What is Precision Agriculture?

Precision agriculture is managing each crop production input on a site-specific basis to reduce waste, increase profits and maintain the quality of the environment. Precision agriculture is a set of managerial practices whereby spatial variability in a field can be controlled on a micro scale. The main idea behind precision agriculture is spatial variability. This refers to all of the ways a field can differ from one location to another. Variability can include: soil pH, soil moisture, soil depth, soil type, soil texture, topography, pest populations, nutrient levels, organic matter content, etc. The reason precision agriculture can be an effective practice is it defines more accurately the needs of specific locations of individual fields. This in turn can increase the efficiency of crop management in ways that were financially, and scientifically unattainable in the past .

Tools For Precision Agriculture:

• Global Navigation Satellite System (GNSS) and Geographic Information System (GIS) - These systems allow for precise mapping and interpretation of variability in a field.
• Yield Monitoring and Mapping - These practices record and collect data about crop yield from specific locations of a field.
• Soil Testing - This practice defines soil characteristics based on differences in availability of nutrients and variable between testing locations. When used in concert with GPS it has very powerful uses such as variable rate application.
• Variable Rate Technology and Application - There are two basic methods of Variable Rate Application (VRA), map-based and sensor based. Map-based VRA adjust the application rate based on an electronic map, also called a prescription map. Using GPS field position can be determined, based on the prescription map the rate, or concentration of input can be changed as you drive through the field. Sensor-based VRA requires no map or positioning system. Sensors on the application machine measure soil properties or crop characteristics "on-the-go". Based on this continuous stream of information a computer program calculates the input needs of the soil or plants and then transfers the information to an applicator which delivers the input to the needed location.
• Crop Scouting and Ground Truthing - These practices aid soil testing and accuracy of GPS and GIS.
• Remote Sensing - Remote sensing is gathering information about an object without being in contact with it. The two most common methods of remote sensing are aerial photography and satellite imagery. Electromagnetic (EM) energy is the basic element of remote sensing. When electromagnetic radiation comes in contact with an object it can be reflected, absorbed or transmitted. Depending on the characteristics of the object different wavelengths of EM radiation respond differently. Plants have a distinct reflectance pattern thus enabling us to interpret this pattern and gather information about plant health, plant moisture content, plant nutrient levels, etc.

For more information on precision agriculture visit:

• University of Nebraska, Lincoln's Precision Agriculture Homepage
• Ohio State University Precision Agriculture
• University of Minnesota Precision Agriculture
• Texas A&M University Precision Agriculture
• NASA's Remote Sensing Tutorial
• American Society for Photogrammetry and Remote Sensing
• GPS World Magazine
• Trimble
• International Society of Precision Agriculture (ISPA)

Research

Current Projects

• Two-spotted spider mite detection using artificial intelligence
• Strawberry plant wetness detection
• Strawberry plant canopy volume detection

Teaching

• View Classes

  AOM 3333: Pesticide Application Techniques

  Semester Taught - Fall
  Credits: 3
  Equipment and methods used to apply pesticides in agriculture. Techniques used to avoid misapplication, a common cause of pesticide failures, are emphasized.

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  AOM 4434: Precision Agriculture

  Semester Taught - Spring
  Credits: 3
  Principles and applications of technologies supporting precision farming and planning for natural resource data management. Global positioning system (GPS), yield monitoring and mapping, remote sensing, geographic information system (GIS), variable rate technologies (VRT), data layering of independent variables, Internet information access, and computer software for management.

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  AOM 5435: Advanced Precision Agriculture

  Semester Taught - Fall, even years
  Credits: 3
  Principles and applications of technologies supporting precision farming and natural resource data management planning. Global positioning system (GPS), geographic information system (GIS), variable rate technologies (VRT), data layering of independent variables, field sensors and computer software for precision farming.

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  AOM 5334C: Agricultural Chemical Application Technology

  Semester Taught - Fall
  Credits: 3
  Equipment and methods used to apply pesticides in agriculture. Emphasis on techniques to avoid misapplication and pesticide drift.

Publications

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2020  2019  2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001

2020

• SH Park, Y Hong, M Shuaibu, S Kim, WS Lee. 2020. Detection of Apple Marssonina Blotch with PLSR, PCA, and LDA Using Outdoor Hyperspectral Imaging. SPECTROSCOPY AND SPECTRAL ANALYSIS 40 (4), 1309-1314.
• DM Queiroz, EDTS Sousa, WS Lee, JK Schueller. 2020. Development and Testing of a low-Cost Portable Apparent Soil Electrical Conductivity Sensor Using a BeagleBone Black. Applied Engineering in Agriculture 36(3), 341-355 .

2019

• MM Shimwela, SE Halbert, ML Keremane, P Mears, BH Singer, WS Lee,JB Jones, RC Ploetz, AHC van Bruggen. 2019. In-Grove Spatiotemporal Spread of Citrus Huanglongbing and Its Psyllid Vector in Relation to Weather. Phytopathology 109 (3), 418-427.
• H Gan, WS Lee, N Peres, C Fraisse. 2019. Development of A Multi-Angle Imaging System for Automatic Straw-berry Flower Counting. Association for the Advancement of Artificial Intelligence (www.aaai.org).
• Y Chen, WS Lee, H Gan, N Peres, C Fraisse, Y Zhang, Y He. 2019. Strawberry Yield Prediction Based on a Deep Neural Network Using High-Resolution Aerial Orthoimages. Remote Sensing 11 (13), 1584.
• Y Zhang, M Li, L Zheng, Q Qin, WS Lee. 2019. Spectral features extraction for estimation of soil total nitrogen content based on modified ant colony optimization algorithm. Geoderma 333, 23-34.

2018

• Gan, H., Lee, W.S., Alchanatis, V., Ehsani, R., Schueller, J.K. 2018. Immature green citrus fruit detection using color and thermal images. Computers and Electronics in Agriculture, 152 (2018): 117-125.
• Gan, H., Lee, W.S., Alchanatis, V. 2018. A photogrammetry-based image registration method for multi-camera systems - with applications in images of a tree crop. Biosystems Engineering, 174 (2018): 89-106.
• Gan, H., Lee, W.S., Alchanatis, V., Abd-Elrahman, A. 2018. An active thermography method for immature citrus fruit detection. Proceedings of the 14th ICPA.
• Lu, J., Lee, W. S., Gan, H., Hu, X. 2018. Immature citrus fruit detection based on local binary pattern feature and hierarchical contour analysis. Biosystems Engineering, 171(2018): 78-90.
• Wang, C., Lee, W. S., Zou, X., Choi, D., Gan, H., Diamond, J. 2018. Detection and counting of immature green citrus fruit based on the local binary patterns (LBP) feature using illumination-normalized images. Precision Agriculture, https://doi.org/10.1007/s11119-018-9574-5.
• Shuaibu, M., Lee, W. S., Schueller, J., Gader, P., Hong, Y.K., Kim, S. 2018. Unsupervised hyperspectral ban selection for apple Marssonina blotch detection. Computers and Electronics in Agriculture, 148(2018): 45-53.

2017

• Barocco, R., W. S. Lee, and G. Hortman. 2017. Yield mapping hardware components for grains and cotton using on-the-go monitoring systems. UF/IFAS EDIS
• Khedher Agha, M. K., W. S. Lee, C. Wang, R. W. Mankin, A. R. Blount, R. A. Bucklin, and N. Bliznyuk. 2017. Detection and prediction of Sitophilus oryzae infestations in triticale via visible and near infrared spectral signatures. Journal of Stored Products Research
• Queiroz, D., Lee, W.S., Schueller, J. K., Santos, E.D.T. (2017). Development and test of a low cost portable soil apparent electrical conductivity sensor using a Beaglebone Black. In the proceeding of 2017 ASABE AIM, Spokane, Washington DOI: 10.13031/aim.201700062
• Gan, H., Lee, W.S., Alchanatis, V. (2017). A prototype of an immature citrus fruit yield mapping system. In the proceeding of 2017 ASABE AIM, Spokane, Washington DOI: https://doi.org/10.13031/aim.201700164
• Choi, D., Lee, W.S., Schueller, J. K., Ehsani, R., Roka, F., Diamond, J. (2017). A performance comparison of RGB, NIR, and depth images in immature citrus detection using deep learning algorithms for yield prediction. In the proceeding of 2017 ASABE AIM, Spokane, Washington DOI: https://doi.org/10.13031/aim.201700076
• Khedher Agha, M.K., Bucklin, R.A., Lee, W. S., Mankin, R.W., Blount, A.R. (2017). Effect of Drying Conditions on Triticale Seed Germination and Rice Weevil Infestation. Transactions of ASABE, Vol. 60(2):571-575
• Pourreza, A., Lee, W. S., Czarnecka, E., Verner, L., & Gurley, W. (2017). Feasibility of Using the Optical Sensing Techniques for Early Detection of Huanglongbing in Citrus Seedlings. Robotics, 6(2)
• Shuaibu, M., Lee, W. S., Hong, Y. K., & Kim, S. (2017). Detection of Apple Marssonina Blotch Disease using Particle Swarm Optimization. Transactions of ASABE, Vol. 60(2): 303-312

2016

• Ni, Z., Burks, T. F., & Lee, W. S. (2016). 3D Reconstruction of Plant/Tree Canopy Using Monocular and Binocular Vision. Journal of Imaging 2(4), 28.
• Zhao, C., Lee, W. S., & He, D. (2016). Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove. Computers and Electronics in Agriculture, 124, 243-253.
• Choi, D., Lee, W. S., Ehsani, R., Schueller, J., & Roka, F. M. (2016). Detection of dropped citrus fruit on the ground and evaluation of decay stages in varying illumination conditions. Computers and Electronics in Agriculture, 127, 109-119.
• Li, H., Lee, W. S., & Wang, K. (2016). Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images. Precision Agriculture, 1-20.
• Yun, H. S., Park, S. H., Kim, H. J., Lee, W. D., Do Lee, K., Hong, S. Y., & Jung, G. H. (2016). Use of Unmanned Aerial Vehicle for Multi-temporal Monitoring of Soybean Vegetation Fraction. Journal of Biosystems Engineering, 41(2), 126-137.
• Cubero, S., Lee, W. S., Aleixos, N., Albert, F., & Blasco, J. (2016). Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest—a review. Food and Bioprocess Technology, 9(10), 1623-1639.

2015

• Pourreza, Alireza, Lee, Won Suk, Reza Ehsani, Schueller, John K, and Raveh, Eran. An Optimum Method for Real-Time In-Field Detection of Huanglongbing Disease Using a Vision Sensor. (2015). Computers and Electronics in Agriculture, Elsevier, Vol. 110, 221–232. (doi: 10.1016/j.compag.2014.11.021)
• Pourreza, Alireza, Lee, Won Suk, Etxeberria, Edgardo, and Banerjee, Arunava. An evaluation of a vision based sensor performance in Huanglongbing disease identification. (2015). Biosystems Engineering, Elsevier, Vol. 130, 13-22. (doi:10.1016/j.biosystemseng.2014.11.013)
• Yang, Ce, Lee, Won Suk, and Gader, Paul. Blueberry maturity stage detection based on spectral-spatial detection of hyperspectral image using selected bands. (2015). 2015 ICRA
• Pourreza, Alireza, Lee, Wonsuk, Combs, Rebekah , Roberts, Pamela, Ritenour, Mark. Spectral band selection to design a low cost sensor for citrus black spot disease detection.(2015). In Proceeding of the 2015 ASABE Annual International Meeting. New Orleans, Louisiana. (doi: 10.13031/aim.20152181312)
• Choi, D., W. S. Lee, R. Ehsani, Schueller, John K, and Roka, Fritz. (2015). Machine vision system for early yield estimation of citrus in a site-specific manner. ASABE Paper No. 152181863 . St. Joseph, Mich.: ASABE.
• Li, X., Lee, W. S., Li, M., Ehsani, R., Mishra, A. R., Yang, C., & Mangan, R. L. (2015). Feasibility study on Huanglongbing (citrus greening) detection based on WorldView-2 satellite imagery. Biosystems Engineering, Elsevier, 132, 28-38.
• Lee, W. S., & Ehsani, R. (2015). Sensing systems for precision agriculture in Florida.Computers and Electronics in Agriculture, Elsevier, Vol. 112, 2-9.
• Choi, D., W. S. Lee, R. Ehsani, Schueller, John K, and Roka, Fritz. (2014). A Machine Vision System for Quantification of Citrus Fruit Dropped on the Ground under the Canopy . Trans. ASABE, 58(4) 933-946.
• Kweon, Giyoung, Lund, Eric D., Maxton, Chase, Lee, Won Suk, and Mengel, David B. (2014). Comparison of soil phosphorus measurements. Trans. ASABE, 58(2) 405-414.

2014

• Pourreza, A., Lee W.S., Ehsani R. (2014). “A Vision Based Sensor for Huanglongbing Disease Detection under a Simulated Field Condition ,” In Proceeding of the 2014 ASABE Annual International Meeting. Montreal, Canada. (doi: 10.13031/aim.20141900251)
• Pourreza, A., Lee W.S. (2014). “Effect Of Starch Accumulation In Huanglongbing Symptomatic Leaves On Reflecting Polarized Light,” In Proceeding of the 12th ICPA International Conference, Sacramento, California, USA.
• Pourreza, A., Lee W.S., Etxeberria E. (2014). “Rapid in-field diagnosis of Huanglongbing disease using computer vision,” In Proceeding of the  127th Florida State Horticultural Society Annual Meeting, Clearwater, Florida, USA.
• Pourreza, A., Lee, W. S., Raveh, E., Ehsani, R., & Etxeberria, E. (2014). Citrus Huanglongbing Disease Detection Using Narrow Band Imaging and Polarized Illumination. Trans. ASABE, 57(1).
• Kurtulmus, F., W. S. Lee, and A. Vardar. 2014. Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network. Precision Agriculture 15: 57-79.
• Sengupta, S., and W. S. Lee. 2014. Identification and determination of the number of immature green citrus fruit under different ambient light conditions. Biosystems Engineering 117: 51-61.

2013

• Bansal, R., W. S. Lee, and S. Satish. 2013. Green citrus detection using Fast Fourier Transform (FFT) leakage. Precision Agriculture 14(1): 59-70.
• Garcia-Ruiz, F., S. Sankaran, J. M. Maja, W. S. Lee, J. Rasmussen, and R. Ehsani. 2013. Comparison of two aerial imaging platforms for identification of Huanglongbing infected citrus trees. Computers and Electronics in Agriculture 91: 106-115.
• Li, H., W. S. Lee, K. Wang, R. Ehsani, and C. Yang. 2013. ‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging. Precision Agriculture.
• Sengupta, S., and W. S. Lee. 2013. Identification and determination of the number of immature green citrus fruit under different ambient light conditions. Biosystems Engineering.
• Yang, C. and W. S. Lee. 2013. Precision agricultural systems. In Agricultural automation: fundamentals and practices. Eds. Q. Zhang and F. J. Pierce. CRC Press.
• Choi, D., W. S. Lee, R. Ehsani, and A. Banerjee. 2013. Detecting and counting citrus fruit on the ground using machine vision. ASABE Paper No. 131591603. St. Joseph, Mich.: ASABE.
• Katti, A. R., W. S. Lee, and C. Yang. 2013. Laser weeding system for elimination of in-row weeds. In Proceedings of the 5th Asian Conference on Precision Agriculture (ACPA), June 25-28, 2013, Jeju, Korea.
• Khedher Agha, M. K., W. S. Lee, C. Wang, R. W. Mankin, N. Bliznyuk, and R. A. Bucklin. 2013. Determination degrees of insect infestation in triticale seed using NIR spectroscopy. ASABE Paper No. 131592957. St. Joseph, Mich.: ASABE.
• Khedher Agha, M. K., W. S. Lee, R. A. Bucklin, A. A. Teixeira, and A. Blount. 2013.Equilibrium moisture content equation for triticale seed. ASABE Paper No. 131620333. St. Joseph, Mich.: ASABE.
• Li, H., W. S. Lee, and K. Wang. 2013. Airborne hyperspectral imaging based citrus greening disease detection using different dimension reduction methods. ASABE Paper No. 131592802. St. Joseph, Mich.: ASABE.
• Li, H., W. S. Lee, and K. Wang. 2013. Spectral mixture analysis based citrus greening disease detection using satellite image of Florida. In Proceedings of the 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). 25-28 June 2013, Gainesville, Florida, USA.
• Pourreza, A., W. S. Lee, E. Raveh, Y. K. Hong, and H. J. Kim. 2013. Identification of citrus greening disease using a visible band image analysis. ASABE Paper No. 131591910. St. Joseph, Mich.: ASABE.
• Saber, M., W. S. Lee, T. F. Burks, G. E. MacDonald, and G. Salvador. 2013.An automated mechanical weed control system for organic row crop production. ASABE Paper No. 131593595. St. Joseph, Mich.: ASABE.
• Yang, C., W. S. Lee, and P. Gader. 2013. Band selection of hyperspectral imagery for the classification of blueberry fruit maturity stages and leaf. ASABE Paper No. 131593276. St. Joseph, Mich.: ASABE.
• Yang, C., W. S. Lee, P. Gader, and H. Li. 2013. Hyperspectral band selection using Kullback-Leibler divergence for blueberry fruit detection. In Proceedings of the 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). 25-28 June 2013, Gainesville, Florida, USA.

2012

• Shin, J., W. S. Lee, and R. Ehsani. 2012. Postharvest citrus mass and size estimation using logistic classification model and watershed algorithm. Biosystems Engineering 113(1): 42-53.
• Ruslan, R., R. Ehsani, and W. S. Lee. 2012. Quantification of total soluble solids and titratable acidity for citrus maturity using VIS-NIR spectroradiometer. Applied Engineering in Agriculture 28(5): 735-743.
• Kumar, A., W. S. Lee, R. Ehsani, L. G. Albrigo, C. Yang, and R. L. Mangan. 2012. Citrus greening disease detection using aerial hyperspectral and multispectral imaging techniques. Journal of Applied Remote Sensing 6, 063542, http://link.aip.org/link/doi/10.1117/1.JRS.6.063542.
• Mishra, A. R., D. Karimi, R. Ehsani and W. S. Lee. 2012. Identification of citrus greening (HLB) using a VIS-NIR spectroscopy technique. Trans. ASABE 55(2): 711-720.
• Li, X., W. S. Lee, M. Li, R. Ehsani, A. Mishra, C. Yang, and R. L. Mangan. 2012. Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Computers and Electronics in Agriculture 83: 32-46.
• Sengupta, S., and W. S. Lee. 2012. Identification and determination of the number of green citrus fruit under different ambient light conditions. International Conference of Agricultural Engineering CIGR-AgEng2012, July 8-12, 2012, Valencia, Spain.
• Li, H., W. S. Lee, K. Wang, R. Ehsani, and C. Yang. 2012. Spectral angle mapper (SAM) based citrus greening disease detection using airborne hyperspectral imaging. 11th International Conference on Precision Agriculture, Indianapolis, Indiana.
• Lee, W. S. 2012. Sensing technologies for precision agriculture: current status and future needs. Proceedings of the 6th International Symposium on Machinery and Mechatronics for Agriculture and Biosystems Engineering (ISMAB), June 18-20, 2012, Jeonju, Korea.
• Yang, C. and W. S. Lee. 2012. Blueberry fruit detection by Bayesian classifier and support vector machine based on visible to near-infrared multispectral imaging. ASABE Paper No. 12-1338433. St. Joseph, Mich.: ASABE.
• Zheng, L., W. S. Lee, M. Li, A. Katti, C. Yang, and H. Li. 2012. Analysis of soil phosphorus concentration based on Raman spectroscopy. SPIE Asia-Pacific Remote Sensing 2012, Kyoto, Japan, 29 Oct. – 1 Nov., 2012.
• Mason, A. D., R. Schnell, J. Ferrell, J. Sartain, and W. S. Lee. 2012. Comparing grid and directed zone soil sampling schemes for peanut production. ASA, CSSA, and SSSA International Annual Meetings, Oct. 21-24, 2012, Cincinnati, Ohio.
• Khosro, F. A., J. M. Maja, R. Ehsani, and W. S. Lee. 2012. An automated tine control for tractor drawn citrus canopy shakers. ASABE 2012 Annual Meeting, July 29-Aug. 1, 2012, Dallas, Texas.
• Li, H., W. S. Lee, K. Wang, R. Ehsani, and C. Yang. 2012. Spectral angle mapper (SAM) based citrus greening disease detection using airborne hyperspectral imaging. 11th International Conference on Precision Agriculture, Indianapolis, Indiana.

2011

• Kurtulmus, F., W. S. Lee, and A. Vardar. 2011. Green citrus detection using eigenfruit, color and circular Gabor texture features under natural outdoor conditions. Computers and Electronics in Agriculture 78(2): 140-149.
• Bansal, R., W. S. Lee, R. Shankar, and R. Ehsani. 2011. Automated debris mass estimation for citrus mechanical harvesting systems using machine vision. Applied Engineering in Agriculture 27(5): 673-685.
• Han, Y., W. S. Lee, C. Lee, S. Park, K. Kim, and S. Kim. 2011. Entrapment of Mg-Al layered double hydroxide in calcium alginate beads for phosphate removal from aqueous solution. Desalination and Water Treatment. 36: 178-186.
• Kurtulmus, F., W. S. Lee, and A. Vardar. 2011. An advanced green citrus detection algorithm using color images and neural networks. 11th International Congress on Mechanization and Energy in Agriculture, Sep. 21-23, 2011, Istanbul, Turkey. Journal of Agricultural Machinery Science, 7(2): 145-151.
• Bansal, R., W. S. Lee and S. Satish. 2011. Green citrus detection using Fast Fourier Transform (FFT) leakage. 8th European Conference on Precision Agriculture, July 11-14, 2011, Prague, Czech Republic.
• Kurtulmus, F., W. S. Lee, and A. Vardar. 2011. An advanced green citrus detection algorithm using color images and neural networks. 11th International Congress on Mechanization and Energy in Agriculture. Sep. 21-23, 2011, Istanbul, Turkey.
• Yang, C. and W. S. Lee. 2011. Spectral signatures of blueberry fruits and leaves. ASABE Paper No. 1110582. St. Joseph, Mich.: ASABE.
• Li, X., W. S. Lee, M. Li, R. Ehsani, A. Mishra, C. Yang, and R. Mangan. 2011. Comparison of different detection methods for citrus greening disease based on airborne multispectral and hyperspectral imagery. ASABE Paper No. 1110570. St. Joseph, Mich.: ASABE.

2010

• Lee, W. S., V. Alchanatis, C. Yang, M. Hirafuji, D. Moshou, and C. Li. 2010. Sensing technologies for precision specialty crop production.Computers and Electronics in Agriculture 74(1): 2-33.
• Jones, C. D., J. B. Jones, and W. S. Lee. 2010. Diagnosis of bacterial spot of tomato using spectral signatures.Computers and Electronics in Agriculture74(2):329-335.
• Kumar, A., W. S. Lee, R. Ehsani, L. G. Albrigo, C. Yang, and R. L. Mangan. 2010. Citrus greening disease detection using airborne multispectral and hyperspectral imaging. 10th International Conference on Precision Agriculture. July 18-21, 2010, Hyatt Regency Tech Center, Denver, Colorado.
• Patil, R., W. S. Lee, R. Ehsani, and F. Roka. 2010. Elimination of debris using de-stemmers on a continuous citrus canopy shake and catch harvester. ASABE Paper No. 1008384. St. Joseph, Mich.: ASABE.
• Bansal, R., W. S. Lee, R. Shankar, and R. Ehsani. 2010. Automated trash estimation in a citrus canopy shake and catch harvester using machine vision. ASABE Paper No. FL10-123. St. Joseph, Mich.: ASABE.

2009

• Patil, R., W. S. Lee, R. Shankar, and R. Ehsani. 2009. Detection and elimination of trash using machine vision and extended de-stemmer for a citrus canopy shake and catch harvester. ASABE Paper No. FL09-129. St. Joseph, Mich.: ASABE.
• Chinchuluun, R., W. S. Lee, and R. Ehsani. 2009. Machine vision system for determining citrus count and size on a citrus canopy shake and catch harvester. Applied Engineering in Agriculture 25(4): 451-458.
• Okamoto, H., and W. S. Lee. 2009. Green citrus detection using hyperspectral imaging. Computers and Electronics in Agriculture 66(2): 201-208.
• Kumar, A., W. S. Lee, R. Ehsani, and L. G. Albrigo. 2009. Airborne hyperspectral imaging for citrus greening disease detection. In the Proceedings of the 3rd Asian Conference on Precision Agriculture, Beijing , China .
• Okamoto, H., and W. S. Lee. 2009. Computer vision technique for green citrus recognition in tree image. In the Proceedings of the 4th IFAC International Workshop on Bio-Robotics, Information Technology, and Intelligent Control for Bioproduction Systems, September 10–11, 2009, I Hotel and Conference Center, Champaign, Illinois.

2008

• Lee, W. S., R. Ehsani, and R. Shankar. 2008. Trash detection system for a citrus canopy shake and catch harvester using machine vision. ASABE Paper No. 084249. St. Joseph, Mich.: ASABE.
• Lehtola, C. J. and W. S. Lee. 2008. U. S. agricultural safety issues and trends. 3rd Korea-Japan Joint Seminar on “Safety for agricultural machinery”. September, 23, 2008. National Institute of Agricultural Engineering, Rural Development Administration, Suwon, Korea.
• Lee, W. S., R. Ehsani, and L. G. Albrigo. 2008. Citrus greening (Huanglongbing) detection using aerial hyperspectral imaging. 9th International Conference on Precision Agriculture. July 20-23, 2008, Hyatt Regency Tech Center, Denver, Colorado.
• Lee, W. S. 2008. Precision agriculture: methods and applications. United Nations/Colombia/United States of America Workshop on the Applications of Global Navigation Satellite Systems. Medellin, Colombia, June 23-27, 2008.
• Lee, W. S., L. Estrada, and R. Espinel. 2008. Precision agriculture in Ecuador. 2008 Florida Section ASABE Annual Conference and Tradeshow. June 12-13, 2008 – Duck Key, Florida.
• Lee, W. S. 2008. Precision agriculture: methods and possibilities. Invited presentation at Escuela Superior Politécnica del Litoral (ESPOL), March 9-15, Guayaquil, Ecuador.
• Lee, W. S., R. Chinchuluun, and R. Ehsani. 2008. Citrus fruit identification using machine vision for a canopy shake and catch harvester. International Symposium: “Application of precision agriculture for fruits and vegetables”. January 6-9, 2008. Grosvenor Resort, Orlando, Florida.
• Chinchuluun, R., Lee, W., Bhorania, J., and Pardalos, P. M. 2008. Clustering and classification algorithms in food and agricultural applications: A survey. In Papajorgji, P. ed. "Advances in Modeling Agricultural Systems." Berlin : Springer.
• Lee, W., Bogrekci, I. and Min, M. 2008. Modeling in nutrient sensing for agricultural and environmental applications." In Papajorgji, P. ed. "Advances in Modeling Agricultural Systems. Berlin : Springer.
• Min, M., W. S. Lee, T. F. Burks, J. D. Jordan, A. W. Schumann, J. K. Schueller, and H. Xie. 2008. Design of a hyperspectral nitrogen sensing system for citrus. Computers and Electronics in Agriculture 63(2): 215-226.

2007

• Chinchuluun, R., W. S. Lee, and R. Ehsani. 2007. Citrus yield mapping system on a canopy shake and catch harvester. ASABE Paper No. 073050. St. Joseph, Mich.: ASABE.
• Kane, K. E., and W. S. Lee. 2007. Multispectral imaging for in-field green citrus identification. ASABE Paper No. 073025. St. Joseph, Mich.: ASABE.
• Okamoto, H., W. S. Lee, and K. E. Kane. 2007. Hyperspectral imaging for green citrus fruit detection. 6th European Conference in Precision Agriculture and 2nd European Conference on Precision Livestock Farming, June 3-6, 2007, Skiathos , Greece .
• Cohen, M. J., R. Mylavarapu, I. Bogrekci, W. S. Lee, and M. W. Clark. 2007. Reflectance Spectroscopy for Routine Agronomic Soil Analyses. Soil Science 172(6): 469-485.
• Bogrekci, I. and W. S. Lee. 2007. Comparison of Ultraviolet, Visible, and Near Infrared Sensing for Soil Phosphorus. Biosystems Engineering 96(2): 293-299.

2006

• Bogrekci, I., and W. S. Lee. 2006. Effects of soil moisture content on absorbance spectra of sandy soils in sensing phosphorus concentrations using UV-VIS-NIR spectroscopy. Trans. ASABE 49(4): 1175-1180.
• Bogrekci, I., and W. S. Lee. 2006. The effect of particle size on sensing phosphorus by Raman spectroscopy. ASABE Paper No. 063048. St. Joseph, Mich.: ASABE.
• Chinchuluun, R., and W. S. Lee. 2006. Citrus yield mapping system in natural outdoor scenes using the Watershed transform. ASABE Paper No. 063010. St. Joseph, Mich.: ASABE.
• Kane, K. E., W. S. Lee. 2006. Spectral sensing of different citrus varieties for precision agriculture. ASABE Paper No. 061065. St. Joseph, Mich.: ASABE.
• Min, M., and W. S. Lee. 2006. Design of a hyperspectral nitrogen sensing system for citrus. ASABE Paper No. 061061. St. Joseph, Mich.: ASABE.
• MacArthur, D. K., J. K. Schueller, W. S. Lee, C. D. Crane, E. Z. MacArthur, and L. R. Parsons. 2006. Remotely-piloted helicopter citrus yield map estimation. ASABE Paper No. 063096. St. Joseph , Mich. : ASABE.
• Min, M., W. S. Lee, Y. H. Kim, and R. A. Bucklin. 2006. Nondestructive detection of nitrogen in Chinese cabbage leaves using VIS- NIR spectroscopy. HortScience 41(1): 162-166.
• Pydipati, R., T. F. Burks, and W. S. Lee. 2006. Identification of citrus disease using color texture features and discriminant analysis. Computers and electronics in agriculture 52(1): 49-59.

2005

• Bogrekci, I., and W. S. Lee. 2005. Spectral measurement of common soil phosphates. Trans. ASAE. 48(6): 2371-2378.
• Lee, W. S., J. K. Schueller, and T. F. Burks. 2005. Wagon-based silage yield mapping system. Agr. Engng. Intl. Vol. VII. Manuscript IT 05 003, website: cigr-ejournal.tamu.edu.
• Bogrekci, I. , and W. S. Lee. 2005. Spectral soil signatures and sensing phosphorus. Biosystems Engineering 92(4): 527-533.
• Pydipati, R., T. F. Burks, and W. S. Lee. 2005. Statistical and neural network classifiers for citrus disease detection using machine vision. Trans. ASAE. 48(5): 2007-2014.
• Bogrekci, I., and W. S. Lee. 2005. Improving phosphorus sensing by eliminating soil particle size effect in spectral measurement. Trans. ASAE. 48(5): 1971-1978.
• Shin, D. J., W. S. Lee, and S. Y. Na. 2005. Dynamic load estimation in silage harvesting. Trans. ASAE 48(4): 1131-1320.
• Bogrekci, I., and W. S. Lee. 2005. A Raman sensor for phosphorus sensing in soil and vegetations. ASAE Paper No. 051040. St. Joseph, Mich.: ASAE.
• Bogrekci, I., W. S. Lee, and J. D. Jordan. 2005. Multispectral image analysis for phosphorus measurement in Bahia grass. ASAE Paper No. 051067. St. Joseph, Mich.: ASAE.
• Min, M., and W. S. Lee. 2005. Hyperspectral nitrogen sensing system for citrus. ASAE Paper No. 051068. St. Joseph, Mich.: ASAE.
• Na, S. Y., W. S. Lee, D. J. Shin, J. Y. Kim, and B. H. Lee. 2005. Crop flow measurement using photosensors to reduce disturbance error in yield estimation. ASAE Paper No. 051131. St. Joseph, Mich.: ASAE.
• Regunathan, M., and W. S. Lee. 2005. Citrus yield mapping and size determination using machine vision and ultrasonic sensors. ASAE Paper No. 053017. St. Joseph, Mich. ASAE.
• Sevier, B. J., and W. S. Lee. 2005. Precision farming adoption in Florida citrus: A grower case study. ASAE Paper No. 051054. St. Joseph, Mich.: ASAE.
• Bogrekci, I., W. S. Lee, and J. D. Jordan. 2005. Airborne hyperspectral imaging for sensing phosphorus concentrations in the Lake Okeechobee basins. In Proc. SPIE Defense & Security Symposium, Vol. 5795, p. 106-112, Chemical and Biological Sensing VI. P. J. Gardner ed. March 28-April 1, 2005. Gaylord Palms Resort and Convention Center, Orlando, FL.
• Bogrekci, I. and W. S. Lee. 2005. Spectral phosphorus mapping using diffuse reflectance of soils and grass. Biosystems Engineering 91(3): 305-312.
• Sevier, B. J., and W. S. Lee. 2005. Precision farming adoption by Florida citrus producers: probit model analysis. UF/IFAS Extension Circular 1461 - DLN AE283.
• Min, M., and W. S. Lee. 2005. Determination of significant wavelengths and prediction of nitrogen content for citrus. Trans ASAE 48(2): 455-461.

2004

• Annamalai, P., W. S. Lee, and T. F. Burks. 2004. Color vision system for estimating citrus yield in real-time. ASAE Paper No. 043054. St. Joseph, Mich.: ASAE.
• Annamalai, P., and W. S. Lee. 2004. Identification of green citrus fruits using spectral characteristics. ASAE Paper No. FL04-1001. St. Joseph, Mich.: ASAE.
• Bogrekci, I., W. S. Lee, and J. P. Herrera. 2004. Spectral signatures for the Lake Okeechobee soils using UV-VIS-NIR spectroscopy and predicting phosphorus concentrations. ASAE Paper No. 041076. St. Joseph, Mich.: ASAE.
• Bogrekci, I., and W. S. Lee. 2004. Soil particle size effect on absorbance spectra of sandy soils in UV-VIS-NIR regions. ASAE Paper No. 043112. St. Joseph, Mich.: ASAE.
• Shoup, W. D., W. S. Lee, and T. Harrison. 2004. Precision technologies for precision management. In Agricultural Systems Management: Optimizing Efficiency and Performance, 245-276. R. M. Peart and W. D. Shoup, eds. New York, N.Y.: Marcel Dekker.
• Bogrekci, I., and W. S. Lee. 2004. Determining phosphorus concentration using spectral reflectance of grass and soil samples in the Lake Okeechobee basins in UV, VIS, and NIR regions. In Proc. 7th International Conference on Precision Agriculture.
• Bogrekci, I., and W. S. Lee. 2004. The effects of soil moisture content on reflectance spectra of soils using UV-VIS-NIR spectroscopy. In Proc. 7th International Conference on Precision Agriculture.
• Herrera, J. P., W. S. Lee, and I. Bogrekci. 2004. Spectral based phosphorus sensing in the Lake Okeechobee Basin. ASAE Paper No. 041077. St. Joseph, Mich.: ASAE.
• Min, M., W. S. Lee, and I. Bogrekci. 2004. The effect of water and variety on nitrogen sensing of citrus leaf. ASAE Paper No. 041080. St. Joseph, Mich.: ASAE.
• Lee, W. S., and D. C. Slaughter. 2004. Recognition of partially occluded plant leaves using a modified Watershed algorithm. Trans. ASAE 47(4): 1269-1280.
• Sevier, B. J., and W. S. Lee. 2004. Precision agriculture in citrus: a Probit model analysis for technology adoption. ASAE Paper No. 041092. St. Joseph, Mich.: ASAE.
• Shoup, W. D., W. S. Lee, and T. Harrison. 2004. Precision technologies for precision management. In Agricultural Systems Management: Optimizing Efficiency and Performance, 245-276. R. M. Peart and W. D. Shoup, eds. New York, N.Y.: Marcel Dekker.

2003

• W. S. Lee, J. F. Sanchez, R. S. Mylavarapu, and J.S. Choe. 2003. Estimating chemical properties of Florida soils using spectral reflectance. Trans. ASAE 46(5): 1443-1453
• "Citrus yield mapping system using machine vision", ASAE Paper No. 031002
• "Adoption trends and attitudes towards precision agriculture in Florida citrus: preliminary results from a citrus producer survey", ASAE Paper No. 031100
• "Spectral-based nitrogen sensing for citrus", ASAE Paper No. 031137
• "Assessment of P-concentration in the Lake Okeechobee drainage basins with spectroscopic reflectance of VIS and NIR", ASAE Paper No. 031139
• "Data fusion in yield maps to reduce errors using distinctive types of sensors", ASAE Paper No. 031035

2002

• "Silage yield monitoring system", ASAE Paper No. 021165
• "Temporal Variation of Soil Ca, Mg, and K using Precision Agriculture in Florida ", ASAE Paper No. FL 2002-19.
• "Comparing interpolation methods for mapping soil properties: Ca, Mg, and K", ASAE Paper No. FL 2002-20.

2001

• "Study on soil properties and spectral characteristics in Florida", ASAE Paper No. 011179

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