Agrovision Plantcount | ST Studio

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Agrovision Plantcount | ST Studio

pip install onnxruntime

import onnxruntime as ort
import numpy as np

# Cargar modelo
sess = ort.InferenceSession("agrovision-plantcount-v1.0.0.onnx")

# Preparar imagen (RGB, 0-1, letterbox a 312x312)
image = np.random.rand(1, 3, 312, 312).astype(np.float32)

# Inferencia
outputs = sess.run(None, {"images": image})
dets, labels, masks = outputs

# Filtrar por confianza
threshold = 0.5
valid = dets[:, :, 4] > threshold
count = valid.sum()
print(f"Plantas detectadas: {count}")

{
  "task": "segment",
  "boxes": [[x1, y1, x2, y2, conf, cls], ...],
  "masks": [...],
  "areas_px": [area_px_1, area_px_2, ...],
  "area_proj_m2": 12.5,
  "count": 42,
  "conf_threshold": 0.5,
  "classes": {"0": "arbusto"},
  "crop": "blueberry",
  "model_version": "1.0.0",
  "architecture": "rfdetr_seg_nano",
  "license": "Apache-2.0",
  "nms_free": true
}

area_proj_m2 = Σ(mask_pixels) × GSD2

@software{agrovision_plantcount_2026,
  title = {AgroVisión PlantCount: Modelo de Conteo de Plantas},
  author = {Prieto, Alex},
  year = {2026},
  version = {1.0.0},
  license = {Apache-2.0},
  url = {https://huggingface.co/aprieto/agrovision-plantcount}
}

Readme17. Aug.huggingface.co/alexp97/agrovision-plantcount

--- language: - es - en license: apache-2.0 library_name: onnx tags: - instance-segmentation - plant-counting - agriculture - computer-vision - blueberry pipeline_tag: image-segmentation --- # AgroVisión PlantCount v1.0.0 Modelo de segmentación de instancias para conteo de plantas de arándano (blueberry) en imágenes aéreas de alta resolución. ## Resumen | Campo | Valor | |-------|-------| | **Arquitectura** | RF-DETR-Seg Nano | | **Tarea** | Segmentación de instancias | | **Cultivo** | Arándano (blueberry) | | **Clases** | 1 (Nevado/arbusto) | | **Licencia** | Apache 2.0 | | **Versión** | 1.0.0 | ## Métricas | Métrica | Box | Mask | |---------|-----|------| | mAP 50 | 0.892 | 0.889 | | mAP 50:95 | 0.778 | 0.773 | | Precision | 0.918 | 0.914 | | Recall | 0.808 | 0.797 | | F1 | 0.86 | 0.86 | ## Uso ### Requisitos ```bash pip install onnxruntime ``` ### Inferencia ```python import onnxruntime as ort import numpy as np # Cargar modelo sess = ort.InferenceSession("agrovision-plantcount-v1.0.0.onnx") # Preparar imagen (RGB, 0-1, letterbox a 312x312) image = np.random.rand(1, 3, 312, 312).astype(np.float32) # Inferencia outputs = sess.run(None, {"images": image}) dets, labels, masks = outputs # Filtrar por confianza threshold = 0.5 valid = dets[:, :, 4] > threshold count = valid.sum() print(f"Plantas detectadas: {count}") ``` ### Contrato de Inferencia (§6.3) La salida del modelo sigue el contrato de AgroVisión: ```json { "task": "segment", "boxes": [[x1, y1, x2, y2, conf, cls], ...], "masks": [...], "areas_px": [area_px_1, area_px_2, ...], "area_proj_m2": 12.5, "count": 42, "conf_threshold": 0.5, "classes": {"0": "arbusto"}, "crop": "blueberry", "model_version": "1.0.0", "architecture": "rfdetr_seg_nano", "license": "Apache-2.0", "nms_free": true } ``` ## Preprocesamiento - **Formato**: RGB - **Normalización**: 0-1 (float32) - **Tamaño**: 312x312 (letterbox) - **GSD esperado**: 0.02 m/px ## Dataset - **Fuente**: Roboflow Universe (`ws-l3s1y/vivero-plant-counting` v3) - **Imágenes**: 1728 (113 base + augmentations) - **Anotaciones**: 5485 (Instance Segmentation) - **Licencia**: CC BY 4.0 - **Split**: train (1422) / valid (207) / test (99) ## Entrenamiento | Hiperparámetro | Valor | |----------------|-------| | Epochs | 100 | | Batch size | 16 | | Learning rate | 1e-4 | | Seed | 42 | | GPU | RTX 4070 Ti SUPER (16GB) | ## Sele...