arXiv:2609.12505v1 Announce Type: new Abstract: Vision-based reinforcement learning holds strong potential for decision-making in controlled environment agriculture (CEA). However, its development is...
arXiv:2512.06174v3 Announce Type: replace Abstract: Generating realistic cast shadows for inserted foreground objects requires reasoning about scene geometry and illumination. However, most learning-...
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.
Accurate yield forecasting at the individual-plant level is critical for precision agriculture and supply-chain planning, yet public datasets capturing both visual growth dynamics and per-plant measurement labels are scarce. In this paper, we introduce a novel, annotated image time-series dataset of 691 sweet pepper plants monitored over two growing seasons, comprising 4837 images with per-plant fruit counts categorized by maturity.
arXiv:2607. 12065v1 Announce Type: cross Abstract: While visual navigation has been extensively studied in agricultural robotics, most existing systems assume daytime conditions.
Plant root phenotyping is fundamental to understanding below-ground structures, optimizing crop management, and improving agricultural sustainability. This paper presents a multimodal robotic AI framework that integrates 3D skeleton extraction with language-guided reasoning for interpretable and data-efficient root analysis.
The paper introduces a benchmark called Shedding Light to evaluate how well generative image models understand and reproduce lighting. The benchmark tests models by asking them to inpaint a simple object, called a light probe, into real photographs and then compares the generated probe to the ground truth to assess lighting direction, colour, and radiance. The authors provide a scalable protocol and open-source code and data for systematic assessment of photometric accuracy in future models.
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.
RoMu4o is a ground robot equipped with a 6‑DOF arm and a vision system that performs real‑time deep‑learning image processing and motion planning for proximal hyperspectral leaf sensing in orchards. The system uses robust perception and manipulation pipelines to identify leaf 3D structure, propose 6‑D poses, and generate collision‑free, constraint‑aware paths for precise leaf grasping and spectroscopy. In lab trials the robot achieved a 95 % success rate for 1‑LPB hyperspectral sampling, while field trials in a pistachio orchard reached 70 % success for autonomous leaf grasping and measurement. whyItMatters":"The system demonstrates a viable robotic solution to automate leaf‑level hyperspectral sensing, addressing labor shortages and enabling precise crop health monitoring in precision agriculture."
arXiv:2603.27519v4 Announce Type: replace Abstract: Image-based plant phenotyping depends on dense structural understanding of crops, yet pixel-level annotation remains expensive across species, orga...
arXiv:2608.30088v1 Announce Type: new Abstract: Accurate detection of tomato growth stages is essential for stage-specific greenhouse management and precision agriculture. In Bhutan, greenhouse culti...
arXiv:2512.05539v3 Announce Type: replace Abstract: The visible parts of a scene are determined by occlusion among overlapping surfaces. Here we consider "dead leaves" models, which replicate this by...