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UID:pretalx-global-workshop-2026-9G3MQF@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Amsterdam:20261007T181000
DTEND;TZID=Europe/Amsterdam:20261007T181500
DESCRIPTION:Fungal diseases such as Ascochyta remain a major obstacle to ch
 ickpea production\, leading to significant yield losses if not detected ea
 rly. Whilst hyperspectral imaging (HSI)\, combined with machine learning\,
  has demonstrated strong potential for early and non-destructive detection
  under controlled laboratory conditions\, its transferability to real-worl
 d field environments remains a major challenge. This study aims to validat
 e detection models developed in the laboratory under field conditions usin
 g multispectral images acquired by a drone. Following promising results ob
 tained using hyperspectral data (400–1000 nm) and advanced machine learn
 ing pipelines\, we have extended our approach to drone-based multispectral
  detection to assess its robustness in real-world agricultural scenarios. 
 Field data were acquired using drone-mounted sensors capturing key spectra
 l bands relevant to vegetation health and stress detection. A comprehensiv
 e processing pipeline was implemented\, comprising radiometric correction\
 , image pre-processing\, vegetation index calculation\, and feature extrac
 tion. The previously developed classification framework was adapted and ap
 plied to multispectral data\, incorporating both spectral and statistical 
 features. The results demonstrate that the proposed approach can be succes
 sfully applied from the laboratory to field conditions\, achieving high de
 tection performance with a classification accuracy of over 90% in distingu
 ishing healthy chickpea plants from infected ones. Furthermore\, the syste
 m proved capable of detecting signs of infection at an early stage in the 
 canopy\, despite environmental variability such as changes in light intens
 ity and ground background effects. These results confirm the feasibility o
 f deploying AI-based disease detection systems using drone-based multispec
 tral imaging in real agricultural environments. This work represents a sig
 nificant step towards operational precision agriculture solutions\, enabli
 ng large-scale monitoring\, early intervention\, reduced chemical inputs\,
  and improved crop management strategies. Future work will focus on valida
 tion over multiple seasons\, integration with close-range detection\, and 
 extension to other plant-pathogen systems.
DTSTAMP:20260624T084404Z
LOCATION:Aula Magna
SUMMARY:AI-Driven Early Detection of Chickpea Ascochyta Blight: From Contro
 lled Hyperspectral Analysis to UAV Multispectral Field Monitoring - Mohame
 d
URL:https://pretalx.earthmonitor.org/global-workshop-2026/talk/9G3MQF/
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