing soil and plants: 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.  Promising controller performance was achieved by guiding the actuator as close to the crop plants as possible while not damaging them to mechanically control intra-row weed plants. 

Cucurbit pest exclusion technology: Several investigations into the deployment of row covers were completed in summer of 2018.  The goal of the row covers is to exclude insect pests from cucurbit crops and reduce the spread of bacterial wilt.  Various methods can be used to sealing row cover perimeters and prevent cucumber beetles from entering rows.  Four row cover securing and sealing methods were investigated using polyethylene netting (ProtekNet) as the row cover fabric: burying, PVC clip attachment, sand-bags placed at 5 foot and 14 ft intervals on the fabric. The method of burying the edges showed no discernable advantages over the three methods where the edges were not buried.

(MI)

Over-the-Row canopy shaking tart cherry production has demonstrated good promise to the extent several progressive growers have implemented the system on a trial basis.  Some challenges remain in fruit handling.  Computed Tomography concepts and parameters for internal fruit, vegetable, and nut quality and defect detection have been developed and further advancement lies in the demand for such systems and the economics so as to draw interest from hardware technology development entities.  Paw paw fruit were found to require near-final on-tree fruit ripening to obtain optimal flavor and texture characteristics, but they can be harvested while slightly immature if a selective harvest technique could be developed.

(MI-ARS) A multichannel hyperspectral imaging system in semi-transmittance mode was constructed for detecting internally defective apples. Results showed that combination of six spectra, each covering different sections of fruit, overall resulted in better results for classification of defective and normal apples, with the overall accuracies of as high as 96%.

Good progress has been made on the development of structured-illumination reflectance imaging (SIRI) technique as a new modality for enhanced defect detection of fruit. A fast image preprocessing algorithm, called bi-dimensional empirical mode (BEMD), was developed for removing noise and artifacts in the demodulated SIRI images. The proposed BEMD method was further implemented in conjunction with machine learning algorithms to detect both surface and subsurface defects of apples, with superior classification results (up to 98% accuracy).

SIRI also showed superior performance in early detection of disease infection in peaches.

An improved version bin filler was constructed, tested and evaluated in both laboratory and field conditions.

(PA)

Conducted strategic planning with representatives from regional horticultural associations.

The RootRobot unit was designed and under construction through DOE ARPA-E funding.  The RootRobot will automate the excavation, cleaning, and imaging of corn roots from research plots.

Initiated and conducted a new study to evaluate crop sensing in apple trees that were pruned to three levels of severity, with and without prohexadione calcium, a plant growth regulator that reduces current shoot extension growth.

Initiated and planted an intensive peach orchard, with four levels of planting density. Once established, this orchard block will be used to evaluate tree density and novel trellis design and components.

A sensor-based irrigation system was installed and tested in an apple block. Four irrigation strategies were investigated for scheduling irrigation events: evapotranspiration, crop water stress index, soil water content, and soil water potential.

The impact of various canopy depths on machine sensing performances was studied in a tall spindle orchard system. Developed sensing systems measured the size and count of apples at various times from early in the.

Various technologies for automated mushroom harvesting have been investigated. A proof of concept for harvesting robot mechanisms that are specifically designed for PA wooden bed system is being developed.

(WA)

WA team worked on fruit harvesting and handling technologies using a dual-robot system, which is expected to achieve 50% improvement in the cycle time (time for harvesting each fruit) compared to a single robot system used in the past to pick and place fruit. Another fruit harvesting approach evaluated for apple harvesting was to use a targeted shake-and-catch system. This approach showed promise for faster and potentially low cost harvesting of apples for fresh market consumption. However, the method tended 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. Field assessment tests showed that fruit detachment and collection efficiency increased with shorter branch length on similar size branches, and could reach 90% or more in modern, formally trained orchards.

For handing the harvested fruit, our team has developed and evaluated a self-propelled bin managing robot in laboratory and orchard environments. A multi-robot simulation with bin- managing robots and human pickers reduced the time to collect bins in a real-world orchard simulation by up to 30%. Our team also worked on a weeding robot, which was able to control the end-effector base at a desired level of accuracy while traveling on uneven ground causing up to 10° roll and/or pitch of the machine. The machine can follow either linear, sinusoidal or circular paths with a maximum position error of 2.2 cm at an operation speed of 0.10 m/s. In another project, the team evaluated cane bundling mechanism for red raspberries, which showed a success rate of 90% whereas the success percentage for combined bundling and tying mechanisms was ~84%. 

The team also works on soil sensing for precision management. The work advanced with the design and testing of a new trailer for electromagnetic induction (EMI) surveys in orchards, remote and proximal sensor-based, and work on sensor fusion for soil profile characterization.

WSU team also participated economic analysis of Neutral Harvest Aid System and Sensor Technologies for Fresh Market Highbush Blueberries. Cost-benefit was analyzed for 4 alternative mechanization devices for fresh market blueberries. It was found that price differential between fresh market and processing market blueberries is the main determinant to trigger adoption of mechanized technologies.

(WV-ARS)

Research centered on creating tools for autonomous shape phenotyping of plants using computer vision and robotics; outreach consisted of giving four invited talks at Phenome 2018, the Donald Danforth Plant Science Center, Michigan State University, and the University of Minnesota, as well as giving public tours.

Activities:

(CA)

Simulation experiments were conducted using 3D models of cling peach and pear trees to investigate the picking efficiency and throughput of robotic harvesters with many cartesian arms, given gripper size and arm extension constraints.

A prototype novel fruit-catch harvesting system was developed and tested to assess catching efficiency and fruit bruising. Results were promising and the design is being developed further.

A Linear Mixed Model was developed to predict the picking time in manual strawberry harvesting. Prediction errors lower than 10% were achieved.

A simulator was developed and calibrated that models crew harvest activities and robot fruit-transporting activities during strawberry harvesting. Robot scheduling algorithms were tested using the simulator.

A perception system was developed to detect trees and tree rows for autonomous orchard navigation.

A set of 15 fully automated smart machines were designed and deployed in the processing tomato industry in California that prepare and inspect tomato samples and perform disposal and sanitation tasks.

Application of commercially available produce quality spectroscopic systems, and nondestructive characterization of structural changes during in vitro gastric digestion of applies using micro Computed Tomography (CT).

(GA) 1) We developed a laser ranging sensor based 3D imaging approach to measure blueberry bush size and shape traits that are relevant to mechanical harvesting. One-dimensional traits (height, width, and crown size) had strong correlations between sensor and manual measurements, whereas bush volume showed a decreased correlation. Statistical results demonstrated that the five genotype groups were statistically different in crown size and bush shape. The differences matched with human evaluation regarding optimal bush architecture for mechanical harvesting. 2) Fundamental optical properties (absorption, reduced scattering coefficient, and scattering anisotropy) were measured for healthy and bruised blueberry tissues at the spectral range of 400–1400 nm.  We also investigated the light propagation model of blueberries using Monte Carlo multi-layered simulation. The simulation results showed that the spectral region of 400-700 nm is not effective in detecting bruises due to strong absorption and backward scattering of the blueberry skin. In contrast, the absorption effect of the skin in the near infrared range (930-1400 nm) was small, allowing light to penetrate and interact with the flesh.

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.

 (MI)

Programming continued on comparison of tart cherry quality resulting from electronic impact data collected from conventional trunk shaking and a developmental over-the-row (OTR) canopy shaking production concept.  Canopy shaking showed more impacts but the severity of impacts, and a possible total integrative analysis of energy of input to the cherry in each system resulting from harvesting could not be concluded as many impacts in both systems exceeded the maximum measurement capacity of the wireless impact sensor.

Computed Tomography-based image analysis efforts for sensing quality characteristics of commodities continues only in the capacity of sharing results with various industries looking for opportunity to detect internal characteristics not detectable by current commercial systems.  This technology is in need of advancement by dedicated hardware development beyond the capability of project.

New programming was initiated toward determining maturity indicators for harvest of paw paw fruit such that they can reach optimal quality characteristics yet be harvested at a status when low occurrence of damage will occur to the fruit. 

(MI-ARS)

Experiments were conducted on detecting internal defect of ‘Honeycrisp’ apples using the multichannel hyperspectral system.

Experiments were also carried out on using the structured-illumination reflectance imaging (SIRI) for early detection of disease infection for two varieties of peach. Preliminary tests were also conducted on real-time acquisition of SIRI images from moving apples, with promising results.

Laboratory tests were conducted using 3-D imaging technique to evaluate the performance of an improved bin filler for distributing apples in the bin.

(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 dual-robot system was developed for fruit picking and catching, which was evaluated in field conditions. The picking hand has been improved using a novel smart soft material.

A multi-layer self-propelled shake and catch harvesting platform was developed and further evaluated in 2018 harvest season.

A robotic prototype was evaluated for automated bin movement in orchards. The prototype with four wheel steering system used RTK GPS and laser sensing systems to navigate in orchards.

In another project, WSU team designed and fabricated a research prototype of self-propelled robotic weeding machine for vegetable production.

Another activity was the investigation of mechanized red raspberry cane bundling and tying. A cane tying mechanism has been designed, fabricated, and then evaluated in field operation.

WSU team also works on sensing and automation technologies with UASs. One specific project we worked on was to use UASs to deter birds from fruit crops such as wine grapes, blueberries and apples. We are also working on integrating ground and aerial imaging systems to rapidly quantify/evaluate biotic and abiotic stressors in specialty crops using hyperspectral imaging and associated data analytics methods. We have also been evaluating applicability of small-UAS-based multi-spectral sensing modules to monitor retrofitted/modified irrigation techniques. In one of the other projects, sub-surface pulse and continuous drip irrigation treatments effect on vine physiology and fruit quality are being monitored. In another project, the team is developing and automating solid set canopy delivery system for tree fruit and berry crops. This year, we also started working on Localized Sensing of Canopy and Fruit Microclimate for Real-time Management of Sunburn in Apple.

ARIZONA

Lefcourt, A.M., Siemens, M.C. & Rivadeneira, P. 2018. Optical parameters for using VIS reflectance or fluorescence imaging to detect bird excrements in produce fields. Applied Sciences (submitted)

CALIFORNIA

Arikapudi R., Vougioukas, S.G., (2018a). Estimating the Fruit Picking Throughput of a Telescopic Arm in High Density Trellised Pear Orchards. ASABE Annual International Meeting. Paper Number # 1801684 , Detroit, Michigan.

Arikapudi, R., Vougioukas, S. (2018b). A Study on Pick-Cycle-Times of Robotic Multi-Arm Tree Fruit Harvesters. Intl. Conference on Agricultural Engineering (AgEng 2018).

Durand-Petiteville, A., Le Flecher, E., Cadenat, V., Sentenac, T.,  Vougioukas, S.G. (2018). Tree detection with low-cost 3D sensors for autonomous navigation in orchards. IEEE Robotics and Automation Letters. 3(4): 3876-3883.

Jang, W.J., (2018). Investigation on the Harvest-aid Robot Scheduling Problem and the Implementation of Its Simulation Platform. M.Sc. Thesis. University of California, Davis.

Khosro Anjom, F., Vougioukas, S. G., Slaughter, D.C. (2018a). Development of a Linear Mixed Model to Predict the Picking Time in Strawberry Harvesting Processes. Biosystems Engineering. (166): 76-89.

Khosro Anjom, F. (2018b). Predictive Modeling of the Temporal Distribution of Tray-Transport Requests for Robot-Aided Strawberry Harvesting. Ph.D. Dissertation. University of California, Davis.

Peng, C.,  Seyyedhasani, H., Vougioukas, S.G., (2018). Optimized predictive dispatching of robotic harvest- aids using Multiple Scenario Approach. ASABE Annual International Meeting. Paper Number # 1801694, Detroit, Michigan.

Seyyedhasani, H., Peng, C., Vougioukas, S.G., (2018). Efficient Dispatching of a Team of Harvest-aid Ro- bots to Reduce Waiting Time for Human Pickers. ASABE Annual International Meeting. Paper Number # 1801715, Detroit, Michigan.

Jantra, C., D.C. Slaughter, P.S. Liang, S. Pathaveerat.  2017.  Nondestructive determination of dry matter and soluble solids content in dehydrator onions and garlic using a handheld visible and near infrared instrument.  Postharvest Biology and Technology.  133: 98-103.

Westwood, J.H., R. Charudattan , S.O. Duke, S.A. Fennimore, P. Marrone, D.C. Slaughter, C. Swanton and R. Zollinger.  2018.  Weed Management in 2050: Perspectives on the Future of Weed Science.  Weed Science. Volume: 66   Issue: 3   Pages: 275-285.

Perez-Ruiz, M., Brenes, R., Urbano, JM., Slaughter, DC., Forcella, F., Rodriguez-Lizana, A. 2018. Agricultural residues are efficient abrasive tools for weed control. AGRONOMY FOR SUSTAINABLE DEVELOPMENT. Volume: 38: 2 Article Number: 18.

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.

GEORGIA

Patrick, A., and C. Li. 2017. High throughput phenotyping of blueberry bush morphological traits using unmanned aerial systems. Remote Sensing. 9 (12): 1250.

Kuzy, J., Y. Jiang, and C. Li, 2017. Blueberry bruise detection by pulsed thermographic imaging. Postharvest Biology and Technology, 136 (2018): 166-177.

Gallardo, R. K., E.T. Stafne, L.W DeVetter, Q. Zhang, C. Li, F. Takeda, J. Williamson, W. Yang, R. Beaudry, W. Cline, R. Allen. 2018. Blueberry producers' attitudes toward harvest mechanization for fresh market. Hort Technology. 28.1: 10-16.

Zhang, M., C. Li, F. Takeda, and F. Yang. 2017. Detection of internally bruised blueberries using hyperspectral transmittance imaging. Transactions of ASABE, 60(5): 1-14.

Fan, S.X., C. Li, W.Q. Huang, and L.P. Chen. 2017. Detection of blueberry internal bruising over time using NIR hyperspectral reflectance imaging with optimum wavelengths. Postharvest Biology and Technology, 134:55-66.

Takeda, F., W. Yang , C. Li, A. Freivalds, K. Sung , R. Xu, B. Hu, J. Williamson and S. Sargent. 2017.  Applying new technologies to transform blueberry harvesting. Agronomy, 7: 33.

HAWAII

Bittenbender, H. C., L. D. Gautz, E. Seguine, and J. L. Myers. 2017. Microfermentation of Cacao: The CTAHR Bag System. Horttechnology 27(5):690-694.

IOWA

Conference Papers:

Gai, J., L. Tang, I. Tegeler, X.Yu. 2018. Machine vision for plant detection and localization for robotic weeding based on deep convolutional neural network. ASABE Annual International Meeting, Detroit, MI. July 29 - August 1, 2018

Bao, Y. and L. Tang. 2018. A robotized multi-sensor perception-driven indoor plant phenotyping system. ASABE Annual International Meeting, Detroit, MI. July 29 - August 1.

Bao, Y. and L. Tang. 2018. Plant architectural traits characterization for maize using time-of-flight 3D imaging. ASABE Annual International Meeting, Detroit, MI. July 29 - August 1.

Xiang, L., Y. Bao, L. Tang, and M. G. Salas-Fernandez. 2018. Automated morphological trait extraction for sorghum plants via 3D point cloud data analysis. ASABE Annual International Meeting, Detroit, MI. July 29-August 1.

Widmer, J. M., H. Mark Hanna, Brian L. Steward, and Kurt A. Rosentrater. "Improving and Testing Methods of Securing Row Cover for Organic Cucurbit Production." 2018 ASABE Annual International Meeting, Detroit, MI, July 29-August 1, 2018. Paper No. 1801263. DOI:10.13031/aim.201801263

Journal Articles:

Hanna, H. M., B. L. Steward, and K. A. Rosentrater. 2018. Evaluating row cover establishment systems for cantaloupe and summer squash. Applied Engineering in Agriculture. 34(2):355-364. https://doi.org/10.13031/aea.12217

Bao, Y., L. Tang, M. W. Breitzman, M. G. Salas Fernandez, P. S. Schnable. 2018. Field-based robotic phenotyping of sorghum plant architecture using stereo vision. Journal of Field Robotics 2018: 1-19. DOI: 10.1002/rob.21830.

Bao, Y., D. Shah, L. Tang. 2018. 3D Perception-based Collision-Free Robotic Leaf Probing for Automated Indoor Plant Phenotyping. Transaction of the ASABE 61(3).

MICHIGAN

Huang, Y., Lu, R., Xu, Y., Chen, K. 2018. Prediction of tomato firmness using a spatially-resolved multichannel hyperspectral imaging probe. Postharvest Biology and Technology. 140:18-26. 

Huang, Y., Hu, D., Lu, R., Chen, K. 2018. Quality assessment of tomato quality by optical absorption and scattering properties. Postharvest Biology and Technology. 143:78-85.

Huang, Y., Lu, R., Chen, K. 2018. Assessment of tomato soluble solids content and pH by spatially-resolved and conventional Vis/NIR spectroscopy. Journal of Food Engineering. 236:19-28. 

Li, R., Lu, Y., Lu, R. 2018. Structured illumination reflectance imaging for enhanced detection of subsurface tissue bruising in apples. Transactions of the ASABE. 61(3):809-819. 

Lu, Y., Lu, R. 2017. Non-destructive defect detection of apples by spectroscopic and imaging technologies: A review. Transactions of the ASABE. 60(5):1765-1790. 

Lu, Y., Lu, R. 2017. Development of a multispectral structured-illumination reflectance imaging (SIRI) system and its application to bruise detection of apples. Transactions of the ASABE. 60(4):1379-1389. 

Lu, Y., Lu, R. 2018. Structured-illumination reflectance imaging coupled with phase analysis techniques for surface profiling of apples. Journal of Food Engineering. 232:11-20.

Lu, Y., Lu, R. 2018. Fast bi-dimensional empirical mode decomposition as an image enhancement technique for fruit defect detection. Computers and Electronics in Agriculture. 152:314-323. 

Lu, R., Pothula, A. K., Mizushima, A., Vandyke, M. and Zhang, Z. 2018. System for sorting fruit. U.S. Patent #9,919,345.

Pothula, A., Zhang, Z., Lu, R. 2018. Design features and bruise damage evaluation of an apple harvest and infield sorting machine. Transactions of the ASABE. 61(3):1135-1144. 

Zhang, Z., Pothula, A., Lu, R. 2017. Development and preliminary evaluation of a new bin filler for apple harvesting and infield sorting. Transactions of the ASABE. 60(6):1839-1849. 

Zhang, Z., Pothula, A.K., Lu, R. 2017. Economic evaluation of apple harvest and in-field sorting technology. Transactions of the ASABE. 60(5):1537-1550.

PENNSYLVANIA

Chen, L., Karkee, M., He, L., Wei, Y., & Zhang, Q. (2018). Evaluation of a Leveling System for a Weeding Robot under Field Condition. IFAC-PapersOnLine, 51(17), 368-373.

Choi, D., & Jarvinen, T. (2018). A video processing strategy using camera movement estimation for apple yield forecasting. Proceedings of the 9th International Symposium on Machinery and Mechatronics for Agriculture and Biosystems Engineering, page 1-5, Jeju, South Korea, May 28-30, 2018.

Feng, J., & He, L. (2018). Tree canopy estimation for mechanical pruning based on 3D Lidar. NABEC Paper No. 18-054. St. Joseph, MI: ASABE.

Fu, H., Duan, J., Karkee, M., He, L., Chen, D., Sun, D., & Zhang, Q. (2018). Quantifying fruit quality affected by mechanical impact for selected apple varieties. IFAC-PapersOnLine, 51(17), 250-255.

He, L., and J. Schupp. 2018. Sensing and automation in pruning of apple trees: a review. Agron. 2018, 8, 211. http://www.mdpi.com/2073-4395/8/10/211/pdf. 18 pp.

He, L., Zhang, X., Karkee, M., & Zhang, Q. (2018). Fruit Accessibility for Mechanical Harvesting of Fresh Market Apples. ASABE Paper No. 1801007. St. Joseph, MI: ASABE.

Jarvinen, T., Choi, D., Heinemann, P., & Baugher, T. A. (2018). Multiple object tracking-by-detection for apple fruit counting on a tree canopy. 2018 ASABE Annual International Meeting, Paper No. 1801193, page 1-8.

Kon, T. M., J. R. Schupp, K. S. Yoder, L. D. Combs, and M. A. Schupp. 2018. Comparison of chemical blossom thinners using ‘Golden Delicious’ and ‘Gala’ pollen tube growth models as timing aids. HortScience 53:1143-1151.

Schupp, J.R., H.E. Winzeler, T.M. Kon, R.P. Marini, T.A. Baugher, L.F. Kime, M.A. Schupp. 2017. A method for quantifying whole-tree pruning severity in mature tall spindle apple plantings. HortScience 52:1233-1240.

Schupp, J., R. Marini and T. Baugher. 2018. Competitive Orchard Systems and Technologies. Pennsylvania Fruit News 98(1):21-23.

Schupp, J., T. Baugher and P. Heinemann. 2018. Peach Crop Load Management: Blossom Thinning and Fruit Size. Penn State Fruit Times, https://extension.psu.edu/peach-crop-load-management-blossom-thinning-and-fruit-size?j=215664&sfmc_sub=22239605&l=159_HTML&u=4146493&mid=7234940&jb=1

Schupp, J., B. Wiepz, E. Winzeler and M. Schupp. 2018. Evaluation of Artificial Spur Extinction or 6BA at bloom as Potential Crop Load Management Techniques. Pennsylvania Fruit News 98(1):24-25.

Wang, C., Lee, W. S., Zou, X., Choi, D., Gan, H., & Diamond, J. (2018). Detection and counting of immature green citrus fruit based on the Local Binary Patterns (LBP) feature using illumination-normalized images. Precision Agriculture. DOI: https://doi.org/10.1007/s11119-018-9574-5

Zhang, X., Fu, L., Majeed, Y., He, L., Karkee, M., Whiting, M. D., & Zhang, Q. (2018). Field Evaluation of Data-based Pruning Severity Levels (PSL) on Mechanical Harvesting of Apples. IFAC-PapersOnLine, 51(17), 477-482.

WASHINGTON

Chandel, A., L. R. Khot, Y. Osroosh and R. T. Peters. 2018. Thermal-RGB imager derived in-field apple surface temperature estimates for sunburn management. Agricultural and Forest Meteorology, 253-254: 132-140. https://doi.org/10.1016/j.agrformet.2018.02.013 (5-Year IF: 4.753).

Osroosh, Y., L. R. Khot, and R. T. Peters. 2018. Economical thermal-RGB imaging system for monitoring agricultural crops. Computers and Electronics in Agriculture, 147: 34-43. https://doi.org/10.1016/j.compag.2018.02.018 (5-Year IF: 2.502).

Sinha, R., L. R. Khot, B. Schroeder and S. Sankaran. 2018. FAIMS based volatile fingerprinting for real-time postharvest storage infections detection in stored potatoes and onions. Postharvest Biology and Technology, 135: 83-92. https://doi.org/10.1016/j.postharvbio.2017.09.003 (5-Year IF: 3.603).

Zúñiga C. E., A. P. Rathnayake, M. Chakraborty, S. Sankaran, P. Jacoby and L. R. Khot. 2018. Applicability of time-of-flight based ground and multispectral aerial imaging for grapevine canopy vigour monitoring under direct root-zone deficit irrigation. International Journal of Remote Sensing, In Press (5-Year IF: 1.724).

Bahlol, H. Y., R. Sinha, G.–A. Hoheisel, R. Ehsani and L. R. Khot. 2018. Efficacy evaluation of horticultural oil based thermotherapy for pear psylla management. Crop Protection, 113: 97-103. https://doi.org/10.1016/j.cropro.2018.07.015 (5-Year IF: 1.936).

Boydston, R., L. D. Porter, B. Chaves-Cordoba, L. R. Khot and P. N. Miklas. 2018. The impact of tillage on pinto bean cultivar response to drought induced by deficit irrigation. Soil & Tillage Research, 180: 63-72. https://doi.org/10.1016/j.still.2018.02.011 (5-Year IF: 3.856).

Jarolmasjed, S., S. Sankaran, L. Kalcsits and L. R. Khot. 2018. Proximal hyperspectral sensing of stomatal conductance to monitor the efficacy of exogenous abscisic acid applications in apple trees. Crop Protection, 109: 42-50. https://doi.org/10.1016/j.cropro.2018.02.022 (5-Year IF: 1.936).

Jarolmasjed, S., L. R. Khot and S. Sankaran. 2018. Hyperspectral imaging and spectrometry-derived spectral features for bitter pit detection in storage apples. Sensors, In Press. (5-Year IF: 2.677).

Sankaran, S., J. Zhou, L.R. Khot, J.J. Trapp, E. Mndolwa and P.N. Miklas. 2018. High-throughput field phenotyping in dry bean using small unmanned aerial vehicle based multispectral imagery. Computers and Electronics in Agriculture, 151: 84-92. https://doi.org/10.1016/j.compag.2018.05.034 (5-Year IF: 2.502).

Gallardo, R.K., E. Stafne, L. Wasko DeVetter, Q. Zhang, C. Li, F. Takeda, J. Williamson, W. Yang, R. Beaudry, W. Cline, and R. Allen. 2018. “Blueberry Producers’ Attitudes toward Harvest Mechanization for Fresh Market.” HortTechnology, 28(1):10-16.

Gallardo, R.K. and H. Garming. “The Economics of Apple Production.” In Achieving Sustainable Cultivation of Apples. Ed. Kate Evans. Burleigh Dodds Science Publishing. Cambridge, UK. Published June 16, 2017.

Ye, Y., L. He, Z. Wang, D. Jones, G. Hollinger, M. Taylor, and Q. Zhang, (2018). Orchard maneuvering strategy for a robotic bin-handling machine bin-dog a self-propelled platform for bin management in orchards. Biosystems Engineering, 169: 85-103.

Ye, Y., Z. Wang, D. Jones, L. He, M. Taylor, G. Hollinger, and Q. Zhang, (2017). Bin-dog: a robotic platform for bin management in orchards.  Robotics, 6(2): Article 12 (17pp).

Jones, D., and G. Hollinger, (2017). Planning energy-efficient trajectories in strong disturbances. Robotics and Automation Letters, 2(4): 2080-2087.

Alhamid, J.O., C. Mo, X. Zhang, P. Wang, M.D.Whiting, Q. Zhang, (2018).  Cellulose nanocrystals reduce cold damage to reproductive buds in fruit crops. Biosystems Engineering, 172: 124-133.

Zhang, J., L. He, M. Karkee, Q. Zhang, X. Zhang and Z. Gao. 2018. Branch Detection for Apple Trees Trained in Fruiting Wall Architecture using Depth Features and Regions-Convolutional Neural Network (R-CNN). Computers and Electronics in Agriculture. 155: 386-393.

Zhang, X., He, L., Majeed, Y., Karkee, M., Whiting, M. D., & Zhang, Q. 2018. A precision pruning strategy for improving efficiency of vibratory mechanical harvesting of apples. Transactions of the ASABE. 61(5): 1565-1576.

Ma, S., Karkee, M., Han, F., Sun, Q., & Zhang, Q.  (2018). Evaluation of shake-and-catch mechanism in mechanical harvesting of apples. Transactions of the ASABE, 61(4): 1257-1263.

Khanal, K., S. Bhusal, M. Karkee, Q. Zhang. 2018. Raspberry Primocane Bundling and Taping Mechanisms. Transactions of the ASABE. 61(4): 1265-1274.

Ma, S., M. Karkee#, P. Scharf, and Q. Zhang. Adaptability of Chopper Harvester in Harvesting Sugarcane, Energy Cane, and Banagrass. Transactions of the ASABE, 61(1): 27-35.

He, L., M. Karkee#, Q. Zhang. 2018. Evaluation of a localized shake-and-catch harvesting system for fresh market apples. Agricultural Engineering International: CIGR Journal, 19(4), pp.36-44.

Ma, S., P. Scharf, M. Karkee, Q. Zhang, J. Tong, and L. Yu. 2017. A Study on the Effects of Harvester Off-track Errors on Stubble Losses. Applied Engineering in Agriculture. 33(6): 771-779.

Amatya, S., Karkee, M., Zhang, Q. and Whiting, M.D., 2017. Automated Detection of Branch Shaking Locations for Robotic Cherry Harvesting Using Machine Vision. Robotics, 6(4), p.31

Gasch, C.K., D.J. Brown, C.S. Campbell, D.R. Cobos, E.S. Brooks, M. Chahal, M. Poggio, and D.R. Huggins, 2017. A field-scale sensor network data set for monitoring and modeling the spatial and temporal variation of soil water content in a dryland agricultural field. Water Resources Research. 53: 10878-10887. DOI: 10.1002/2017WR021307

WEST VIRGINIA

2018. A. Dias, A. Tabb and H. Medeiros, “Multispecies Fruit Flower Detection Using a Re_ned Semantic Segmentation Network," in IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3003-3010, Oct. 2018.

2018. A. Hollender, P. Thierry, A. Tabb, T. Hadiarto, C. Srinivasan, W. Wang, Z. Liu, R. Scorza, and C. Dardick, \Loss of a highly conserved sterile alpha motif domain gene (WEEP) results in pendulous branch growth in peach trees," Proceedings of the National Academy of Sciences, vol. 115, no. 20, pp. E4690- E4699, 2018.

2018. A. Hollender, J. M. Waite, A. Tabb, D. Raines, S. Chinnithambi, and C. Dardick, “Alteration of TAC1 expression in Prunus species leads to pleiotropic shoot phenotypes," Horticulture Research, vol. 5, no. 1, p. 26, May 2018.

2018. A. Dias, A. Tabb, and H. Medeiros, Apple flower detection using deep convolutional networks," Computers in Industry, vol. 99, pp. 17-28, Aug. 2018.

2018. Tabb and H. Medeiros, “Automatic segmentation of trees in dynamic outdoor environments," Computers in Industry, vol. 98, pp. 90-99, Jun. 2018.

Peer-reviewed conferences:

1943. Tabb and H. Medeiros, “Fast and Robust Curve Skeletonization for Real-World Elongated Objects," in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018, pp. 1935-1943.

595. Tabb, K. E. Duncan and C. N. Topp, “Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets," in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018, pp. 586-595.

Software releases:

2017. Tabb. 2017. Code from: Fast and robust curve skeletonization for real-world elongated objects. Ag Data Commons. 10.15482/USDA.ADC/1399689

Data releases:

P. A. Dias, A. Tabb, H. Medeiros. Multi-species fruit ower detection using a refined semantic segmentation network. Ag Data Commons.10.15482/USDA.ADC/1423466