Hyperspectral Reflectance and Machine Learning Approaches for the Detection of Drought and Root–Knot Nematode Infestation in Cotton

Upland cotton encounters biotic and abiotic stresses during the growing season, which significantly affects the genetic potential of stress tolerance and productivity. The root-knot nematode (RKN) (Meloidogyne incognita) is a soilborne roundworm affecting cotton production. The occurrence of abiotic stress (drought stress, DS) can alter the plant–disease (RKN) interactions by enhancing host plant sensitivity. Experiments were conducted for two years under greenhouse conditions to investigate the effect of RKN and DS and their combination using nematode-resistant (Rk-Rn-1) and nematode susceptible (M8) cotton genotypes. These genotypes were subjected to four treatments: control (100% irrigation with no nematodes), RKN (100% irrigation with nematodes), DS (50% irrigation with no nematodes), and DS + RKN (50% irrigation with nematodes). We measured treatments-induced changes in cotton (i) leaf reflectance between 350 and 2500 nm; and (ii) physiology and biomass-related traits for diagnosing plant health under combined biotic and abiotic stresses. We used a maximum likelihood classification model of hyperspectral data with different dimensionality reduction techniques to learn RKN and DS stressors on two cotton genotypes. The results indicate (i) the RKN stress can be detected at an early stage of 10 days after infestation; (ii) RKN, DS, and DS + RKN can be detected with an accuracy of over 98% using bands from 350–1000 nm and 350–2500 nm. The genotypes ‘Rk-Rn-1’and ‘M8’ showed differential responses to DS, RKN, and DS + RKN. With a few exceptions, all three stressors reduced the pigments, physiology, and biomass traits and the magnitude of reduction was higher in ‘M8’ than ‘Rk-Rn-1’. Observed impact of stressors on plant growth followed DS + RKN > DS > RKN. Similarly, leaf reflectance properties exhibited a significant difference between individual stress treatments indicating that the hyperspectral sensor data can be used to discriminate RKN-infected plants from drought-stressed plants. Thus, our study reveals that hyperspectral and physiological changes in response to RKN and DS could help diagnose plant health before visual symptoms.