Robot dogs are showing up on farms and in greenhouses in 2025, where they’re being used to monitor high-value crops, gather field data, and send real-time alerts. Farmers have had other tools for this job for years, satellites, drones, and plain old walking the rows, but each one comes with gaps. Satellites miss fine detail. Drones get grounded by weather and only stay up so long. Flying them inside greenhouses can be more trouble than it’s worth. And hand checks take time. A robodog can move through uneven ground and squeeze between tight rows, giving growers a crop-level view with less chance of damaging plants than heavier equipment.
Most of these systems carry a mix of thermal electro-optical, and high-resolution cameras, along with LiDAR and onboard graphics processors. That setup lets them build 3D maps and run analysis on the spot for weeds, invasive pests, leaf disease, phenotyping, and other precision agriculture tasks.
Syngenta and the University of Nottingham are working on one that uses cameras and spinning LiDAR to spot small, early-stage threats hiding under the canopy, the kind satellites or drones often miss. If you keep an eye on farm automation, this one is worth watching. One market forecast says the sector could top $100 billion within eight years, and North America is expected to make up 38.45% of the market in 2025.
Trial results already being cited are pretty striking: 95% fewer adjustment errors and 90% accuracy in Chilean grape trials. DEEP Robotics, for its part, says manual workloads can drop by 70%. Even so, plenty of growers still aren’t ready to jump in. In a 2025 study, 53% of farmers said they’d wait for reviews, pointing to cost, know-how, connectivity, privacy, bias, and worries about the workforce, even as these systems keep rolling out on farms and in greenhouses as crop scouts.
Author: Manuel Bosque
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"es-ES": "Manuel Bosque se ocupa de la cobertura de software en Softonic: programas de escritorio, herramientas de productividad y utilidades pequeñas que resuelven bien un solo problema. Su criterio es práctico —qué hace realmente un programa, qué pide a cambio y a quién le sirve—, y por eso desconfía de las listas de funciones y prefiere probar. Forma parte del equipo que decide cómo se cuenta el software en la web y trabaja a diario con herramientas de IA aplicadas a la producción editorial.",
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