Hyperspectral drone imaging and deep learning platform for cacao plantation health monitoring and variety classification

Overview

This solution applies drone-mounted hyperspectral imaging combined with deep learning models to monitor and characterize cacao plantations, with an initial focus on Venezuelan fine Criollo cocoa. The platform integrates three analytical models: physiological condition assessment through water and CO2 absorption and NDVI indices, pathogen detection, and variety classification among Criollo, Forastero, and Trinitario cacao types. By capturing detailed biochemical signatures across many contiguous wavelengths, the system enables precise, non-invasive evaluation of plantation health and genetic diversity, supporting modernization of fine cocoa cultivation.

Technical specifications

Key features:

• Hyperspectral drone sensors capture reflected and transmitted radiance across many contiguous wavelengths for detailed biochemical characterization
• Physiological model measures water and CO2 absorption and computes NDVI indices to evaluate growth conditions
• Pathogen detection model trained on healthy plants and samples affected by major cacao fungal pathogens
• Variety classification model distinguishes Criollo, Forastero, and Trinitario cacao varieties
• Deep learning architectures combine spectral data with biochemical property recognition
• Quality validated through classification error metrics

Background expertise:

• Prior participation in Venezuela's national chocolate research program, including micropropagation of 110 fine cacao varieties using somatic embryogenesis and temporary immersion bioreactors
• Molecular labeling methodology for pathogen identification using ITS markers
• Ecophysiology studies of Venezuelan cacao across multiple regions

Technology readiness level

The platform is in an early research and development stage. Foundational biological knowledge has been established through prior national research programs covering cacao micropropagation, pathogen diversity, and ecophysiology. The current proposal focuses on building and validating the hyperspectral imaging and deep learning models. Future validation will involve applying the system to identify and monitor physiological characteristics of fine Venezuelan Criollo cocoa in modern plantation settings, with model performance measured by classification accuracy.