Brief Summary of Minutes of Annual Meeting

The 2018 NCERA180 Annual Meeting was held in Manhattan, Kansas on May 23, 2018 and chaired by Dr. Ajay Sharda. Upcoming 2018 meeting notifications were provided regarding the International Conference on Precision Agriculture, InfoAg, American Society of Agricultural and Biological Engineers Annual International Meeting, and the Austral Asian Precision Agriculture Conference. Discussion was had on data issues in agriculture. Needs were identified for the group to consider focusing on in 2018/2019. Dr. Steven Thomson provided an update on USDA NIFA programs the group should be considering for funding. The 2019 NCERA180 Annual Meeting will be held in Madison, Wisconsin under the direction of Dr. Brian Luck. A Vice Chair was not elected at the meeting, but Dr. Jason Ward of North Carolina State University was later identified, nominated, and elected. Action items were identified as follows:  1) Identify new members; 2) Identify a Vice Chair for 2018/2019; and 3) Send comments on alternate meeting date to Dr. Sharda and Dr. B. Luck.

Accomplishments:  Institutions made tremendous contribution to knowledge on production machinery, variable rate input decision and application strategies, technology utilization, agronomic practices and enhancing optimal input use to improve yields and economic viability of producers. Research projects conducted with output/outcomes are listed below:

Research:

1. University of California-Davis: Management zone based Precision Irrigation in Almonds and Grapes (Dr. Shrini K Upadhyaya) – Data obtained during the 2015 growing season have been fully analyzed. Both almond and grape crops presented high correlation between the crop water stress index (CWSI) computed from leaf monitor data and the deficit stem water potential measured by the pressure chamber.
   Automated Weeding using Crop Signaling Compound (Dr. David Slaughter) – Progress was made on the development of automated, intra-row weed control technologies for vegetable crops that can accurately and rapidly differentiate crops from weeds, determine their spatial location and automatically kill weeds without damage to the crop.
   Precision Yield mapping in orchard crops (Dr. Stavros Vougioukas) – Yield monitors are currently not available for tree fruits, as all are harvested manually. A commercial picking bag was instrumented to measure harvested fruit weight. Results showed a mean error of 0.39 kg, standard deviation 0.42 kg, and 95th percentile 1.04 kg. Major error sources included bag acceleration and body reaction force.
   A Center for SmartFarm has been established at UC Davis as a part of one of the campus-wide “BIG IDEA” project.
2. North Dakota State University: Dakota Prairie Grasslands UAS Project 2018. Benjamin Geaumont, NDSU, Hettinger REC – Aerial surveys using manned aircraft have historically been an important tool for agencies to assess wildlife populations but these surveys can be expensive, dangerous, and difficult to accurately repeat. The goal of this project is to evaluate the feasibility of using UAS to locate and monitor leks of sharp-tailed grouse in the Dakota Prairie Grasslands.
   Studies Evaluate Precision Herbicide Applications. Rod Lym, NDSU, Plant Science – Herbicide applications with the PWM system on leafy spurge and Canada thistle resulted in control comparable to applications from a standard boom sprayer at all droplet sizes, except 150 microns. The PWM sprayer can be used to apply herbicides in pasture and rangeland at a variety of travel speeds while maintaining medium-sized or larger droplets for reduced drift and more uniform coverage.
   Precision Application of Herbicides for Sustainable Crop Systems. Kirk Howatt, NDSU Plant Science – Herbicides applied in pasture and rangeland with the PWM system provided similar leafy spurge and Canada thistle control compared to traditional boom sprayer with 8002 nozzles as long as the average droplet size exceeded 150 microns. Effects demonstrated in these experiments may result in less than optimal control of weeds; however, results also indicate faster development of herbicide-resistant weeds with PWM sprayers than with conventional systems.
   Coated Confectionery Sunflower Kernels for Precision Agriculture. Harjot Sidhu, NDSU Agricultural and Biosystems Engineering – This project aims to hull and coat extra-large hybrid confection sunflower kernels for use in precision planting was sponsored by the National Sunflower Association (NSA). Overall, this study showed that coating the hulled kernels substantially increased seed plantability and crop performance
   Prediction of Pork Loin Quality using Online Computer Vision System and Artificial Intelligence Mmodel. Xin Suna, Jennifer Younga, Jeng-Hung Liua, David Newman – The objective of this project was to develop a computer vision system (CVS) for objective measurement of pork loin under industry speed requirement. This research shows that the proposed artificial intelligence prediction model with CVS can provide an effective tool for predicting color and marbling in the pork industry at online speeds.
3. Colorado State University: The technological hardware requisite for precise water delivery methods such as variable rate irrigation is commercially available. Despite that, techniques to formulate a timely, accurate prescription for those systems are inadequate.  Research was conducted to assess if vegetation indices derived from multispectral satellite imagery could assist in quantifying soil moisture variability in irrigated maize production. Our study showed that satellite-derived vegetation indices may be useful for creating time-sensitive characterizations of soil moisture variability. Specifically, the Red Edge Normalized Difference (NDRE) Vegetation Index could quantify soil moisture tension variability at V6 (six leaf) (r2 = 0.850, p = 0.009) and V9 (nine leaf) (r2 = 0.913, p = 0.003) crop growth stages.
4. University of Nebraska-Lincoln: Project SENSE (Sensors for Efficient Nitrogen Use and Stewardship of the Environment) – 2017 represented the third year of Project SENSE. During the year, we reported on significant project findings from the first two years as we conducted year three field activities. In addition to communicating at five field days and one crop management clinic (reaching over 100 individuals), we were able to report significant savings in N applied using the crop canopy sensor-based approach.
5. University of Minnesota: Variable Rate Nitrogen (VRN) studies for improved production of maize and better water quality (David Mulla) – Nitrogen pollution from suboptimal management of synthetic N fertilizer continues to pose a challenge, especially for drinking water, in rural Minnesota. Variable rate technology based on in-season sensing is becoming more available and affordable, however, a comprehensive evaluation considering the agronomic, environmental and economic impacts is needed to facilitate acceptance of the technology by growers. VRN subfields displayed significantly and consistently higher Nitrogen Use Efficiency (NUE) values for both growing seasons. Lastly, VRN for maize based on in-season sensing was profitable every year, even in the absence of yield increases, because of the N savings from better utilization of soil N.
6. Texas A&M University: Alex Thomasson – Commercial off-the shelf systems of UAVs and sensors are touted as being able to collect remote-sensing data on crops that include spectral reflectance, plant height, and canopy temperature. Historically a great deal of effort has gone into quantifying and reducing the error levels in the geometry of UAV-based orthomosaics, but little effort has gone into quantifying and reducing the error of the reflectance, plant-height, and canopy-temperature measurements themselves. We have been developing systems and protocols involving multifunctional GCPs (ground-control points) in order to produce crop phenotypic data that are as repeatable as possible. We believe through continued development these error reductions will increase, such that broad-acre phenotypic data can be collected with a high level of repeatability.  We are also working on incorporating temperature calibration into the system so that canopy temperature can be measured precisely and accurately.
7. University of Wisconsin-Madison: Forage harvest is an important process for Wisconsin agriculture, providing feed for dairy cows. This harvest process is very machinery and labor intensive and requires coordination and communication to be done properly. Dr. Brian Luck and the Agriculture Technology Lab have been investigating forage harvest efficiency and developing tools to help producers and custom harvesters efficiently harvest high-quality feed. The focus of this effort has been on two fronts:  1) Time-motion analysis and vehicle movement efficiency, and 2) Image analysis for particle size determination of chopped and processed corn silage. Conversations with producers and custom harvesters have indicated interest in how the forage harvest process could be made more efficient. Research, published in 2018 (Harmon et al., 2018), showed self-propelled forage harvest efficiency to be 65% over three alfalfa crops and corn silage harvest. Transport vehicles showed an average of 20% idle (not working) time during the harvest 2015 and 2016 harvest. From this work a web-based simulator was developed to assess the effect of different transport vehicles on the time to harvest (Dudenhoeffer et al., 2018). Similarly, producers, custom harvesters, and dairy nutritionists were concerned about the assessing the effectiveness of kernel processors in the field during harvest rather than sending samples off to a lab. An image processing app for smart phones was developed to accomplish this task. SilageSnap was released in 2018 and currently has 338 downloads on Apple and Android devices.
   Another research focus area is centered on planting technology and how it benefits producers. Variable-rate starter fertilizer, variable row unit downforce, and furrow closing wheels are being investigated across various Wisconsin soils. Results in 2017 showed 2% better emergence with aftermarket closing wheels (α= 0.10). The addition of starter fertilizer in 2018 seemed to negate these results and no differences in closing wheels were observed. Grant funding has been obtained to investigate planter technology and best practices for high residue organic no-till farming.

Extension and Education:

1. University of Nebraska-Lincoln: Nebraska Precision Ag Data Management Workshops – From January to March, 2017, 8 day-long workshops were held in in Nebraska and 2 in Kansas. Eight day-long workshops across Nebraska, 120 attendees 179,470 producer acres and 1.1 M acres of consultant/advisor acres were represented; 108 responded to pre- and post-workshop surveys. 88% of respondents indicated the workshop was above average to one of the best extension events they’ve attended.
   Site-Specific Crop Management (AGEN/AGRO/MSYM 431) 3 Credit Hour Course – Student enrollment consisted of 75 agronomy, engineering, and mechanized systems management students in 2017. Students were exposed to a variety of topics related to precision agriculture; 15 computer laboratory exercises were conducted throughout the semester to provide hands-on learning experiences with agricultural data applications.
2. University of Wisconsin-Madison: Multiple state, regional, and national extension events were attended and presentations delivered on Forage Harvest Logistics, Kernel Processing and SilageSnap, Unmanned Aerial Vehicles and Remote Sensing, and General Precision Agriculture Topics and Big Data. Direct contacts from this effort totaled >1,300. Indirect contacts via website hits and social media impressions on precision agriculture topics exceeded 118,000.

Arriaga, F. J., B. D. Luck, and G. Slemering. 2018. Managing soil compaction at planting and harvest. University of Wisconsin Extension Learning Store. Article # A4158.

Barker, J. B., Franz, T. E., Heeren, D. M., Neale, C. M. U., Luck, J. (2017). Soil moisture monitoring for irrigation management: a geostatistical analysis. Agricultural Water Management, 188(2017), 36-49.

Coble, K., Ferrell, S.L., Mishra, A., and Griffin, T.W.  2018. Big Data in Agriculture: A Challenge for the Future. Applied Economics Perspectives and Policy, 40(1):79–96.

de Lara, A, R. Khosla, L. Longchamps. 2018. Characterizing spatial variability in soil water content for precision irrigation management. J. Agron.

Drewry, J. L., B. D. Luck, J. M. Shutske, and D Trechter. 2018. Quantifying agricultural data generation on Wisconsin farms:  A case study. Paper # 1800897. ASABE Annual International Meeting. Detroit, MI.

Drewry, J. L., B. D. Luck, R. Willett, E. Rocha, and J. D. Harmon. 2018. Assessing kernel processing score of harvested and processed corn silage via image processing techniques. Paper # 1800888. ASABE Annual International Meeting. Detroit, MI.

Dudenhoeffer, N. E., B. D. Luck, M. F. Digman, and J. L. Drewry. 2018. Technical Note:  Simulation of the forage harvest cycle for asset allocation. Applied Engineering in Agriculture 34(2):  327-334. https://doi.org/10.13031/aea.12619/.

Ferguson, R. B., Luck, J., Thompson, L. J., Parrish, J. D., Crowther, J. D., Mieno, T., Glewen, K., Krienke, B., Krull, D., Mueller, N. M., Ingram, T., Shaver, T. M. (2017). Crop caopy Sensor use with irrigated maize: profit and environmental impacts. (pp. 6). Proceedings of the 11th European Conference on Precision Agriculture. Status: Published

Ferguson, R., Luck, J., Stevens, R. H. (2017). Chapter 9: Developing prescriptive soil nutrient maps. Madison, WI: Practical Mathematics for Precision Agriculture. ASA/CSSA/SSSA. Status: Published

Gan, H., W.S. Lee, and V. Alchanatis. 2018. A photogrammetry-based image registration method for multi-camera systems - with applications in images of a tree crop. Biosystems Engineering 174: 89-106.

Gan, H., W.S. Lee, V. Alchanatis, R. Ehsani, and J. K. Schueller. 2018. Immature green citrus fruit detection using color and thermal images. Computers and Electronics in Agriculture 152: 117-125.

Griffin, T.W. 2010. The Spatial Analysis of Yield Data. In M. Oliver (Ed.) Geostatistical Applications for Precision Agriculture. Springer. 295p.

Harmon, J. D., B. D. Luck, K. J. Shinners, R. P. Anex, and J. L. Drewry. 2018. Time-motion analysis of forage harvest:  A case study. Transactions of the ASABE 62(2):  483-491. https://doi.org/10.13031/trans.12484/.

Kim, D.-W., H. S. Yun, S.-J. Jeong, Y.-S. Kwon, S.-G. Kim, W. S. Lee, and H.-J. Kim. 2018. Modeling and testing of growth status for Chinese cabbage and white radish with UAV-based RGB imagery. Remote Sens. 10(4): 563. https://doi.org/10.3390/rs10040563.

Lo, T. H., Heeren, D. M., Mateos, L., Luck, J., Martin, D. L., Miller, K. A., Barker, J. B., Shaver, T. M. (2017). Field characterization of root zone available water holding capacity for variable rate irrigation. Applied Engineering in Agriculture, 33(4), 559-572.

Lu, J., W. S. Lee, H. Gan, and X. Hu. 2018. Immature citrus fruit detection based on local binary pattern feature and hierarchical contour analysis. Biosystems Engineering 171: 78-90.

Luck, B. D., J. L. Drewry, E. Chasen, S. Steffan. 2018. Unmanned aerial systems and remote sensing for cranberry production. 14th International Conference on Precision Agriculture. Montreal, Quebec, Ca.

Luck, B. D., N. Dudenhoeffer, M. F. Digman, J. L. Drewry. 2018. Simulation of the forage harvest cycle for asset allocation. Paper # 1800523. ASABE Annual International Meeting. Detroit, MI.

Miller, N.J., Griffin, T.W., Ciampitti, I., and Sharda, A. forthcoming Farm Adoption of Embodied Knowledge and Information Intensive Precision Agriculture Technology Bundles. Precision Agriculture https://rdcu.be/6LvT

Seigfried, J, R. Khosla, L. Longchamps. 2018. Multispectral satellite imagery to quantify in-field soil moisture variability. J. Soil Water Conserv.

Shearer, C. A., Luck, J., Evans, J. T. (2017). Development of a sprayer performance diagnostic tool for better management practices of in-field spraying operations. (pp. 6). Proceedings of the 75th International Conference on Agricultural Engineering. Status: Published

Shimwela, M. M., Schubert, T. S., Albritton, M., Halbert, S. E., Jones, D. J., Sun, X., Roberts, P. D., Singer, B. H., Lee, W. S., Jones, J. B., Ploetz, R. C., and van Bruggen A. H. C. 2018. Regional spatial-temporal spread of citrus huanglongbing is affected by rain in Florida. Phytopathology. http://dx.doi.org/10.1094/PHYTO-03-18-0088-R.

Shuaibu, M., W. S. Lee, J. K. Schueller, P. Gader, Y. K. Hong, and S. Kim. 2018. Unsupervised hyperspectral band selection for apple Marssonina blotch detection. Computers and Electronics in Agriculture 148: 45-53.

Shutske, J. M., D. Trechter, B. D. Luck, J. L. Drewry, M. J. DeWitte, L. Pitman, and M. Kluz. 2018. Assessment of digital capacity, needs and access barriers among crop, dairy, and livestock producers. Paper # 1801320. ASABE Annual International Meeting. Detroit, MI.

Torquebiau, E., C., Rosenzweig, A.M. Chatrchyan, N. Andrieu, and R. Khosla 2018. Identifying Climate-Smart Agriculture Research Needs. J. Cahiers Agricultures (France).

Varela, S., Dhodda, P., Hsu, W.H., Prasad, P.V.V., Assefa, Y., Peralta, N.R., Griffin, T., Sharda, A., Ferguson, A., and Ciampitti, I.A. 2018. Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques. Remote Sensing, 10, 343.

Wang, C., W. S. Lee, X. Zou, D. Choi, H. Gan, and J. Diamond. 2018. Detection and counting of immature green citrus fruit based on the Local Binary Patterns (LBP) feature using illumination-normalized images. Precision Agriculture Doi: 10.1007/s11118-018-9574-5.

Wilson, G., Laacouri, A., Galzki, J. and Mulla, D. 2017. Impacts of Variable Rate Nitrogen (VRN) on Nitrate-N Losses from Tile Drained Maize in Minnesota, USA. Advances in Animal Biosciences, 8(2): 317-321.

Zhang, Y., M. Li, L. Zheng, Q. Qin, W. S. Lee. 2018. Spectral features extraction for estimation of soil total nitrogen content based on modified ant colony optimization algorithm. Geoderma 333: 23-34.

Zhang, Y., W. S. Lee, M. Li, L. Zheng, M. A. Ritenour. 2018. Non-destructive recognition and classification of citrus fruit blemishes based on ant colony optimized spectral information. Postharvest Biology and Technology, 143: 119-128.