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• QuEChERS CEN version was modified for determine 12 acidic pesticides in cabbage and spinach. • Carbopack Z, a high surface area graphitized carbon black, was used as sorbent material. • Carbopack Z and C18 mixture showed excellent matrix component removal ability in clean-up. • The extract was 10-fold diluted before LC-MS/MS injection, which reduced matrix effect efficiently. • The method was validated following SANTE/12682/2019 directions. A quick, easy, cheap, effective, rugged, and safe (QuEChERS) method was developed and combined with liquid chromatography–tandem mass spectrometry to analyze 12 acidic pesticides in cabbage and spinach. The extraction solvents, phase partition salts and sorbents effect was studied to optimize the method followed by dilution before sample injection. The extraction involved 5% formic acid in acetonitrile, and the liquid-liquid partition was salt-induced. Carbopack Z, a high surface area graphitized carbon black, was a new sorbent used in the clean-up. The results show that Carbopack Z effectively removes interferences with little loss of acidic pesticides. All tested pesticide recoveries were satisfactory when Carbopack Z was combined with C18 in the clean-up at optimized condition. After clean-up, the extract was subjected to 10-fold dilution to sufficiently reduce the matrix effect (<20%). The limit of quantification (LOQ) was 1–5 ng/g, and the mean recovery was between 95 and 110% with a relative standard deviation <20% (between 2% and 10%) for the spiking of three concentrations: 5, 50, and 500 ng/g. The extract was less pigmented in the modified QuEChERS method than its original version. Thus, the modified method is a useful alternative for investigating the acidic pesticide residues in cabbage and spinach. [ABSTRACT FROM AUTHOR]

• An Airborne hyperspectral imaging system was used to monitor kimchi cabbage downy mildew disease. • The Red-edge, Green, and Blue band (ReGB) composite combination was used to visualize early disease symptoms. • SLIC algorithm was used to generate a leaf-by-leaf dataset and then trained on the 3D-ResNet model. • DBSCAN algorithm was used to group nearby leaves for disease severity assessment. Kimchi cabbage (Brassica rapa pekinensis) , one of the main agricultural products in Korea, is susceptible to downy mildew disease infections. Infected plants develop yellow spots (chlorosis) on the upper (adaxial) side of the infected leaf, undermining cabbage production and quality. An early detection method to recognize and treat the disease is crucial to prevent downy mildew and lessen its physical effects on plants. Hyperspectral imaging can capture data from a broad spectrum, which can be utilized to detect disease occurrence before any visible symptoms appear. Combining a hyperspectral camera with an unmanned aerial vehicle (UAV) can provide a non-destructive, field-scale disease detection system. In this study, three-dimensional (3D) convolutional neural network (CNN) models were used to simultaneously account for the spectral and spatial features of the disease to enable automatic disease detection. Using a 3D-residual network (ResNet) CNN with four residual blocks, each followed by a rectified linear unit activation function and a max-pooling layer, helped achieve an overall accuracy of 0.876 and a diseased class accuracy of 0.873. Disease severity was estimated by grouping nearby diseased leaves using the density-based spatial clustering of applications with noise clustering algorithm to achieve a 27.07 % relative error or a 1.08 level difference from the actual. [ABSTRACT FROM AUTHOR]