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
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
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
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
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
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
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
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
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
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
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
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
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
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
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]
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
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
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
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
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
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
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
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
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
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
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
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
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
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)
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.
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).
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.
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.
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.
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.