Books and Book Chapters:
1. Z. Zhang, P. Flores. Detection of Wheat Lodging Plots using Indices Derived from Multi-spectral and Visible Images. In: J.Li & Z. Zhang (Eds.), Nondestructive Evaluation of Agro_Products by Inteliggent Sensing Techniques, 2021, Bentham Books, 267-289.
2. Z. Zhang, C. Igathinathane, P. Flores, Y. Ampatzidis, H. Liu, J. Mathew, A. Das. Time Effect After Initial Wheat Lodging on Plot Lodging Ratio Detection Using UAV Imagery and Deep Learning. In: Z. Zhang, H. Liu, C. Yang, Y. Ampatzidis, J. Zhou, Y. Jiang (Eds.). Unmanned Aerial Systems in Precision Agriculture Technological - Progresses and Applications, 2022, Springer, 59-72.
3. Z. Zhang, C. Igathinathane, P. Flores, J. Mathew, J. Ransom, Y. Ampatzidis, A. Das. UAV Mission Height Effects on Wheat Lodging Ratio Detection. In: Z. Zhang, H. Liu, C. Yang, Y. Ampatzidis, J. Zhou, Y. Jiang (Eds.). Unmanned Aerial Systems in Precision Agriculture Technological - Progresses and Applications, 2022, Springer, 73-86.
4. A. Das, J. Mathew, Z. Zhang, A. Friskop, Y. Huang, P. Flores, X. Han,. Corn Goss’s Wilt Disease Assessment Based on UAV Imagery. In: Z. Zhang, H. Liu, C. Yang, Y. Ampatzidis, J. Zhou, Y. Jiang (Eds.). Unmanned Aerial Systems in Precision Agriculture Technological - Progresses and Applications, 2022, Springer, 123-136.
Journal Articles:
1. P. Flores, Z. Zhang, C. Igathinathane, M. Jithin, D. Naik, J. Stenger, J. Ransom, R. Kiran. Distinguishing seedling volunteer corn from soybean through greenhouse color, color-infrared, and fused images using machine and deep learning. Industrial Crops and Products, 161, 2021, 113223. https://doi.org/10.1016/j.indcrop.2020.113223.
2. Flores, P., Zhang, Z., Mathew, J., Jahan, N., Stenger, J. Distinguishing Volunteer Corn from Soybean at Seedling Stage Using Images and Machine Learning. Smart Agriculture, 2020, 2(3): 61-74. https://doi.org/10.12133/j.smartag.2020.2.3.202007-SA002.
3. Zhang, Z.; Flores, P.; Igathinathane, C.; L. Naik, D.; Kiran, R.; Ransom, J.K. Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms. Remote Sens. 2020, 12, 1838. https://doi.org/10.3390/rs12111838.
4. Zhang, Z., Igathinathane, C., Li, J., Cen, H., Lu, Y., Flores, P. Technology progress in mechanical harvest of fresh market apples. Computers and Electronics in Agriculture, 175, 2020, 105606. https://doi.org/10.1016/j.compag.2020.105606.
5. Tracy, B. F., Albrecht, K., Flores, J., Hall, M., Islam, A., Jones, G., Lamp, W., Macadam, J.W., Skinner, H., Teutsch, C. 2016. Evaluation of Alfalfa–Tall Fescue Mixtures across Multiple Environments. Crop Sci. 56:2026-2034. Doi: https://doi.org/10.2135/cropsci2015.09.0553.
6. Tracy, B. F., Schlueter, D. H., Flores, J. P. (2015), Conditions that favor clover establishment in permanent grass swards. Grassland Science, 61: 34 -40. doi: https://doi.org/10.1111/grs.12075.
Non-Refereed Publications
1. UAS Imagery and Computer Vision for Site-Specific Weed Control in Corn
R Sapkota, P Flores - arXiv preprint arXiv:2204.12417, 2022.
2. A greenhouse-based high-throughput phenotyping platform for identification and genetic dissection of resistance to Aphanomyces root rot in field pea
Md Abdullah Al Bari, Dimitri Fonseka, John Stenger, Kimberly Zitnick-Anderson, Sikiru Adeniyi Atanda, Hannah Worral, Lisa Piche, Jeonghwa Kim, Mario Morales, Josephine Johnson, Rica Amor Saludares, Paulo Flores, Julie Pasche, Nonoy Bandillo - bioRxiv, 2022.
3. Opportunities for Agriculture through Industrial Internet of Things/Industry 4.0-A comparison between US and Europe. H Bernhardt, M Treiber, P Flores, X Sun, L Schumacher - 2022 ASABE Annual International Meeting, 2022.
5. Scaling Up Window-based Regression for Crop-row Detection. G. E. Hokanson, A. M. Denton, P. Flores. 15th International Conference on Precision Agriculture, 2022.
6. Comparative Analysis of Light-weight Deep Learning Architectures for Soybean Yield Estimation Based on Pod Count from Proximal Sensing Data for Mobile and Embedded Vision Applications. J. J. Mathew, P. Flores, J. Stenger, C. Miranda, Z. Zhang, A. K. Das. 15th International Conference on Precision Agriculture, 2022.
7. Assessment of Goss Wilt Disease Severity Using Machine Learning Techniques Coupled with UAV Imagery. A. Das, Z. Zhang , P. Flores, A. Friskop, J. Mathew. 15th International Conference on Precision Agriculture, 2022.
8. K. Das, A. Friskop, P. Flores, C. Igathinathane, J. Mathew, Z. Zhang. Using aerial imagery coupled with machine learning to assess Goss’s Wilt disease severity in field corn. 2021 ASABE Annual International Virtual Meeting 2100146. (doi:10.13031/aim.202100146).
9. N. Rai, P. Flores. Leveraging transfer learning in ArcGIS Pro to detect “doubles” in a sunflower field. 2021 ASABE Annual International Virtual Meeting 2100742.(doi:10.13031/aim.202100742).
10. J. Mathew, Y. Zhang, P. Flores, C. Igathinathane, Z. Zhang. Development and test of RGB-D camera-based rock detection system and path optimization algorithm in an indoor environment. 2021 ASABE Annual International Virtual Meeting 2100105. (doi:10.13031/aim.202100105).
11. N. Jahan, Z. Zhang, Z. Liu, A. Friskop, P. Flores, J. Mathew, A. Das. Using images from a handheld camera to detect wheat bacterial leaf streak disease severities. 2021 ASABE Annual International Virtual Meeting 2100112. (doi:10.13031/aim.202100112)
12. Gris, D., Flores, P., & Osorno, J. M. (2021) Implementing High-Throughput Phenotyping at the NDSU Dry Bean (Phaseolus vulgaris L.) Breeding Program Using Unmanned Aerial Systems [Abstract]. ASA, CSSA, SSSA International Annual Meeting, Salt Lake City, UT. https://scisoc.confex.com/scisoc/2021am/meetingapp.cgi/Paper/138462
13. Sapkota, R., & Flores, P. (2021) Using UAV Imagery and Computer Vision to Support Site-Specific Weed Management in Corn [Abstract]. ASA, CSSA, SSSA International Annual Meeting, Salt Lake City, UT. https://scisoc.confex.com/scisoc/2021am/meetingapp.cgi/Paper/138658
14. Bari, M. A. A., Fonseka, D., Stenger, J., Worral, H., Morales, M. A., Piche, L., Flores, P., Bandillo, N., & Pasche, J. S. (2021) High-Throughput Phenotyping for Genetic Dissection and Prediction of Aphanomyces Root Rot in Pea [Abstract]. ASA, CSSA, SSSA International Annual Meeting, Salt Lake City, UT. https://scisoc.confex.com/scisoc/2021am/meetingapp.cgi/Paper/135427
