Rapid, early, and accurate diagnosis, especially at the orchard level, is essential to contain and eliminate the Citrus greening (HLB) disease threat at an early stage of infection. The primary focus of this project was to determine if spectral reflectance of citrus tree canopy and leaves at the visible and near-infrared range can be used in distinguishing healthy and HLB-infected samples and develop a rugged sensor that can work under field conditions to detect HLB symptoms. Leaf spectral reflectance data of HLB infected and healthy trees were collected in the lab and under field conditions. Lab data were collected with a Varian UV-VIS-NIR spectroradiometer (400-2500 nm) and field data were collected with an ASD spectroradiometer (400 -2500 nm). Partial least squares (PLS) modeling and discriminant analysis techniques identified HLB under field conditions and in a greenhouse with artificial lights. Results supports that these techniques have the potential to discriminate for HLB in different types of citrus. A second set of experiment was conducted to evaluate if a reduced spectral range (400-900 nm) is sufficient for detecting HLB symptoms. Overall, the full range of data (400-2500 nm) gave more accurate results compared to a narrow range with both techniques. It seems that the narrow range can produce very good results if the HLB symptoms are visible. Additional tests are underway to determine at what stages of HLB infection the anomaly in spectral characteristics can be detected using ground hyperspectral imaging and portable spectrometer. An aerial hyperspectral image (397-995 nm) of a citrus orchard with HLB infected trees was acquired and analyzed using hyperspectral imaging software (ENVI, Research System Inc.) and its toolboxes. The software was able to correctly identify most of the HLB infected trees; however, it identified many false positive trees which could be the results of significant GPS error in ground truth data. In spite of some initial wrong identification, it seems that aerial hyperspectral imaging still has potential for detecting HLB if error sources can be eliminated. Especially, more accurate ground truth data and higher resolution aerial images could enhance the results significantly. Based on the results from PLS analysis of the hyperspectral data, four spectral bands (570, 670, 870, and 970 nm) were selected and a prototype of a portable rugged, four-band active optic sensor was developed. This active optic sensor can operate under different light conditions, which is very desirable for use in the field. It is also a very fast responding sensor that can quickly scan for unhealthy trees. This sensor is very suitable for field applications and an array of them can monitor the full canopy. A series of tests were conducted to evaluate the optimal working condition of the sensor. Also tests were conducted to evaluate the potential of this sensor in detecting HLB infected leaves with symptoms, HLB infected leaves without symptoms and healthy leaves. Twelve different index were calculated from the output of the sensor and preliminarily results shows that two of the indices were able to separate these three groups successfully. More field tests are in progress to verify these results and to determine how this sensor responds to other diseases or nutrient deficiency with similar symptoms. In general the project is on track to achieve its objectives. Based on the preliminary results from this study a second proposal was submitted to FCPRAC for expanding and continuation of this work.