Sensor-based phenotyping of above-ground plant-pathogen interactions.

Area of Science:

• Agricultural Science
• Plant Pathology
• Sensor Technology

Background:

• Plant diseases significantly reduce global crop yields, necessitating effective detection methods.
• Precision agriculture and breeding rely on accurate quantification of plant disease signs and symptoms.
• Current plant phenotyping for biotic stresses is less developed than for abiotic stresses.

Purpose of the Study:

• To review and evaluate various non-invasive sensor technologies for detecting plant disease signs and symptoms on shoots.
• To assess the strengths and weaknesses of different sensing modalities in plant pathology.
• To provide insights into selecting and combining sensors for accurate plant disease monitoring.

Main Methods:

• Review of monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence, and thermal sensors.
• Inclusion of Raman spectroscopy, X-ray computed tomography, and optical coherence tomography.
• Analysis of sensor capabilities for measuring above-ground plant-pathogen interactions.

Main Results:

• Non-invasive sensor technologies offer high throughput and repeated measurements for plant phenotyping.
• Various sensors show potential for detecting specific signs and symptoms of plant diseases.
• Combining sensors and optimizing spatial resolution are key for accurate disease assessment.

Conclusions:

• Sensor technology is vital for advancing plant disease detection in crop management.
• Careful selection and integration of sensors can improve the accuracy of identifying plant diseases.
• Further development in phenotyping methods for biotic stresses is essential for sustainable agriculture.