Crop Ecophysiology &
Precision Agriculture

Guo Precision Ag Lab

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.

Creating a More Efficient Future

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.

Publications

Google Scholar

ResearchGate

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. 2024. 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.

People

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, particularly UAV application in precision plant phenotyping. 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 PSS5329 - Precision Agriculture, PSS6301 - Quantitative Agricultural Remote Sensing, and PSS6302 - Plant Growth Modeling.

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