Conventional farming practices treat an agricultural field uniformly despite the inherent
variability in soil properties and crop growth conditions. The challenge and necessity
of feeding 9 billion people on the earth by 2050 urges us to use our land and water
resources more wisely.
At the Crop Ecophysiology & Precision Agriculture Laboratory, we strive to improve
the bottom line of production agriculture through optimization of crop inputs, management,
while protecting the environment.
The challenge and necessity of feeding 9 billion people on the earth by 2050 stimulates
us to use our land and water resources more wisely. Conventional farming practices
treat an agricultural field uniformly despite the inherent variability in soil properties
and crop growth conditions. Uniform management may result in over- or under-application
of resources in specific locations within a field, which may have a negative impact
on the environment, profitability, and sustainability of agricultural production.
One solution is to manage our soil and water more efficiently and effectively by applying
crop inputs at the right location with the right amount and at the right time. Precision
agriculture offers a suite of technologies to implement such intensive management.
These technologies include global navigation satellite systems, geographic information
systems, remote and proximal sensing, yield monitoring, variable rate technology,
and data science. This list is expanding. The goal of precision agriculture is to
optimize crop input and management to enhance productivity, profitability, sustainability,
while minimizing the impact of agriculture on the environment.
At the Crop Ecophysiology & Precision Agriculture Laboratory, we strive to improve
the bottom line of production agriculture through optimization of crop inputs, management,
while protecting the environment. Our mission is to build on partnerships with the
industry and research institutions to develop and expand innovative research and teaching
programs in precision agriculture and environmental modeling that align well with
the department and university plans, address important local and national agricultural
needs, and support the economic development of Texas and the nation through education
and research.
Our research interests include studies at a range of scales, from individual plant,
experimental plot, commercial field, to regional, national, or even international
levels. Specific research projects include irrigation scheduling based on weather
and satellite imagery, variable rate water and nutrient application, remote sensing
for plant phenotyping and growth monitoring, crop growth modeling and simulation,
environmental modeling and hazard prediction, etc. The lab maintains state-of-the-art
facilities to support our research, including sophisticated UAS platforms and sensors,
high performance computation power and software programs, as well as various radiometers,
field spectrometers, calibration equipment, and ecosystem measurement instrumentation.
Ghimire, B., Adedeji, O., Ritchie, G., & Guo, W. (2025). Simulating crop yields and
water productivity for three cotton-based cropping systems in the Texas High Plains.
Crop and Environment. https://doi.org/10.1016/j.crope.2025.03.001.
Adedeji, O., Abdalla, A., Ghimire, B., Ritchie, G., & Guo, W. (2024). Flight altitude
and sensor angle affect unmanned aerial System cotton plant height assessments. Drones,
8(12), 746. https://doi.org/10.3390/drones8120746.
Gu, H., Mills, C., Ritchie, G., & Guo, W. (2024). Water stress assessment of cotton
cultivars using unmanned aerial system images. Remote Sensing, 16(14), 2609, https://doi.org/10.3390/rs16142609.
Neupane, J., Wang, C., Ritchie, R., Zhang, F., Deb, S., & Guo, W. (2024). Spatial
and temporal patterns of cotton profitability in management zones based on soil properties
and topography. Precision Agriculture, 25(4):1-24. https://doi.org/10.1007/s11119-024-10158-5.
Abdalla, A., Karn, R., Adedeji, O, & Guo, W. (2024). Dual-stage color calibration
of UAV imagery using multivariate regression and deep learning. Computers and Electronics
in Agriculture, 224 (2024), 109170.
Karn, R. Hillin, D., Helwi, P., Scheiner, J., Guo, W. 2024. Assessing grapevine vigor
as affected by soil physicochemical properties and topographic attributes for precision
vineyard management. Scientia Horticulturae, 328: 112857.
Abdalla, A., Wheeler, T.W., Dever, J. Lin, J., Arce, J., and Guo, W. 2024. Assessing
fusarium oxysporum disease severity in cotton using unmanned aerial system images
and a hybrid domain adaptation deep learning time series model. Biosystems Engineering
237, 220-231.
Singh, A., Deb, S.K., Slaughter, L.C., Singh, S., Ritchie, G.L., Guo, W., and Saini,
R. 2023. Simulation of root zone soil water dynamics under cotton-silverleaf nightshade
interactions in drip-irrigated cotton. Agricultural Water Management, 288, 108479.
Rabia, A., J. Neupane, Z. Lin, K. Lewis, G. Cao, and W. Guo. 2022. Principles and
Applications of Topography in Precision Agriculture. Advances in Agronomy, 171: 143-189.
https://doi.org/10.1016/bs.agron.2021.08.005.
Neupane, J., W. Guo, G. Cao, F. Zhang, L. Slaughter, and S. Deb. 2022. Spatial patterns
of soil microbial communities and implications for precision soil management at the
field scale. Precision Agriculture. 23: 1008–1026. DOI: 10.1007/s11119-021-09872-1.
Neupane, J., W. Guo, C. West, F. Zhang, and Z. Lin. 2021. Effects of Irrigation Rates
on Cotton Yield as Affected by Soil Physical Properties and Topography in the Southern
High Plains. Plos One 16(10): e0258496. https://doi.org/10.1371/journal.pone.0258496.
Gikunda, R. M., D. Lawver, M. Baker, A. Boren, and W. Guo. 2021. Extension education
needs for improved adoption of sustainable organic agriculture in Central Kenya. American
Journal of Geographic Information System, 10(2): 61-71.
Lin, Z., and W. Guo. 2021. Cotton Stand Counting from Unmanned Aerial System Imagery
using MobileNet and CenterNet Deep Learning Models. Remote Sensing, 13(14), 2822.
Sun, Y., W. Guo, D. Weindorf, F. Sun, S. Deb, G. Cao, J. Neupane, Z. Lin, A. Raihan.
2021. Field-scale Calcium Spatial Variability: Implications for Soil Erosion and Site-specific
Management. Pedosphere. 31(5): 705-714.
Wen M., W. Zhao, W. Guo, X. Wang, P. Li, J. Cui, Y. Liu, and F. Ma. 2021. Coupling
effects of reduced nitrogen, phosphorus and potassium on drip-irrigated cotton growth
and yield formation in northern XinJiang. Archives of Agronomy and Soil Science. 68(9):
1239-1250. https://doi.org/10.1080/03650340.2021.1881776.
Gu, H., Z. Lin, W. Guo, and S. Deb. 2021. Retrieving Surface Soil Water Content Using
a Soil Texture Adjusted Vegetation Index and Unmanned Aerial System Images. Remote
Sensing 2021, 13(1), 145; https://doi.org/10.3390/rs13010145.
Lin, Z., and W. Guo. 2020. Sorghum Panicle Detection and Counting using Unmanned
Aerial System Images and Deep Learning. Frontiers in Plant Science, 11:534853. doi:
10.3389/fpls.2020.534853.
Lin, Y., Z. Zhu, W. Guo, Y. Sun, X. Yang, and X. Kovalskyy. 2020. Continuous Monitoring
of Cotton Stem Water Potential using Sentinel-2 Imagery. Remote Sens. 12, 1176.
Pabuayon, I. L., Y. Sun, W. Guo, and G. Ritchie. 2019. High-Throughput Phenotyping
in Cotton: A review. Journal of Cotton Research, 2(1), 2-18.
Gusso, A., W. Guo, and S. Rolim. 2019. Reflectance-based model for soybean mapping
in United States at common land unit scale with Landsat 8. European Journal of Remote
Sensing, 52(1): 522-531.
Thompson, C., W. Guo, B. Sharma, and G. Ritchie. 2019. Using Normalized Difference
Red Edge index to assess maturity in cotton. Crop Science, 59(5): 2167-2177.
Neupane, J., and W. Guo. 2019. Agronomic basis, technology, and benefits of precision
water management: a review. Agronomy 9(2): 87. doi:10.3390/agronomy9020087.
Guo, W. 2018. Spatial and temporal trends of irrigated cotton yield in the Southern
High Plains. Agronomy 8(12): 298. doi:10.3390/agronomy8120298.
Guo, W. 2018. Application of geographic information system and automated guidance
system in optimizing contour and terrace farming. Agriculture 8(9): 142. doi: 10.3390/agriculture8090142.
Chen, T., R. Zeng, W. Guo, X. Hou, Y. Lan, and L. Zhang. 2018. Detection of stress
in cotton (Gossypium hirsutum L.) caused by aphids using leaf level hyperspectral
measurements. Sensors 18(9), 2798. doi:10.3390/s18092798.
Guo, W., S. Cui, J. Torrion, and N. Rajan. 2015. Data-Driven Precision Agriculture:
Opportunities and Challenges. In Soil-Specific Farming: Precision Agriculture. L.
Rattan and B. A. Stewart (eds.). CRC Press, Boca Raton, FL. P.353–372. doi: 10.1201/b18759-15.
Torrion, J. A., S. J. Maas, W. Guo, J. P. Bordovsky, and A.M. Cranmer. 2014. A three-dimensional
index for characterizing crop water stress. Remote Sensing 6: 4025-4042.
Guo, W., S.J. Maas, and K.F. Bronson. 2012. Relationship between cotton yield and
soil electrical conductivity, topography, and Landsat imagery. Precision Agriculture
13: 678-692.
Guo, W., and S.J. Maas. 2012. Terrace layout design utilizing geographic information
system and automated guidance system. Applied Engineering in Agriculture 28:31-38.
Ko, J., G. Piccinni, W. Guo, and E. Steglich. 2009. Parameterization of EPIC crop
model for simulation of cotton growth in South Texas. Journal of Agricultural Science
147: 169-178.
Todd, R.W., N.A. Cole, R.N. Clark, W.C. Rice, and W. Guo. 2008. Soil nitrogen distribution
and deposition on shortgrass prairie adjacent to a beef cattle feedyard. Biology and
Fertility of Soils. 44(8): 1099-1102. doi: 10.1007/s00374-008-0286-2.
Todd, R., W. Guo, B.A. Stewart, and C. Robinson. 2004. Vegetation, phosphorus, and
dust gradients downwind from a cattle feedyard. Journal of Range Management 57: 291-299.
Meng, Q., A. Meng, J. Wang, Z. Liu, W. Guo, and M. Cui. 1998. The path analysis of
main quantity characters and dry leaf yield of Stevia Rebaudiano Bertoni. Journal
of Jilin Agricultural University 20:17-19.
Lab PI
Wenxuan Guo, Ph.D. Associate Professor of Crop Ecophysiology & Precision Agriculture
Dr. Guo's research efforts are centered in precision agriculture, environmental sciences,
and remote sensing in agriculture. One of his primary goals is to lead interdisciplinary
research and teaching programs that leverage state-of-the-art technologies to improve
agricultural production with limited resources, especially water. He teaches courses
on Precision Agriculture, Quantitative Agricultural Remote Sensing, and Plant Growth
Modeling.
