Plant Pathology
Research for sustainable, healthy, and productive rice crops
Xin-Gen “Shane” Zhou
Professor, Rice Diseases and their Management
Dr. Zhou’s team
- Sabin Khanal, Ph.D., Postdoctoral Research Associate
- Dr. Guangjie Liu, Ph.D., Research Associate
- Sabina Tripathi, Ph.D. Student
Basic and applied research for sustainable, healthy, and productive rice crops
The goal of the plant pathology program’s basic and applied research is to develop sustainable and profitable strategies to safeguard rice crop health and enhance productivity.
The plant pathology research program focuses on etiology, epidemiology, and management of diseases in rice, including organic rice. Dr. Zhou’s research activities center on sheath blight, kernel smut, Hoja Blanca, narrow brown leaf spot, the seedling disease complex, false smut, bacterial panicle blight, and other diseases, economically important in main and ratoon (second harvest) rice crops in Texas.
Innovative focus areas
His research focuses on enhancing cultivar resistance, improving chemical control, developing effective biological control methods (cover crops, PGPRs, endophytes and other beneficial microorganisms), and developing and utilizing UAVs for remote sensing, AI-based image identification, Raman spectroscopy, spore traps, core-microbiome manipulation, and other innovative approaches to detect and manage diseases.
Dr. Zhou has secured $19 M dollars in research funding and has authored or co-authored 815 publications, including 99 refereed papers. He has discovered three new plant pathogens and six new plant diseases. His research has contributed to the release of a new rice cultivar and resulted in two patents. He has also served as a senior editor for Plant Disease, Plant Health Progress, PhytoFrontiers, and other international journals.
Selected recent publications
Expand publications
1 Li, S., Y. Yang, J. Zhang, L. T. Wilson, S. O. P. B. Samonte, F. Dou, T. Bera, X. G. Zhou, D. Sanchez, and J. Wang. 2025. UAV image analysis for detecting rice seedling gaps and gap effect on grain yield. Smart Agricultural Technology 10, 109544. Doi: https://doi.org/10.1016/j.atech.2024.100753
2 Zhang, D., Z. Chen, H. Luo, G. Hu, X. G. Zhou, C. Gu, L. Li, and W. Guo. 2024. Predicting wheat scab levels based on rotation detector and Swin classifier. Biosystems Engineering 248:15-31. Doi:https://doi.org/10.1016/j.biosystemseng.2024.09.016 10.1128/spectrum.03662-23.
3 Zhang, D., W. Tao, T. Cheng, X. G. Zhou, G. Hu, H. Qiao, W. Guo, Z. Wang, and C. Gu. 2024. GSD-YOLO: A lightweight decoupled wheat scab spore detection network based on Yolov7-Tiny. Agriculture 2024, 14, 2278. Https://doi.org/10.3390/agriculture14122278
4 Shrestha, B. K., B. M. Ward, T. W. Allen, E. T. da Silva, H. Zulli, W. Dunford, V. Doyle, C. A. Bradley, B. Buckley, P. Chen, M. Clubb, H. Kelly, J. Koebernick, B. Padgett, J. C. Rupe, E. J. Sikora, T. N. Spurlock, S. Thomas-Sharma, A. Tolbert, X. G. Zhou, and P. P. Price. 2024. Characterization of QoI-fungicide resistance in Cercospora isolates associated with Cercospora leaf blight of soybean from the southern United States. Plant Disease 108:149-161. Https://doi.org/10.1094/PDIS-03-23-0588-RE
5 Khanal, S., M. Imran, X. G. Zhou, and S. Antony-Babu. 2024. Characterization of differences in seed endophytic microbiome in conventional and organic rice by amplicon-based sequencing and culturing methods. Microbiology Spectrum 12:1-17. Doi: 10.1128/spectrum.03662-23.
6 Cheng, T., D. Zhang, C. Gu, X. G. Zhou, H. Qiao, W. Guo, Z. Niu, J. Xie, and X. Yang. 2024. YOLO-CG-HS: A lightweight spore detection method for wheat airborne fungal pathogens. Computers and Electronics in Agriculture 227 (2024) 109544. Doi: https://doi.org/10.1016/j.compag.2024.109544
7 Aslam, H. M. U., M. Chikh-Ali, X. G. Zhou, S. Zhang, S. Harris, A. K. Chanda, H. Riaz, A. Hameed, S. Aslam, and N. Killiny. 2024. Epigenetic modulation of fungal pathogens: a focus on Magnaporthe oryzae. Frontiers in Microbiology 15:1-14. Doi:https://doi.org/10.3389/fmicb.2024.1463987.
8 Zhang, D. Y., W. Zhang, T. Cheng, X. G. Zhou, A. Yan, Y. Wu, G. Zhang, and X. Yang. 2023. Detection of wheat scab fungus spores utilizing the Yolov5-ECA-ASFF network structure. Computers and Electronics in Agriculture 210:107953. Https://doi.org/10.1016/j.compag.2023.107953
9 Zhang, D. Y., H. S. Luo, T. Cheng, W. F. Li, X. G. Zhou, W. Guo, C. Y. Gu, and Z. Diao. 2023. Enhancing wheat Fusarium head blight detection using rotation Yolo wheat detection network and simple spatial attention network. Computers and Electronics in Agriculture 211:107968. Https://doi.org/10.1016/j.compag.2023.107968.
10 Yu, J., T. Cheng, N. Cai, X. G. Zhou, A. Diao, T. Wang, S. Du, D. Liang, and D. Zhang. 2023. Wheat lodging segmentation based on Lstm_PSPNet Deep Learning Network. Drones 7:143. Https://doi.org/10.3390/drones702143
11 Xin, Z., S. Ju, D. Zhang, X. G. Zhou, S. Guo, Z. Pan, L. Wang, and T. Cheng. 2023. Construction of spectral detection models to evaluate soluble solids content and acidity in Dangshan pear using two different sensors. Infrared Physics & Technology 131:104632.104632
13 Searight, J., A. N. Famoso, X. G. Zhou, V. P. Doyle, and J. K. Richard. 2023. A high-quality genome assembly for Cercospora janseana, causal agent of narrow brown leaf spot of rice. Molecular Plant-Microbe Interactions, 2023. Https://doi.org/10.1094/MPMI-10-22-0222-A.
14 Khanal, S., X. G. Zhou, and S. P. Gaire. 2023. Kernel smut and false smut: the old-emerging diseases of rice – a review. Phytopathology. 113:931-944. Doi: 10.1094/PHYTO-06-22-0226-RVW
15 Khanal, S., X. G. Zhou, and S. Antony-Babu. 2023. Multiple biosynthetic gene pathways are revealed in whole-genome sequence of endophytic biocontrol strain Pantoea stewartii subsp. Indologenes ST25. Microbiology Resource Announcements: 2023. Https://journals.asm.org/doi/epub/10.1128/mra.00425-23
16 Khanal, S., S. Antony-Babu, and X. G. Zhou. 2023. De novo genome sequence of endophytic biocontrol strain Bacillus sp. ST24 isolated from rice seed. Microbiology Resource Announcements :2023. Https://doi.org/10.1128/MRA.00352-23
17 Khanal, S., S. Antony-Babu, and X. G. Zhou. 2023. Draft genome resources of seven strains of Tilletia horrida, causal agent of kernel smut of rice. PhytoFrontiers. 3: 2-4. Https://doi.org/10.1094/PHYTOFR-11-22-0139-A.
18 Gu, C., T. Cheng, N. Cai, W. Li, G. Zhang, X. G. Zhou, and D. Zhang. 2023. Assessing narrow brown leaf spot severity and fungicide efficacy in rice using low altitude UAV imaging. Ecological Informatics 77:102208. Https://doi.org/10.1016/j.ecoinf.2023.102208.
19 Gaire, S. P., X. G. Zhou, Y. Zhou, J. Shi, and Y. K. Jo. 2023. Identification and distribution of fungal pathogens associated with seedling blight of rice in the southern United States. Plant Pathology. 72:76-88. Https://doi.org/10.1111/ppa.13645.
20 Cheng, T., G. Chen, Z. Wang, R. Hu, B. She, Z. Pan, X. G. Zhou, G. Zhang, and D. Zhang. 2023. Hyperspectral and imagery integrated analysis for vegetable seed viability detection. Infrared Physics and Technology. 131:104605. Https://doi.org/10.1016/j.infrared.2023.104605.
21 Bao, W., Z. Zhu, G. Hu, X. G. Zhou, D. Zhang, and X. Yang. 2023. UAV remote sensing detection of tea leaf blight based on DDMA-YOLO. Computers and Electronics in Agriculture. 205:107637. Https://doi.org/10.1016/j.compag.2023.107637
22 Bao, W., W. Liu, X. Yang, G., H. Hu, D. Zhang, and X. G. Zhou. 2023. Adaptively spatial feature fusion network: an improved UAV detection method for wheat scab. Precision Agric. 24:1154-1180.
23 Zhang, D. Y., H. S. Luo, D. Y. Wang, X. G. Zhou, W. F. Li, C. Y. Gu, G. Zhang, and F. M. He. 2022. Assessment of the levels of damage caused by Fusarium head blight in wheat using an improved YoloV5 method. Computers and Electronics in Agriculture. 198: 107086. Https://doi.org/10.1016/j.compag.2022.107086.
24 Yu, J., T. Cheng, N. Cai, F. Lin, X. G. Zhou, S. Du, D. Zhang, G. Zhang, and D. Liang. 2022. Wheat lodging extraction using Improved_Unet network. Front. Plant Sci. 13:1009835. Doi: 10.3389/fpls.2022.1009835.
25 Samonte, S. O. P., L. T. Wilson, R. E. Tabien, C. L. Harper, and X. G. Zhou. 2022. Registration of Trinity long-grain rice cultivar. J. Plant Regist. 2022:1-10. DOI: 10.1002/plr2.20238.
26 Liu, R., V. Singh, S. Abugho, H. Lin, X. G. Zhou, and M. Bagavathiannan. 2022. Morphophysiological diversity and its association with herbicide resistance in Echinochloa ecotypes. Weed Science. 70:26-35.
27 Khanal, S., S. Antony-Babu, S. P. Gaire, and X. G. Zhou. 2022. Multi-locus sequence analysis reveals diversity of the rice kernel smut population in the United States. Frontiers in Microbiology. 13:874120. DOI: 10.3389/fmicb.2022.874120
28 Imran, M., S. Khanal, X. G. Zhou, S. Antony-Babu, and M. Atiq. 2022. First report of Fusarium sheath rot of rice caused by Fusarium incarnatum-equiseti species complex in the United States. Plant Disease. 106 DOI: 10.1094/PDIS-12-21-2693-PDN
29 Imran, M., S. Khanal, X. G. Zhou, S. Antony-Babu, and M. Atiq. 2022. First report of brown leaf spot of rice caused by Curvularia hawaiiensis in the United States. Plant Disease. 106 doi: 10.1094/PDIS-10-21-2253-PDN
30 Imran, M., S. Khanal, X. G. Zhou, S. Antony-Babu, and M. Atiq. 2022. First report of leaf spot of rice caused by Epicoccum sorghinum in the United States. Plant Disease. 106 doi: 10.1094/PDIS-10-21-2177-PDN
31 Cheng, T., S. Guo, Z. Pan, S. Fan, S. Ju, Z. Xin, X. G. Zhou, F. Jiang, and D. Zhang. 2022. Near-infrared model and its robustness as affected by fruit origin for ‘Dangshan’ pear soluble solids content and pH measurement. Agriculture. 2022, 12, 1618. Https:// doi.org/10.3390/agriculture12101618
32 Bao, W., T. Cheng, X. G. Zhou, W. Guo, Y. Wang, X. Zhang, H. Qiao, and D. Zhang. 2022. An improved DenseNet model to classify the damage caused by cotton aphid. Computers and Electronics in Agriculture. 203:107485. Https://doi.org/10.1016/j.compag.2022.107485
33 Zhou, X. G., K. V. K. Kuma, L. W. Zhou, M. S. Reddy, and J. W. Kloepper. 2021. Combined use of PGPR and reduced rates of azoxystrobin to improve management of sheath blight of rice. Plant Disease 105: 1034-1041.
34 Zhang, D., C. Gu, Z. Wang, X. G. Zhou, and W. Li. 2021. Evaluating the efficacy of fungicides for wheat scab control by combined image processing technologies. Biosystems Engineering 211:230-246. Https://doi.org/10.1016/j.biosystemseng.2021.09.008.
35 Wang, L., E. Nysetvold, and X. G. Zhou. 2021. Cultural media promoting sporulation of rice kernel smut fungus Tilletia barclayana. Eur. J. Plant Pathol. 161:629-635. Https://doi.org/10.1007/s10658-021-02348-y.
36 Shi, J., X. G. Zhou, Z. Yan, R. E. Tabien, L. T. Wilson, and L. Wang. 2021. Hybrid rice outperforms inbred rice in resistance to sheath blight and narrow brown leaf spot. Plant Disease 105:2981-2984. Https://doi.org/10.1094/PDIS-11-20-2391-RE.
37 Redman, R. S., Y. O. Kim, S. Cho, M. Mercer, M. Rienstra, R. Manglona, X. G. Zhou, M. Chilvers, Z. Gray, and R. J. Rodriguez. 2021. A symbiotic approach to generating stress tolerant crops. Microorganisms 8:920-947.
38 Liu, R., V. Singh, X. G. Zhou, and M. Bagavathiannan. 2021. Stakeholder and field surveys on weed issues and research needs in rice production in Texas. Weed Technology 35:242-250.
39 Gaire, S. P., X. G. Zhou, and Y. K. Jo. 2021. Sterile white basidiomycete fungus Marasmius graminum: a new pathogen causing seeding blight in rice. Plant Disease 105: https://doi.org/10.1094/PDIS-05-20-1136-PDN.
40 Zhou, X. G., D. Zhang, and E. Lin. 2021. UAV remote sensing: an innovative tool for detection and management of rice diseases. Page 43-67 In: Diagnostics of Plant Diseases, ed. Dmitry Kurouski, IntechOpen. DOI: http://dx.doi.org/10.5772/intechopen.95535.
41 Zhou, X. G., M. O. Way, A. McClung, and F. Dou. 2021. Texas organic rice production guidelines. Texas AgriLife Research. 10 pages. EPLP-052.
42 Zhang, D., G. Chen, X. Yin, R. Hu, C. Gu, Z. Pan, X. G. Zhou, and Y. Chen. 2020. Integrating spectral and image data to detect Fusarium head blight of wheat. Computers and Electronics in Agriculture 175:105588 DOI:10.1016/j.compag.2020.105588.
43 Lin, F., S. Gao, C. Tan, X. G. Zhou, and D. Zhang. 2020. Identification of rice sheath blight through spectral responses using hyperspectral images. Sensors 20:6243. DOI:10.3390/s20216243.
44 Li, X., F. Dou, K. B. Watkins, S. Wang, K. Chen, X. G. Zhou, A. McClung, J. O. Storlien, and F. M. Hons. 2020. Seeding rate effects on organic rice growth, yield, and economic returns. Agronomy Journal 2020:1-16. DOI:10.1002/agj2.20304.
45 Gaire, S., X. G. Zhou, Y. Jo, and J. Shi. 2020. First report of Rhizoctonia solani AG-4 causing seedling disease in rice. Plant Disease 104. DOI:10.1094/PDIS-07-19-1570-PDN.
46 Cai, N., X. G. Zhou, Y. B. Yang, J. Wang, D. Y. Zhang, and R. J. Hu. 2019. Use of UAV images to assess narrow brown leaf spot severity in rice. Int J. Precis. Agric. Aviat. 2 (2):38-42.
47 Hua, Y., N. Zhang, X. Yuan, L. Quan, J. Yang, K. Nagasaka, and X. G. Zhou. 2019. Recent advances in intelligent automated fruit harvesting robots. The Open Agriculture Journal 13: 101-106.
48 Shew, A. M., A. Durand-Morat, L. L. Nalley, X. G. Zhou, C. Rojas, and G. Thoma. 2019. Warming increases bacterial panicle blight (Burkholderia glumae) occurrences and impacts on USA rice production. PLoS ONE 14(7): e0219199. Https://doi.org/10.1371/journal.pone.0219199
49 Uppala, S. and X. G. Zhou. 2019. Optimum timing of propiconazole to manage narrow brown leaf spot in the main and ratoon crops in Texas. Crop Protection 124: 1-6.
50 Uppala, S., X. G. Zhou, B. Liu, and M. Wu. 2019. Plant-based culture media for improved growth and sporulation of Cercospora janseana. Plant Disease. 103:504-508.
51 Zhao, X., J. Zhang, D. Zhang, X. G. Zhou, X. Liu, and J. Xiu. 2019. Comparison between the effects of visible light and multispectral sensor based on low-altitude remote sensing platform in the evaluation of rice sheath blight. Spectroscopy and Spectral Analysis 39(4): 1191-1198
52 Zhou, X. G. 2019. Sustainable strategies for managing bacterial panicle blight in rice. In: Protecting Rice Grains in the Post-Genomic Era, ed. Yulin Jia, IntechOpen, DOI: 10.5772/intechopen.84882.
