Huanglongbing (HLB) is an important disease of citrus that is spread mainly through a vector, psyllid (Diaphorina citri), that feeds predominantly on young leaves.  Given the selective feeding of the insect, treating only the young flush, instead of spraying the entire tree canopy, may be a prudent and economical way to control psyllids.  A young leaf detection module, intended for a spot sprayer system, was developed and tested in the lab under both static and dynamic conditions. It consisted of a four-band active optic sensor (570, 670, 750, and 870 nm), a control box, a data logger, and a Java program on a portable laptop computer. The Java program contains commands to operate the sensor and solenoid-controlled spray nozzles, as well as the classification algorithm to detect young leaves. The algorithm was based on either Euclidean Distance (ED) or Matching Measures (MM) methods of classification (for comparison) using different vegetation indices derived from the spectral reflectance data obtained with the four-band active sensor. A light-emitting diode (LED) in the control box comes on/off, with clicking sounds, to indicate the presence/absence of young leaves. When young leaves are detected, the nozzles of a spot sprayer should simultaneously open to apply spray. A laboratory arrangement (where the spot-sprayer component was left out) was set up and the module tested to characterize its potential utility. The static test was done to determine the repeatability error, %e_Rmax, of each of the four bands at four different target distances (TD) based on reflectance of young leaves. Overall, 570>870>750>670 nm, in decreasing order of %e_Rmax, and the 670 nm band was least influenced by TD. The dynamic test was performed to determine young leaf detection efficiency, η_YLD, and leaf discrimination efficiency, η_LD, of the system as a basis for comparing: 1) a subset of four vegetation indices (VI) against all nine originally considered; and 2) Euclidean Distance Method against Matching Measures. The treatments thus consisted of three classification methods (ED, MM80 (MM with cutoff = 80%), and MM70) and two sets of VIs (All and Sub).The results established that using the subset of VIs (η_LD > 50%) was better than using all nine (η_LD < 50%) and ED classification method was better than MM in discriminating young leaves. Also, sensor travel direction was not found to be significant. With respect to η_YLD, no significant difference was found between ED_Sub and MM70_Sub at the 95% confidence level. However, MM80_Sub was significantly lower than both but not significantly different from MM80_All . Based on η_LD, apart from ED_Sub that was significantly greater than ED_All , all other treatments were not significantly different. At the end, ED_Sub (η_YLD = 90.6%, η_LD = 62.5%) was better than MM70_Sub (η_YLD = 86.9%, η_LD = 58.1%) although this difference was not significant. Overall, these results show a good potential for discriminating young and old leaves; however, η_LD of 62.5% was not considered adequate and the system thus requires further testing and improvement.