greenhouse operators

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.

Abstract: Greenhouse climate management aims to improve crop production while limiting energy use. This requires knowing how a crop will respond before conditions are changed. A crop digital twin can support this decision only if it represents how plant physiology and structure develop together. A unified framework was developed to simulate lettuce growth from the physiology of individual leaves. Each leaf received the conditions at its position in the canopy and contributed carbon through photosynthesis. Part of this carbon was used for maintenance and the remainder supported growth, distributed among leaves by their age, size and local environment. The predicted leaf mass, area and age generated an evolving three-dimensional plant in NVIDIA Isaac Sim. Ray tracing calculated the radiation intercepted by each leaf and returned it to photosynthesis, so structure and growth influenced each other over time. Against greenhouse measurements, the relative root mean square error was 9.5% for total dry weight and 9.2%, 12.7% and 13.1% for leaf number, canopy diameter and largest-leaf area, respectively. A 30% decrease in incident radiation reduced final dry weight by 10.4%, while the same increase raised it by 6.9%, and adding 200 ppm carbon dioxide raised it by 46.1%. Within a simulated 40-plant block, interior plants accumulated 8.6% less dry weight than border plants with identical initial states, and the leaf-specific tipburn index rose in the enclosed leaves over the period in which tipburn appeared on the greenhouse plants. Resolving individual leaves therefore explains how local exposure changes plant growth within the greenhouse. The framework provides the forward plant model needed for a bidirectional digital twin, where observations of the physical plant can update predictions and support greenhouse climate decisions.

Abstract: Accurate plant growth estimation is essential for greenhouse monitoring, yet obtaining labeled data remains costly and time-consuming. To address this, we propose a few-shot regression framework that combines Vision Transformer (ViT) feature embeddings, clustering-based task construction, and gradient-based meta-learning, and show that task construction in embedding space is a primary driver of performance. The approach leverages an unlabeled image pool to organize data into structured tasks using fuzzy c-means clustering, enabling efficient learning from a small number of labeled samples. We systematically evaluate meta-learning methods and show that second-order methods (e.g., Model-Agnostic Meta-Learning variants such as MAML++) outperform classical baselines in the few-shot regime. Furthermore, intra-cluster support selection has a limited and dataset-dependent impact. Experiments on two plant datasets show that structured task design combined with meta-learning enables reliable plant growth estimation under severe label scarcity.