Site-specific variable spraying is an effective approach to reducing pesticide use and improving the use efficiency for crop protection against disease, pests and weeds through chemical spraying, and target detection is a key procedure for site-specific variable spraying. Active illumination was adopted to detect green plant targets (crops and weeds), and the fluorescence spectral information of targets was analyzed. White, blue and red LEDs were utilized for illumination, and the spectra of green plants and others were collected in four circumstances, i.e., day-indoor, day-under sunshine, day-shadow, and night-dark environment. Classification models were built based on multi-wavebands spectral features using soft independent modeling of class analogy (SIMCA) and linear discriminant analysis (LDA) methods. Results showed that with the illumination of the three types of LEDs, the recognition rates for the prediction dataset using SIMCA models were all above 92%, and corresponding rejection rates were all 100%. The LDA models could predict all samples with 100% accuracy, performing better than SIMCA models. And the difference in the effect of the three types of LEDs was indistinguishable. -The objective function for classifying green plants and others was proposed, and the particle swarm optimization (PSO) method was used to select the optimal single waveband. The optimal waveband for the three types of LEDs (white, blue and red) was 731.1, 730.76 and 731.1 nm, respectively, and corresponding thresholding classification models were established. Results showed that the classification F1-scores for the three classification models were 76.71%, 80.52% and 78.48%, respectively. Under complex circumstances, the blue LED provided the best illumination for greed plant detection. The selected blue LED light source and optimal waveband are valuable for developing low-cost green plant sensors.