Back

Odor-based real-time detection and identification of pests and diseases attacking crop plants

Arce, C. M.; Mamin, M.; Roder, G.; Kanagendran, A.; Degen, T.; Defossez, E.; Rasmann, S.; AKIYAMA, T.; MINAMI, K.; YOSHIKAWA, G.; Lopez-Hilfiker, F.; Cappellin, L.; Turlings, T.

2024-07-29 plant biology
10.1101/2024.07.29.605549 bioRxiv
Show abstract

Early detection of crop pests and diseases can enable timely, targeted interventions, and help reduce pesticide use. Plants under biotic stress are known to rapidly emit characteristic blends of volatile compounds that could potentially serve as early and attacker-specific cues for precise pest monitoring. Here, we evaluated the feasibility of this approach using two complementary, state-of-the-art sensing technologies: a handheld nanomechanical membrane-based sensor array and chemical ionization time-of-flight mass spectrometry. Under laboratory conditions, with enclosed headspace sampling, both technologies readily distinguished undamaged maize plants from plants infested by caterpillars or infected with a fungal pathogen. Under semi-controlled outdoor open-air conditions, where volatile concentrations were strongly diluted, the membrane-based sensor no longer retained discriminatory power, whereas mass spectrometry predicted herbivory status with more than 90% accuracy using one-second measurements. Finally, in an initial field trial based on simulated herbivory, a compact, field-deployable, real-time mass spectrometer distinguished damaged from undamaged maize plants with highly encouraging performance under real field conditions. Together, these results demonstrate the potential of odor-based detection of pest attacks in maize and identify real-time mass spectrometry as a promising tool for crop monitoring, while pinpointing challenges that remain to be addressed for translation to practical field applications.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.