In pigeonpea, resistance against vascular wilt disease was assessed based on leaf images captured throughred-green–blue (RGB) and chlorophyll fluorescence imaging sensors. At leaf level, wilt response in RGB images was characterized by changes in pixel intensities in red, green, and blue channels leading to variation in texture. Texture analysis based on gray level co-occurrence matrix (GLCM) was able to explain variation pattern between resistance and susceptible genotypes. Extracted texture features particularly contrast and energy were significantly different between the two genotype groups. Training of a neural network model for contrast and energy feature enabled genotype prediction with 79–98% accuracy. Healthy leaf area estimated based on photosynthetic or quantum efficiency (F_v/F_m > 0.75 as healthy) in chlorophyll fluorescence images, indicated significant variation (p < 0.05) between genotype groups at 10–25 days after inoculation (dpi). In susceptible genotype, healthy area was observed to decrease in significant proportion over time as compared to resistant type. Resistant genotype was less sensitive to infection as healthy leaf area (Fv/F_m > 0.75) remained unaffected between 10-25dpi.At canopy level, although differences in pixel intensity (Fv/Fm > 0.75) were noted between inoculated and healthy (mock) particularly in susceptible types but differences between inoculated susceptible and resistant type were non-significant (p > 0.05). Although trained ML algorithms for leaf and canopy level images resulted low accuracy (41–54%) in genotype classification but with large number of images captured later than 15 dpi expected to increase in accuracy. A protocol to facilitate non-invasive imaging techniques in association with machine learning tools is proposed over the tedious, time consuming and error-prone conventional screening method.