Optimize

flight planning and acquisition of hyperspectral images with drones.

Improve

the quality of UAV-based hyperspectral images and automate the workflow.

Enhance

performance of in-house ML algorithms to early identify the plants affected by the disease with rigorous validation with field measurements.

Commercialize

the monitoring services and the decision support system.

HYGRI

HYGRI aimed to acquire and use UAV-based hyperspectral imagery to monitor and detect plant pathogens in fruit trees and to visualize the information in a user-friendly decision support system. Existing know-how at AVT-ASI provides the basis for operating the UAV system and detecting plants affected by a number of diseases, for example flavescence dorée in vineyards, and for analyzing plant health status, but advances were needed to solve challenges in the acquisition and processing phases and in the choice of the machine learning approach.

waves

DJI Matrice 600 Pro, DJI Mavic 2 Enterprise Dual

AFX10 hyperspectral sensor

Gimbal

GPS STONEX S8 Plus GNSS

Drone Data analytics models

Model 1: Spectral analysis

Model 2: Classification of plants based on health status

Model 3: Multi-temporal analysis

Model 4: Thermal images analysis"

Outcomes

Correspondence between vegetation indices and ground sensors (TER)

Classification of stressed / unstressed plants

Advances with MAPPIS visualization tool

Challenges Faced

UAV-flight execution

Local people were suspicious (use of drones during war was influencing)

Ιnformative material used, contact details in the company and farms

Staff was wearing uniforms to be recognized

Topography of terrain, presence of obstacles

Flight plans modified

Image classification with respect to health status

Number of samples of unhealthy plants not sufficient to train the model

Impact (Socio-economic & Environmental)

Socio-economic impact

Lower exposure to chemicals for farmers

Support for sustainable land management

Enhancement of efficiency in agriculture and costs reduction

Positive effects on rural innovation

Ιncreased trust in digital technologies

Environmental impact

Water resources optimization

Pesticide reduction

Early detection of diseases

Wide areas monitoring at low impact

Improved crop management 

Optimization of resource use