Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: June 29, 2006
Publication Date: July 24, 2006
Citation: Johnson, R.M., Grisham, M.P., Richard Jr, E.P., Zimba, P.V. 2006. Utilization of Leaf Reflectance Measurements in Louisiana Sugarcane Disease, Variety, and Harvest Management Systems. In: Proceedings of the 8th International Conference on Precision Agriculture, July 23-26, 2006, Minneapolis, Minnesota. 2006 CDROM. Interpretive Summary: Louisiana’s sugarcane producers and millers have been under increased economic pressure for the past several years. If the industry is to survive, new technologies that maximize productivity and profitability must be identified and adopted. Several tests were initiated in 2005 to ascertain if leaf reflectance measurements at specific wavelengths could be used to determine disease presence, identify varieties, and predict sucrose levels. In the first test, leaf samples were collected at several dates before and after the appearance of visual symptoms from sugarcane yellow leaf disease (SCYLD) and mosaic test plots. Samples exhibiting either mild or severe mosaic symptoms could be correctly identified with leaf reflectance in 75 and 68% of the cases, respectively. Disease-free controls could be identified in 77% of the cases. Results also showed that leaf reflectance could be used to identify samples infected with SCYLV (sugarcane yellow leaf virus) in 77% of the cases. The SCYLV-infected leaves also had lower levels of most plant pigments compared to non-infected controls. In a second study, leaf samples were collected from plots in a historical sugarcane nursery containing seven generations of varieties selected for sucrose accumulation over a time period of more than eighty years. Reflectance measurements were effective in correctly identifying 80% of the varieties present in the study. In the final test, leaf samples were collected from the Sugarcane Research Unit’s 2005 first-stubble maturity study, which examines the natural ripening of released and soon-to-be released varieties, on three separate dates, ranging from early to late in the harvest season. Theoretically Recoverable Sugar (TRS) levels ranged from 134 to 317 lb/T. Leaf reflectance was effective at predicting TRS values in 77% of the cases in the combined data set. The successful development of remote sensing techniques would help growers identify yield limiting crop disease outbreaks at earlier stages so that corrective actions could be taken in a timely and efficient manner. These techniques could also have a potential benefit to varietal development programs by allowing for increased accuracy and efficiency in varietal selection. Finally, growers and mill personnel could potentially use a simple leaf sample taken in the field to develop ripener treatment and harvest schedules that could aid in maximizing sugar yields.
Technical Abstract: Tests were initiated in 2005 to determine if visible and near-infrared leaf reflectance measurements could be used to predict sugarcane disease incidence, identify varieties, and estimate sucrose content. Leaf samples were collected from: sugarcane yellow leaf disease (SCYLD) and mosaic test plots; a nursery containing thirty-five varieties, both modern and obsolete, selected for sucrose accumulation; and, a sugarcane maturity study. In all cases, leaf reflectance measurements were made in the laboratory. For the SCYLD study, leaf discs were collected for pigment analysis, and reverse-transcriptase, polymerase chain reaction (RT-PCR) analysis to confirm disease presence. Results showed that leaves exhibiting, mild or severe mosaic symptoms could be correctly identified with reflectance measurements in 75 and 68% of the cases, respectively. Reflectance measurements correctly identified plants with and without SCYLD in 77% of the cases. Sugarcane with SCYLD also had lower levels of most plant pigments (chlorophyll a & b, '-carotene, neoxanthin, and violaxanthin) compared to controls. In varietal discrimination tests, reflectance measurements were found to be effective in correctly identifying 79% of the varieties tested. Finally, in maturity studies, reflectance data was effective at predicting sucrose levels in 77% of the cases. These combined results indicate that leaf reflectance measurements may offer a method to predict disease incidence, identify varieties and estimate sucrose levels remotely and non-destructively.