arXiv:2609.21059v1 Announce Type: cross Abstract: Plant growth and agricultural production form the foundation of a country's sustainable development and directly impact human livelihoods. Recent adv...
arXiv:2608. 06404v1 Announce Type: cross Abstract: Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response.
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 th...
The paper introduces MCLC‑NET, a multimodal continual learning framework for leaf counting that sequentially learns tasks using a memory buffer to retain key samples. It also presents MMLC, a new real‑world dataset containing RGB, depth, and thermal images across different crops and environmental conditions, organized in crop‑wise, time‑wise, and mixed orderings. Experiments show that MCLC‑NET outperforms existing methods on all three task orderings, achieving the lowest average mean squared errors.
arXiv:2607. 11959v1 Announce Type: new Abstract: Greenhouse reinforcement learning can test climate-control ideas at a speed and scale that is difficult to achieve with crop experiments alone.
A unified framework was created to simulate lettuce growth by modeling each leaf’s physiology and structure within a greenhouse environment. The model integrates leaf-level photosynthesis, carbon allocation, and 3‑D plant growth in NVIDIA Isaac Sim, allowing radiation interception to influence growth and vice versa. Validation against greenhouse data shows low prediction errors and demonstrates how variations in light, CO₂, and plant position affect dry weight, leaf number, and tipburn incidence.
arXiv:2608.13856v2 Announce Type: replace-cross Abstract: Timely urban-canopy information is essential for linking remote sensing with heat, mobility, and neighborhood planning. We developed an optic...
arXiv:2609.24906v1 Announce Type: cross Abstract: Dormant tree pruning is labor-intensive yet essential for maintaining modern high-productivity fruit orchards. In this work, we focus on pruning of m...
arXiv:2609.12350v1 Announce Type: new Abstract: Close-range first-person field images are important for mobile maize phenotyping because many plant-level traits depend on in-canopy structures that ar...
The paper presents a benchmark to test whether vision‑language models can produce plant simulation configurations from images using in‑context learning. It focuses on cowpea plot reconstruction, requiring the models to output structured JSON that includes field and plant details. Open‑source multimodal models from the Gemma 4 and Qwen3.5 families are evaluated on synthetic and real drone datasets, using five in‑context methods, and the results show that while VLMs can generate valid JSON and estimate key agronomic metrics, their performance varies and often lags behind dataset baselines.
The paper compares Ultralytics YOLO27, YOLO26, YOLO11, and YOLOv8 for detecting and segmenting small fruit parts in orchard settings. It evaluates five model scales across 30 experiments, finding that YOLO11s-960 and YOLO26s-960 achieve the best mask and box mAP scores while maintaining efficient parameter counts. The study also highlights the difficulty of peduncle detection and provides publicly available code and models for reproducibility.
arXiv:2606. 31941v1 Announce Type: cross Abstract: Unstructured navigational features, such as irregular planting or discontinuities, remain the primary failure mode for under-canopy agricultural robots.