Singapore-based DroneDash Technologies and GEODNET have formed a joint venture called GEODASH Aerosystems to build agricultural spraying drones for large-scale industrial farms. The companies say this near-production drone technology is designed to eliminate the need to map the field to be processed before each flight or to rebuild flight plans when ground conditions change.
The aircraft is able to recognize its surroundings during flight and adjust its movements depending on the footage it captures to spray crops.
Current agricultural spraying drones are based on generic models developed outside the industry, requiring human operators on farms to survey and map each field, create a flight plan for each spraying job, and repeat the mapping process when canopy conditions change. The technology is designed to be cost-effective on very large plots of land, especially palm oil plantations where crops are planted in rows, and this required preparation and coordination time can limit the area of land that teams can cover.
GEODASH says its platform is built to eliminate the need for such preliminary steps. The drone combines DroneDash’s AI vision system with GEODNET’s position correction technology to achieve accuracy down to 1 centimeter. Drones can interpret columns, trees, terrain, and operational zones in the air. You can adjust the altitude and spray amount according to changing conditions.
The difference in smart robotics is whether the machine can operate in a changing environment. Structured spaces (assembly lines, warehouses, etc.) present simpler operating parameters. However, in the case of agriculture, autonomous, real-time decision making is required. In agricultural land, especially in plantation terrain with a mix of crops of different ages and variable plant growth, drones need to be aware of all relevant physical features and change their flight paths and processing patterns according to unpredictable conditions.
In this sense, the perfect agricultural machine should be able to combine perceptual and positional abilities and damp its motion depending on environmental conditions. Deterministic systems are not well-suited for this kind of use case because you can’t hard-code all randomly occurring edge cases.
The solution proposed by GEODASH Aerosystems is not a completely unsupervised machine that can make its own decisions anywhere on the farm premises, but it can operate without an existing map within a geofenced boundary. You can also record each decision in case adjustments need to be made by the operator for the best results.
The nature of agriculture (and the natural world more generally) is that reforestation, pruning, soil erosion, and many other changes can make static maps less and less accurate over time. A platform that can be quickly redeployed after a change in the environment is likely to be more useful than one that is only as accurate as the last survey data.
The companies say each flight feeds data into DroneDash’s AI smart farming backend, providing metrics on canopy density analysis, stress and anomalies, plant health scores, spray effectiveness checks, and terrain profiles. Each drone therefore has a dual purpose: as a spray applicator and, in effect, as an aerial sensor platform. The data collected could be used by farmers on an ongoing basis, perhaps to change dosages, modify treatment timing, inform the need for fertilization or pest control, inform replanting schedules, and more.
GEODASH is initially targeting its technology to palm oil plantations in Southeast Asia, row crop operators in the United States, and large plantations in South America. The companies said they carried out a pilot implementation and validation project from 2025 to early 2026. Commercial implementation by GEODASH Aerosystems is planned for the third quarter of 2026.
“Agriculture doesn’t need bigger drones; it needs smarter drones,” said Paul Yam, CEO of DroneDash Technologies and GEODASH Aerosystems.
(Image source: “New Technologies in Agricultural Drones” by Shreesha Sharma is licensed under CC BY-SA 4.0. To view a copy of this license, please visit https://creativecommons.org/licenses/by-sa/4.0)
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