Robotics paper index

MARTS: A Modular Agricultural Robot for Targeted Spraying of Diseased Tomato Plants in Greenhouses

2026-10-13 · arXiv: 2610.18123

One-line summary

A robotics research paper on MARTS: A Modular Agricultural Robot for Targeted Spraying of Diseased Tomato Plants in Greenhouses.

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充:本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original abstract

Broadcast spraying remains the dominant method of disease control in greenhouse tomato production, yet it treats healthy and diseased tissue alike, exposes workers to hazardous aerosols, and depends on operator skill. This paper presents MARTS (Modular Agricultural Robot for Targeted Spraying), a low-cost robotic platform that closes the loop from plant-level disease detection to leaf-localised spray delivery in commercial greenhouses. MARTS integrates a Clearpath Husky A200 mobile base, a UR5 manipulator, a novel modular spraying end-effector built around a single 12 V micro-diaphragm pump, and a wrist-mounted Intel RealSense D435 RGB-D camera. Disease detection reuses our previously published Tomato Leaf Disease Vision Language Model (TLDVLM; Grounding DINO, SAM-2 and LoRA-tuned BLIP-2, 97.27% accuracy over 10 classes); this work focuses on actuation, control and system integration. A ROS pipeline lifts detections into 3D targets, generates collision-aware nozzle poses at a 120 mm stand-off using fitted leaf normals, and scales pump pulse duration to lesion area. Water-sensitive paper calibration showed a linear pulse-volume relationship (R^2 = 0.994). In MuJoCo simulation, an adaptive base-repositioning strategy achieved 100% coverage of 18 waypoints with a maximum orientation error of 1.1 deg. Deployed at the SILAL commercial greenhouse (Al Ain, UAE) on cherry tomatoes under natural infection, MARTS treated 312 diseased leaves in 12 autonomous trials with 92.4% targeting success, 18.7 mm mean spray-centre error, 6.1% adjacent-leaf overspray, and a 71% reduction in pesticide volume versus broadcast spraying. Mean cycle time was 4.8 s per leaf, and the spraying subsystem costs USD 412, 6-10x lower than comparable systems, showing that accurate leaf-level targeting is achievable on commodity hardware.

5.0Engineering value
7.0Research novelty
4.0Business relevance

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