A new publication by CERTH and IKH, supported by E-SPFdigit and PestNu, has been featured in the Agriculture Journal by MDPI. This study explores the use of AI-driven detection for early Botrytis cinerea disease (gray mold) in pepper crops.

A new publication, supported by E-SPFdigit and PestNu, has been featured in the Agriculture Journal by MDPI. This study explores the use of AI-driven detection for early Botrytis cinerea disease (gray mold) in pepper crops.

Key Highlights:

Botrytis cinerea is a major threat to pepper crops, causing significant yield loss and increasing the reliance on fungicides.
This study uses the YOLO deep learning algorithm for single-class segmentation of hyperspectral and RGB images.
By combining advanced Transformer models and ensemble classifiers, the research achieves an impressive 87.42% overall accuracy in identifying disease stages.
The methodology also integrates qPCR-based methods for fungal biomass quantification, offering a comprehensive disease detection strategy.

This research is a step forward in precision agriculture, with the potential for real-time, on-site detection using mobile apps or robotic systems, significantly reducing economic loss and fungicide use.

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