Journal Articles (Selected)

1. Ram, B. G., Zhang, Y., Costa, C., Ahmed, M. R., Peters, T., Jhala, A., ... & Sun, X*. (2023). Palmer amaranth identification using hyperspectral imaging and machine learning technologies in soybean field. Comput. Electron. Agric., 215, 108444. https://doi.org/10.1016/j.compag.2023.108444

2. Rai, N., Mahecha, M. V., Christensen, A., Quanbeck, J., Zhang, Y., Howatt, K., ... & Sun, X*. (2023). Multi-format open-source weed image dataset for real-time weed identification in precision agriculture. Data Brief, 51, 109691. https://doi.org/10.1016/j.dib.2023.109691

3. Rai, N., Zhang, Y., Ram, B. G., Schumacher, L., Yellavajjala, R. K., Bajwa, S., & Sun, X*. (2023). Applications of deep learning in precision weed management: A review. Comput. Electron. Agric., 206, 107698. https://doi.org/10.1016/j.compag.2023.107698

4. Rai, N., Sun, X*., Igathinathane, C., Howatt, K., & Ostlie, M. (2023). Aerial-based weed detection using low-cost and lightweight deep learning models on an edge platform. J. ASABE. 66(5). 1040-1055. 10.13031/ja.15413 

5. Sommer, D. M., Young, J. M., Sun, X., López-Martínez, G., & Byrd, C. J. (2023). Are infrared thermography, feeding behavior, and heart rate variability measures capable of characterizing group-housed sow social hierarchies? J. Anim. Sci., 101, skad143. https://doi.org/10.1093/jas/skad143

6. Zhang, Y., Striker, R., Disu, M., Monono, E., Peckrul, A., Advani, G., ... & Sun, X*. (2023). Dielectric constant-based grain mass estimation using radio frequencies sensing technology. Appl. Eng. Agric., 39(2), 207-214. 10.13031/aea.15121 

7. Sunil, G. C., Zhang, Y., Koparan, C., Ahmed, M. R., Howatt, K., & Sun, X*. (2022). Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions. J. Agric. Food Res., 9, 100325. https://doi.org/10.1016/j.jafr.2022.100325

8. Sunil, G. C., Koparan, C., Ahmed, M. R., Zhang, Y., Howatt, K., & Sun, X*. (2022). A study on deep learning algorithm performance on weed and crop species identification under different image background. Artif. Intell. Agric., 6, 242-256. https://doi.org/10.1016/j.aiia.2022.11.001

9. Ahmed, M. R., Ram, B., Koparan, C., Howatt, K., Zhang, Y., & Sun, X*. (2022). Multiclass classification on soybean and weed species using a customized greenhouse robotic and hyperspectral combination system. J. ASABE, 66(5). 10.13031/ja.15131  

10. Sunil, G.C., Zhang, Y., Koparan, C., Ahmed, M.R., Howatt, K. & Sun, X*. (2022). Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions. J. Agric. Food Res., 9, p.100325. https://doi.org/10.1016/j.jafr.2022.100325

11. Eide, A., Zhang, Y., Koparan, C., Stenger, J., Ostlie, M., Howatt, K., Bajwa, S., & Sun, X*. (2021). Image based thermal sensing for glyphosate resistant weed identification in greenhouse conditions. Comput. Electron. Agric., 188, p.106348. https://doi.org/10.1016/j.compag.2021.106348

12. Chen, X., Ogdahl, W., Hanna, L.L.H., Dahlen, C.R., Riley, D.G., Wagner, S.A., Berg, E.P., & Sun, X*. (2021). Evaluation of beef cattle temperament by eye temperature using infrared thermography technology. Comput. Electron. Agric., 188, 106321. https://doi.org/10.1016/j.compag.2021.106321

13. Shi, Y., Wang, X., Mohammad, B., Young, J., Newman, D., Berg, E., & Sun, X*. (2021). A review on meat quality evaluation methods based on non-destructive computer vision and artificial intelligence technologies. Food Science of Animal Resources. https://doi.org/10.5851/kosfa.2021.e25

14. GC, S., Saidul Md, B., Zhang, Y., Reed, D., Ahsan, M., Berg, E. P., & Sun, X*. (2021). Using deep learning neural network in artificial intelligence technology to classify beef cuts. Frontiers Sens., 2, 5. https://doi.org/10.3389/fsens.2021.654357

15. Ahmed, M. R., Reed, D. D. Jr., Young, J. M., Eshkabilov, S., Berg, E. P., & Sun, X*. (2021). Beef quality grade classification based on intramuscular fat content using hyperspectral imaging technology. Applied Sciences. 11(10):4588. https://doi.org/10.3390/app11104588 [Cover story on Applied Sciences, MDPI]

16. Delavarpour, N., Koparan, C., Nowatzki, J., Bajwa, S., & Sun, X*. (2021). A technical study on UAV characteristics for precision agriculture applications and associated practical challenges. Remote Sensing, 13, 1204. https://doi.org/10.3390/rs13061204

17. Eshkabilov, S., Chiwon, W. L., Sun, X., Ademola, H., & Simsek, H. (2021). Hyperspectral imaging techniques to examine nutrient status of hydroponically grown lettuce cultivars. Comput. Electron. Agric., 181, 105968. https://doi.org/10.1016/j.compag.2020.105968

18. Liu, J. H., Newman, D. J., Young, J. M., & Sun, X*. (2020). Prediction of whole pork loin and individual chops’ intramuscular fat using computer vision system technology. Meat and Muscle Biology, 4(1). https://doi.org/10.22175/mmb.11127

19. Chen, X., Ogdahl, W., Borhan, M. S., & Sun, X*. (2020). Evaluation of beef cattle temperament using video technology. Trans. ASABE, 63(6), 1905-1911. https://doi.org/10.13031/trans.14044 

20.  Sun, X.*, Young, J., Liu, J. H., Chen, Q., & Newman, D. (2018). Predicting pork color Scores using computer vision and support vector machine technology. Meat and Muscle Biology, 2(1), 296-302. https://doi.org/10.22175/mmb2018.06.0015

21. Sun, X.*, Young, J., Liu, J. H., & Newman, D. (2018). Prediction of pork loin quality using online computer vision system and artificial intelligence model. Meat Sci., 140, 72-77. https://doi.org/10.1016/j.meatsci.2018.03.005

22. Sun, X., Newman, D., Liu, J. H., Young, J., & Bachmeier, L. (2016). Predicting pork two tone color grade using image color features and support vector machine. Meat Sci., (112), 154-155. https://doi.org/10.1016/j.meatsci.2015.08.119

23. Sun, X., Newman, D., Young, J. M, & Berg E. P. (2016). Prediction of pork fatty acid content using image texture features. Advance Journal of Food Science and Technology, 12(11): 644-647. http://dx.doi.org/10.19026/ajfst.12.3323

24. Sun, X., Chen G. Y., Young J. M., Liu J. H., Bachmeier L., Chen K. J., Zhang Y. & Newman, D. (2016). Prediction of pork color grade using image two-tone color ratio features and support vector machine.  Advance Journal of Food Science and Technology, 11(9): 593-598. http://dx.doi.org/10.19026/ajfst.11.2733

25. Sun, X., Young, J. M, Liu, J. H., Bachmeier L., Somers R. M., Chen K. J., & Newman, D. (2016). Prediction of pork color attributes using computer vision system. Meat Sci., 113, 62-64. https://doi.org/10.1016/j.meatsci.2015.11.009

26. Sun, X., Chen, K. J., Berg, E. P., Newman, D. J., Schwartz, C. A., Keller, W. L., & Maddock Carlin, K. R. (2014). Prediction of troponin-T degradation using color image texture features in 10d aged beef longissimus steaks. Meat Sci., 96(2), 837-842. https://doi.org/10.1016/j.meatsci.2013.09.012

27. Sun, X., Chen K. J., Maddock- Carlin K. R., Anderson V. L., Lepper A. N., Schwartz C.A., Keller W. L., Ilse B. R., Magolski J. D. & Berg E. P. (2012). Predicting beef tenderness using color and multispectral image texture features. Meat Sci., (4): 382-393. https://doi.org/10.1016/j.meatsci.2012.04.030

28. Sun, X., Chen K. J., Berg E. P. & Magolski J. D. Predicting fresh beef color grade using machine vision imaging and support vector machine (SVM) analysis. (2011). Journal of Animal and Veterinary Advances, (10): 1504-1511. http://dx.doi.org/10.3923/javaa.2011.1504.1511

*indicates corresponding author

Book Chapters

1. Sun, X., Berg, E. P. CH07 - Factors affecting the colour and texture of pig meat. Achieving sustainable production of pig meat. (2017). Burleigh Dodds Science Publishing. (Link)

2. Zhang, Y., Sun, X., Bajwa, S., G., Sivarajan, S., Nowatzki, J., Khan, M. Chapter 8: Plant Disease Monitoring with Vibrational Spectroscopy. Vibrational Spectroscopy for Plant Varieties and Cultivar Characterization. (2018). Elsevier. 

Conference Proceedings

1. Rai, N., Zhang, Y., Quanbeck, J., Christensen, A., & Sun, X*. (2022). SpotWeeds: A multiclass UASs acquired weed image dataset to facilitate site-specific aerial spraying application using deep learning. In 2022 15th International Conference on Precision Agriculture (ICPA). 

2. Sun, X., Young, J. M., Liu, J. H., & Newman, D. J. (2017). Comparison of C and D illuminants of Minolta colorimeter for assessing pork color. Translational Animal Science, 1(supplement1), 245-247. 

3. Knutson, E. E., Sun, X., Fontoura, A. B. P., Gaspers, J., Liu, J. H., Carlin, K. R., ... & Ward, A. K. (2017). Effect of a low vitamin A diet on marbling and carcass characteristics of Angus cross and Simmental steers. Translational Animal Science, 1(supplement1), 90-94.

4. Chen, K. J., Yang, L, Sun, X. (2010). Non-Destructive measurement of SSC, PH, firmness and density of ‘dangshan’ pear using FT-NIR spectrometry. CIGR XVIIth World Congress – Québec City, Canada – June 13-17, 2010.

5. Sun, X., Gong, H. J., Zhang, F. Chen, K. J. (2009). A digital image method for measuring and analyzing color characteristics of various color scores of beef. Proceeding of 2nd International Congress on Image and Signal Processing, Tianjin.