Advances in digital camera-based phenotyping of Botrytis disease development

Area of Science:

• Plant pathology
• Agricultural science
• Digital phenotyping

Background:

• Botrytis cinerea is a widespread fungal pathogen causing substantial economic damage to crops.
• Traditional visual inspection methods for B. cinerea are subjective, labor-intensive, and not amenable to AI/ML analysis.
• Novel remote and proximal sensing techniques offer objective, digital data for plant disease assessment.

Purpose of the Study:

• To detail the B. cinerea infection process and its detection.
• To evaluate digital phenotyping methods for detecting, quantifying, and classifying B. cinerea symptoms.
• To discuss the future of camera-based phenotyping in disease management and resistance breeding.

Main Methods:

• Review of B. cinerea infection biology.
• Evaluation of conventional visual disease assessment techniques.
• Analysis of digital camera-based phenotyping methods for remote and proximal sensing.

Main Results:

• Conventional methods for B. cinerea detection are limited in accuracy and scalability.
• Digital phenotyping provides objective, quantifiable data for improved disease detection and classification.
• Camera-based techniques show promise for AI/ML integration in plant disease management.

Conclusions:

• Digital phenotyping offers a significant advancement over traditional methods for B. cinerea detection.
• Camera-based sensing is crucial for developing objective, data-driven approaches in plant pathology.
• Further research is needed to address challenges and optimize digital phenotyping for practical applications in disease resistance breeding and management.