Earl Ranario

Earl Ranario, Mason J. Earles

Vision-language models (VLMs) are increasingly proposed as general-purpose solutions for visual recognition tasks, yet their reliability for agricultural decision support remains poorly understood. We benchmark a diverse set of open-source and closed-source VLMs on 27 agricultural classification datasets from the AgML collection, spanning 162 classes across plant disease, pest and damage, and plant and weed species identification. Across all tasks, zero-shot VLMs substantially underperform a supervised task-specific baseline (YOLO11), which consistently achieves markedly higher accuracy than any foundation model. Under multiple-choice prompting, the best-performing VLM (Gemini-3 Pro) reaches approximately 62% average accuracy, while open-ended prompting yields much lower performance, with raw accuracies typically below 25%. Applying LLM-based semantic judging increases open-ended accuracy (for example, from 21% to 30% for top models) and alters model rankings, demonstrating that evaluation methodology meaningfully affects reported conclusions. Among open-source models, Qwen-VL-72B performs best, approaching closed-source performance under constrained prompting but still trailing top proprietary systems. Task-level analysis shows that plant and weed species classification is consistently easier than pest and damage identification, which remains the most challenging category across models. Overall, these results indicate that current off-the-shelf VLMs are not yet suitable as standalone agricultural diagnostic systems, but can function as assistive components when paired with constrained interfaces, explicit label ontologies, and domain-aware evaluation strategies.

Lars Lundqvist, Earl Ranario, Hamid Kamangir et al.

Vision foundation models (VFMs) offer the promise of zero-shot object detection without task-specific training data, yet their performance in complex agricultural scenes remains highly sensitive to text prompt construction. We present a systematic prompt optimization framework evaluating four open-vocabulary detectors -- YOLO World, SAM3, Grounding DINO, and OWLv2 -- for cowpea flower and pod detection across synthetic and real field imagery. We decompose prompts into eight axes and conduct one-factor-at-a-time analysis followed by combinatorial optimization, revealing that models respond divergently to prompt structure: conditions that optimize one architecture can collapse another. Applying model-specific combinatorial prompts yields substantial gains over a naive species-name baseline, including +0.357 mAP@0.5 for YOLO World and +0.362 mAP@0.5 for OWLv2 on synthetic cowpea flower data. To evaluate cross-task generalization, we use an LLM to translate the discovered axis structure to a morphologically distinct target -- cowpea pods -- and compare against prompting using the discovered optimal structures from synthetic flower data. Crucially, prompt structures optimized exclusively on synthetic data transfer effectively to real-world fields: synthetic-pipeline prompts match or exceed those discovered on labeled real data for the majority of model-object combinations (flower: 0.374 vs. 0.353 for YOLO World; pod: 0.429 vs. 0.371 for SAM3). Our findings demonstrate that prompt engineering can substantially close the gap between zero-shot VFMs and supervised detectors without requiring manual annotation, and that optimal prompts are model-specific, non-obvious, and transferable across domains.

Heesup Yun, Isaac Kazuo Uyehara, Ioannis Droutsas et al.

Three-dimensional (3D) procedural plant architecture models have emerged as an important tool for simulation-based studies of plant structure and function, extracting plant architectural parameters from field measurements, and for generating realistic plants in computer graphics. However, measuring the architectural parameters and nested structures for these models at the field scales remains prohibitively labor-intensive. We present a novel algorithm that generates a 3D plant architecture from an image, creating a functional structural plant model that reflects organ-level geometric and topological parameters and provides a more comprehensive representation of the plant's architecture. Instead of using 3D sensors or processing multi-view images with computer vision to obtain the 3D structure of plants, we proposed a method that generates token sequences that encode a procedural definition of plant architecture. This work used only synthetic images for training and testing, with exact architectural parameters known, allowing testing of the hypothesis that organ-level architectural parameters could be extracted from image data using a vision-language model (VLM). A synthetic dataset of cowpea plant images was generated using the Helios 3D plant simulator, with the detailed plant architecture encoded in XML files. We developed a plant architecture tokenizer for the XML file defining plant architecture, converting it into a token sequence that a language model can predict. The model achieved a token F1 score of 0.73 during teacher-forced training. Evaluation of the model was performed through autoregressive generation, achieving a BLEU-4 score of 94.00% and a ROUGE-L score of 0.5182. This led to the conclusion that such plant architecture model generation and parameter extraction were possible from synthetic images; thus, future work will extend the approach to real imagery data.

Isaac Kazuo Uyehara, Heesup Yun, Earl Ranario et al.

Agricultural imaging often requires individual images to be stitched together into a final mosaic for analysis. However, agricultural images can be particularly challenging to stitch because feature matching across images is difficult due to repeated textures, plants are non-planar, and mosaics built from many images can accumulate errors that cause drift. Although these issues can be mitigated by using georeferenced images or taking images at high altitude, there is no general solution for images taken close to the crop. To address this, we created a user-friendly and open source pipeline for stitching ground-based images of a linear row of crops that does not rely on additional data. First, we use SuperPoint and LightGlue to extract and match features within small batches of images. Then we stitch the images in each batch in series while imposing constraints on the camera movement. After straightening and rescaling each batch mosaic, all batch mosaics are stitched together in series and then straightened into a final mosaic. We tested the pipeline on images collected along 72 m long rows of crops using two different agricultural robots and a camera manually carried over the row. In all three cases, the pipeline produced high-quality mosaics that could be used to georeference real world positions with a mean absolute error of 20 cm. This approach provides accessible leaf-scale stitching to users who need to coarsely georeference positions within a row, but do not have access to accurate positional data or sophisticated imaging systems.

Earl Ranario, Lars Lundqvist, Heesup Yun et al.

Semantically consistent cross-domain image translation facilitates the generation of training data by transferring labels across different domains, making it particularly useful for plant trait identification in agriculture. However, existing generative models struggle to maintain object-level accuracy when translating images between domains, especially when domain gaps are significant. In this work, we introduce AGILE (Attention-Guided Image and Label Translation for Efficient Cross-Domain Plant Trait Identification), a diffusion-based framework that leverages optimized text embeddings and attention guidance to semantically constrain image translation. AGILE utilizes pretrained diffusion models and publicly available agricultural datasets to improve the fidelity of translated images while preserving critical object semantics. Our approach optimizes text embeddings to strengthen the correspondence between source and target images and guides attention maps during the denoising process to control object placement. We evaluate AGILE on cross-domain plant datasets and demonstrate its effectiveness in generating semantically accurate translated images. Quantitative experiments show that AGILE enhances object detection performance in the target domain while maintaining realism and consistency. Compared to prior image translation methods, AGILE achieves superior semantic alignment, particularly in challenging cases where objects vary significantly or domain gaps are substantial.

Earl Ranario, Ismael Mayanja, Heesup Yun et al.

Accurate plant segmentation in thermal imagery remains a significant challenge for high throughput field phenotyping, particularly in outdoor environments where low contrast between plants and weeds and frequent occlusions hinder performance. To address this, we present a framework that leverages synthetic RGB imagery, a limited set of real annotations, and GAN-based cross-modality alignment to enhance semantic segmentation in thermal images. We trained models on 1,128 synthetic images containing complex mixtures of crop and weed plants in order to generate image segmentation masks for crop and weed plants. We additionally evaluated the benefit of integrating as few as five real, manually segmented field images within the training process using various sampling strategies. When combining all the synthetic images with a few labeled real images, we observed a maximum relative improvement of 22% for the weed class and 17% for the plant class compared to the full real-data baseline. Cross-modal alignment was enabled by translating RGB to thermal using CycleGAN-turbo, allowing robust template matching without calibration. Results demonstrated that combining synthetic data with limited manual annotations and cross-domain translation via generative models can significantly boost segmentation performance in complex field environments for multi-model imagery.