Klebsiella pneumoniae 2N3 is a strain of gram-negative bacteria that can degrade chlorimuron-ethyl and grow with chlorimuron-ethyl as the sole nitrogen source. The complete genome of Klebsiella pneumoniae 2N3 was sequenced using third generation high-throughput DNA sequencing technology. The genomic size of strain 2N3 was 5.32 Mb with a GC content of 57.33% and a total of 5156 coding genes and 112 non-coding RNAs predicted. Two hydrolases expressed by open reading frames (ORFs) 0934 and 0492 were predicted and experimentally confirmed by gene knockout to be involved in the degradation of chlorimuron-ethyl. Strains of ΔORF 0934, ΔORF 0492, and wild type (WT) reached their highest growth rates after 8–10 hours in incubation. The degradation rates of chlorimuron-ethyl by both ΔORF 0934 and ΔORF 0492 decreased in comparison to the WT during the first 8 hours in culture by 25.60% and 24.74%, respectively, while strains ΔORF 0934, ΔORF 0492, and the WT reached the highest degradation rates of chlorimuron-ethyl in 36 hours of 74.56%, 90.53%, and 95.06%, respectively. This study provides scientific evidence to support the application of Klebsiella pneumoniae 2N3 in bioremediation to control environmental pollution.

Soybean bacterial blight disease is a threat to soybean production. Multispectral technology has shown good potential in detecting this disease and can overcome the limitations of traditional methods. The aim of this study was to perform field monitoring of the dynamics of this disease in Northeast China in 2022. The correlation between the soybean chlorophyll content index (CCI) and disease grade was obtained using artificial inoculation of the pathogen. The correlation between the soybean CCI, disease grade, green normalized difference vegetation index (GNDVI), and soybean yield was analyzed using a drone-mounted spectrometer platform for image acquisition and preprocessing. The soybean CCI was negatively correlated with the disease grade. The GNDVI declined with disease progression, which allowed for an indirect determination of the disease grade. The soybean yield loss was significant at disease grade 4 for soybean bacterial blight disease. The random forest regression model was more accurate than the regression model in estimating the yield based on the GNDVI. Therefore, the GNDVI could be used to survey the disease class and estimate the yield using the random forest model. This study provides support for field trials of drone-mounted multispectral equipment. This surveillance approach holds the potential to bring about precision plant protection in the future.