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

Rajmeet Singh; Manveen Kaur; Yusuf Adamu; Lochan Kshetrimayum; Irfan Hussain

cs.ROOct 13, 2026

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

Authors: Rajmeet Singh, Manveen Kaur, Yusuf Adamu, Lochan Kshetrimayum, Irfan Hussain

Organizations: Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi, UAE · Khalifa University Center for Autonomous Robotic Systems (KUCARS) Khalifa University, Abu Dhabi, UAE · Department of Mechanical Engineering University of Windsor ON, Canada · Kobe University Japan

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.

Figures & tables

Figure 1: Representative conventional bulk-spraying practices in greenhouse production. (a) Hose reel trolley sprayer with 200 L tank for manual broadcast application along the row. (b) Self-propelled compact sprayer for ornamental and vegetable greenhouses. (c) Operator pushed cart sprayer with vertical nozzle boom for uniform canopy treatment. (d) Pesticide applicator in full PPE chemical resistant coverall, respirator, face shield, and gloves.

Figure 2: System architecture of the MARTS platform. The four subsystems: Clearpath Husky A200 mobile base, Universal Robots UR5 manipulator, modular spraying end-effector, and on-board perception stack with the Intel RealSense D455 RGB-D camera communicate through a ROS graph running on a single on-board PC. The dashed green target indicates the diseased tomato leaf: the perception stack observes it through the VLM disease detector, and the end-effector delivers the leaf-localised spray pulse, closing the detection-to-spray loop.

Figure 3: Selective spraying end-effector designed for integration with the UR5 manipulator: (a) isometric CAD rendering showing the key components, including the Intel RealSense RGB-D camera mounted on top for target localization, the spraying nozzle at the front, the UR5 tool-flange mount, and the hosepipe connector at the base; (b) sectional cutaway view illustrating the internal routing of the brass threaded fitting that channels the spray fluid from the hosepipe connector to the nozzle outlet; (c) 3D-printed prototype mounted on the UR5 end-effector during field deployment in the greenhouse.

Parameter	Value
Pump type	12 V DC diaphragm
Free-flow rate	4.0 L/min
Max pressure	0.55 MPa
Reservoir capacity	10.0 L (food-grade)
Tubing inner diameter	4.0 mm (polyurethane)
Nozzle type	Hollow-cone (default)

Table 1: Modular spraying end-effector specifications.

Figure 4: MARS integrated hardware-software architecture for autonomous disease detection and selective impulse spraying. The system orchestrates perception (GroundingDINO, SAM-2, BLIP-2 LoRA), spray decision logic, and mobile manipulation via ROS 2.

Figure 5: System architecture of the precision spraying platform: the on-board ROS-based PC communicates via impulse mechanism with an Arduino-controlled pump driver (5 V input) to command the micro-diaphragm pump (12 V supply). The pump draws liquid from the spray tank and delivers pressurized fluid to the proposed end-effector, which atomizes the pesticide formulation for selective spot-spraying application.

Figure 6: MuJoCo-based simulation framework for validating trajectory planning and mobile-base positioning strategies prior to hardware deployment.

Figure 7: Predefined waypoint layout and trajectory design for plant scanning, spanning the full plant height with 18 discrete inspection poses.

Metric	Case I	Case II	Case III	Unit
Average PTE	$3.2\pm 1.1$	$6.8\pm 2.4$	$2.1\pm 0.8$	mm
Average OTE	$0.34\pm 0.18$	$0.78\pm 0.42$	$0.41\pm 0.19$	deg
Max Cumulative OTE	2.8	8.4	1.1	deg
CJM	142.3	189.7	156.2	rad
Cycle Time	156	142	168	s
Workspace Coverage	94.4	100.0	100.0	%

Table 2: Comparative simulation results for three mobile base positioning strategies.

Figure 8: MARTS experimental setup in a real greenhouse environment. The annotated frame shows the complete system deployed in a tomato crop aisle, with key components labelled: the Husky A200 mobile platform, UR5 robot manipulator, front module (RGB-D camera and spray nozzle assembly), on-board PC, and surrounding tomato crop rows. The greenhouse aisle width ( $\approx 1.2$ m) constrains the mobile base footprint and manipulator reach envelope.

Figure 9: MARTS real-greenhouse experiment workflow illustrating the four-stage trial protocol: (1) greenhouse row preparation and environmental assessment; (2) MARTS deployment and aisle alignment; (3) autonomous plant interaction with front module operating near leaves and tomato clusters; (4) post-trial field evaluation using video evidence and treatment logs.

Figure 10: Time sequence of MARTS experimentation in the greenhouse row. Six representative frames captured at (a) $t=0$ s: initial deployment with the platform at the row entrance; (b) $t=20$ s: front module approaches the crop canopy; (c) $t=40$ s: row traversal with coordinated mobile manipulation; (d) $t=65$ s: close-range positioning at the tomato canopy ( $\approx 0.15$ m working distance); (e) $t=100$ s: mid-trial repositioning in the aisle; (f) $t=130$ s: in-canopy interaction with dense foliage and fruit clusters.

Figure 11: Close-range robot plant interaction during MARTS field testing: (a) full view showing the front module positioned within the tomato canopy between upper foliage and lower fruit clusters; (b) zoomed view of the module canopy interface, with the front module housing and RealSense D435 camera operating at 0.20–0.25 m working distance from the leaf surface; (c) zoomed view of a green tomato fruit cluster with dense leaf occlusion, illustrating the visual complexity encountered during real world disease scanning; (d) TLDVLM based segmentation overlay of diseased leaf.

Row	Detected Leaves	Success (%)	Mean Error (mm)	Overspray (%)
Row 1	104	93.3	18.1	5.8
Row 2	108	91.7	19.4	6.5
Row 3	100	92.0	18.6	6.0
Pooled	312	92.4	18.7	6.1

Table 3: Targeting performance of the MARTS platform across three greenhouse rows at the SILAL commercial facility, Abu Dhabi, UAE. Values are reported per row and pooled across all 312 detected diseased leaves.

Item	Cost (USD)
12 V DC diaphragm pump	78
Hollow-cone nozzle set (5 tips)	64
2 L food-grade reservoir + cap	22
Polyurethane tubing, fittings, relief valve	41
MOSFET interface board	56
Printed tool-flange adapter	9

Table 4: Bill of materials for the modular spraying end-effector at single-unit retail pricing.

System	Crop	Targeting (%)	Vol. Red. (%)	Cost (USD)	Modular?
Modular robot + multispectral [ Oberti et al., 2016 ]	Grapevine	85–100	65–85	Not reported	Partial
Robotised patch sprayer [ Gonzalez-de-Soto et al., 2016 ]	Row crops	99.5 ∗	56	$>$ 3 000	No
Smart tree-crop sprayer [ Partel et al., 2021 ]	Citrus	84 †	28	$>$ 2 500	No
DL spraying robot + PWM [ Seol et al., 2022 ]	Pear	68.9 ‡	—	$>$ 4 000	No
Smarter robotic sprayer [ Baltazar et al., 2021 ]	Vineyard	—	40–60	$>$ 3 500	No
MARTS (ours)	Tomato	92.4	71	412	Yes

Table 5: Comparison of MARTS with state-of-the-art selective-spraying systems reported in the literature for greenhouse and protected cultivation applications. Targeting refers to the percentage of detected diseased or target regions that received a correctly placed spray pulse. Volume reduction is relative to each study’s own broadcast or conventional baseline. Cost refers to the spraying subsystem hardware only (excluding mobile base and manipulator).

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Symbol	Description	Unit
$b$	Leaf bounding box from VLM	pixels
$A$	Bounding box area	mm 2
$(u_{c},v_{c})$	Bounding box centre in pixels	—
$(f_{x},f_{y})$	Camera focal lengths	pixels
$(c_{x},c_{y})$	Camera principal point	pixels
$z$	Median depth at leaf centroid	m

Appendix

Table 6: Nomenclature: Geometric and control variables.

Abbrev.	Full Form
BOM	Bill of Materials
CAD	Computer-Aided Design
DC	Direct Current
DoF	Degree of Freedom
FOV	Field of View
IMU	Inertial Measurement Unit

Appendix

Table 7: Nomenclature: Abbreviations.

Agricultural Robotics

Agricultural robotics develops autonomous machines that sense, navigate, and perform tasks such as monitoring, weeding, spraying, and harvesting crops.

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Multi-Speed Field Evaluation of a Semi-Autonomous Precision Sprayer for Weed Control in Vegetable Crops

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Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USA · Department of Horticulture, Michigan State University, East Lansing, MI 48824, USA

Precision Agriculture Agricultural Robotics Weed Detection

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Machine vision-guided precision spraying is promising for sustainable vegetable production by enabling site-specific weed targeting, reducing herbicide application while protecting crop yield and quality. However, the effects of operating speed of sprayer systems on the integrated detection-tracking-actuation pipeline remain insufficiently characterized under field conditions. This study evaluated a semi-autonomous, tractor-based spraying system for targeted weed control in radish and quantified performance across three operating speeds. A five-season dataset comprising 10,599 images and 84,311 annotated instances across seven classes was curated for model development. A YOLOv11-medium model was integrated with ByteTrack tracking and a customized 12-nozzle sprayer on a semi-autonomous electric tractor. Field trials were conducted at 0.13, 0.27, and 0.45 m/s (i.e., 0.3, 0.6, and 1.0 mph) to characterize speed-dependent changes in weed detection, targeting, actuation, and crop damage. At the lowest speed of 0.13 m/s, the system achieved 94.6% mAP@50 and 93.2% recall for weed and crop detection. However, increasing platform speed substantially reduced system performance, with weed hit rate declining from 78.3% to 35.0% and crop contact rates increasing from 20.1% to 28.7%. Consequently, the system did not meet predefined commercial-viability thresholds. These results demonstrate that practical deployment requires improvements beyond weed detection alone, particularly coordinated improvements in motion-robust plant perception, spray-timing compensation, and crop-adjacent actuation precision. The identified performance bottlenecks provide specific engineering targets for advancing vision-guided precision spraying toward practical, high-speed vegetable weed management. The software programs developed in this study have been made publicly available to support future precision weeding research.

Abstract

Agricultural Robotics

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