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.
Or here
Find us at our socials at our Linktree:
#ESPFDigit #ESFPD #Horizoneurope #SoilHealth #HEU #STI #SoilManagement #ClimateAction #SoilDealforEurope
News & Events
The Road to Sustainability and Soil Protection Seminar by APEMETA
On December 13th, E-SPFdigit was highlighted at the seminar “The...
Read MoreA 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 by CERTH and IKH, supported by E-SPFdigit...
Read MoreProximal Sensing, Drones & Soil: Join the Future of Soil Health Monitoring
Proximal Sensing, Drones & Soil: Join the Future of Soil...
Read More