Red Palm Weevil (RPW; Rhynchophorus ferrugineus) is a major and most deadly insect pest affecting date palms across the world. Newly hatched RPW larvae feed on internal tissues, pupate and reproduce causing severe damages to tree health, productivity and ultimately death of the tree itself. Due to quite high resilient nature and its capacity to fly over large distance (~ 50 km/day) had led to wider spread in rapid rates and pose difficulties to monitor and control in isolation. The rapid trans-continental distribution and expansion in host-range of this invasive nature of pest demands urgent attention to explore an integrated approach to map and monitor of its incidences, damages and risks in real time to aid the early detection and control to ensure sustainability of dateplam farking systems in the region. The target-oriented and eco-friendly sustainable approaches to quantity the risk factors, magnitude and direction in space-time (spatial and temporal scales) require a digital platform.
Recent advances in GeoAgro technology driven by Big Data, high-resolution earth observation systems, and machine learning in conjunction with cloud computing (e.g, google cloud) and huge of potential of engaging smart-citizen-science (e.g., farmers with smartphone, apps) have opened tremendous possibilities for near real-time tracking, mapping, and spatial analysis of key pests of date palm such as RPW as well as number of other pests and diseases (e.g. lesser date moth (Batrachedra amydraula), Dubas date bug (Ommatissus lybicus)). Location-based intelligence such as geotagging and agrotagging data (at tree and farm level) coupled with space and time-synchronized surveillance data and digital farm boundary in a cloud computing platform is one of the unexplored approaches never tested before. We leveraged ICARDA’s GeoAgro bigdata analytics in partnership with extension systems to carry out a first of its kind pilot use-case in a quite short duration with limited resources to map risk of RPW in few selected farmer extension systems in Abu Dhabi. The overarching objective of the research has been to develop GeoAgro driven approaches which is smart, efficient, economically viable and scalable to better understand the eco-physiology of the RPW and to support sustainable date palm farming systems. The first phase of the digital augmentation approach demonstrated the with semi-automated workflow from geotagging, agrotagging, aggregation and decision analytics through multisource data integration and interoperability at tree level to farm and system level inferences. Such digital analytics help to provide an insight into several unanswered questions related to ecophysiology of the pests, quantify key drivers for early detection and provide basis for designing optimal management strategies.
Initial testing and analysis of the digital augmentation found to be high potential for expansion with new sets of data assemblages (including linking ground data, drones and very high-resolution satellite data) in cloud computing domain to make real-time analytics a reality. This provides a demand-driven time-sensitive multi-functional decision support system that is scalable and user-friendly for selection of appropriate management measures to develop strategies for sustainable dateplam farming systems in the region. This will be a quite useful tool in support of the date palm sector not only facilitate systemic surveillance, aid early detection of RPW, but also help targeting site specific IPM interventions for biological control, provide useful information on production dynamics to accelerate the trade and value chains. The well-established digital platform provides the most-needed information on regular basis for improved management practices, quantify dynamics of damages (both biophysical, nutrition and economic aspects), choice of inter-crop rotation, resource use efficiency, soil-water-nutrient balance, agronomic practices, and the trade.
Smartphone Extension apps from digital field data collection, management, precision decisions on near-real time basis.
First smartphone app (beta) for multiple level field data collection, sharing and data visualization for the agro-ecosystem research, development and outreach. It is a citizen science based crowdsourcing effort to provide a FREE app for the scientific community and citizen science for the georeferenced field data collection, storing and sharing for the research and development. App helps users to share, visualize, and analyze geo-referenced data from various agro-ecosystem around the world.
You can download the GeoAgro App from