Canopy Photosynthesis Modeling Tutorial Based on 3D Reconstruction
· 16 min read
Introduction
Canopy photosynthesis modeling is a crucial research area in precision agriculture and plant phenomics. This comprehensive tutorial demonstrates how to build a complete canopy photosynthesis simulation system through multi-view image acquisition, 3D reconstruction, canopy construction, and ray tracing techniques. The complete workflow includes: Camera Capture → SfM/3DGS Reconstruction → Mesh Generation → Canopy Construction → Light Distribution Simulation → Photosynthesis Calculation.
Technical Workflow Overview
graph TD
A[Multi-view Image Capture] --> B[SfM 3D Reconstruction]
A --> C[3D Gaussian Splatting]
B --> D[Point Cloud Generation]
C --> D
D --> E[Triangle Mesh Generation]
E --> F[Plant Mesh Model]
F --> G[Plant Replication & Perturbation]
G --> H[3D Canopy Model]
H --> I[Ray Tracing Algorithm]
I --> J[Light Distribution Calculation]
J --> K[Light Response Curve Model]
K --> L[Canopy Photosynthesis Rate]
Step 1: Multi-view Image Acquisition System
Hardware Configuration
# Multi-view capture system configuration
import numpy as np
import cv2
import json
from datetime import datetime
class MultiViewCaptureSystem:
def __init__(self, camera_config):
self.cameras = []
self.calibration_data = {}
self.capture_positions = self.generate_capture_positions()
def generate_capture_positions(self, radius=1.5, height_levels=3, angles_per_level=12):
"""Generate spherical capture positions"""
positions = []
for h_idx in range(height_levels):
# Height from 0.5m to 2.0m
height = 0.5 + (h_idx * 0.75)
for angle_idx in range(angles_per_level):
angle = (angle_idx * 360 / angles_per_level) * np.pi / 180
x = radius * np.cos(angle)
y = radius * np.sin(angle)
z = height
# Camera looks at plant center
look_at = np.array([0, 0, 1.0]) # Plant center height
camera_pos = np.array([x, y, z])
positions.append({
'position': camera_pos,
'look_at': look_at,
'up_vector': np.array([0, 0, 1])
})
return positions
def capture_sequence(self, plant_id, output_dir):
"""Execute multi-view image capture"""
metadata = {
'plant_id': plant_id,
'timestamp': datetime.now().isoformat(),
'positions': [],
'camera_params': self.get_camera_parameters()
}
for idx, pos_config in enumerate(self.capture_positions):
# Move camera to specified position (requires robotic arm or rail system)
self.move_camera_to_position(pos_config)
# Capture image
image_path = f"{output_dir}/image_{idx:03d}.jpg"
image = self.capture_image()
cv2.imwrite(image_path, image)
# Record position information
metadata['positions'].append({
'image_id': idx,
'position': pos_config['position'].tolist(),
'look_at': pos_config['look_at'].tolist(),
'up_vector': pos_config['up_vector'].tolist()
})
# Save metadata
with open(f"{output_dir}/metadata.json", 'w') as f:
json.dump(metadata, f, indent=2)
return metadata
def get_camera_parameters(self):
"""Get camera intrinsic parameters"""
return {
'focal_length': 35.0, # mm
'sensor_width': 36.0, # mm
'sensor_height': 24.0, # mm
'image_width': 4000,
'image_height': 3000,
'distortion_coeffs': [0.1, -0.2, 0.0, 0.0, 0.0]
}
Adaptive Capture Strategy
class AdaptiveCaptureStrategy:
def __init__(self):
self.quality_threshold = 0.8
self.overlap_ratio = 0.6
def optimize_capture_positions(self, plant_bbox, complexity_map):
"""Optimize capture positions based on plant complexity"""
base_positions = self.generate_base_positions(plant_bbox)
# Increase capture density based on plant complexity
enhanced_positions = []
for pos in base_positions:
enhanced_positions.append(pos)
# Add extra views in complex regions
complexity = self.estimate_local_complexity(pos, complexity_map)
if complexity > 0.7:
additional_views = self.generate_additional_views(pos, num_views=3)
enhanced_positions.extend(additional_views)
return enhanced_positions
def estimate_image_quality(self, image):
"""Evaluate image quality"""
# Calculate image sharpness
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
laplacian_var = cv2.Laplacian(gray, cv2.CV_64F).var()
# Calculate exposure quality
hist = cv2.calcHist([gray], [0], None, [256], [0, 256])
exposure_quality = 1.0 - np.sum(hist[[0, 255]]) / image.size
# Overall quality score
quality_score = (laplacian_var / 1000.0) * exposure_quality
return min(quality_score, 1.0)
Step 2: Structure from Motion (SfM) Reconstruction
COLMAP Integration
import subprocess
import os
from pathlib import Path
class SfMReconstruction:
def __init__(self, colmap_path="/usr/local/bin/colmap"):
self.colmap_path = colmap_path
def run_sfm_pipeline(self, image_dir, output_dir, camera_model="PINHOLE"):
"""Execute complete SfM reconstruction pipeline"""
# Create output directory
Path(output_dir).mkdir(parents=True, exist_ok=True)
database_path = os.path.join(output_dir, "database.db")
# 1. Feature extraction
self.extract_features(image_dir, database_path, camera_model)
# 2. Feature matching
self.match_features(database_path)
# 3. Sparse reconstruction
sparse_dir = os.path.join(output_dir, "sparse")
self.sparse_reconstruction(database_path, image_dir, sparse_dir)
# 4. Dense reconstruction
dense_dir = os.path.join(output_dir, "dense")
self.dense_reconstruction(image_dir, sparse_dir, dense_dir)
return dense_dir
def extract_features(self, image_dir, database_path, camera_model):
"""Feature extraction"""
cmd = [
self.colmap_path, "feature_extractor",
"--database_path", database_path,
"--image_path", image_dir,
"--ImageReader.camera_model", camera_model,
"--SiftExtraction.use_gpu", "1",
"--SiftExtraction.max_num_features", "8192"
]
subprocess.run(cmd, check=True)
def match_features(self, database_path):
"""Feature matching"""
cmd = [
self.colmap_path, "exhaustive_matcher",
"--database_path", database_path,
"--SiftMatching.use_gpu", "1"
]
subprocess.run(cmd, check=True)
def sparse_reconstruction(self, database_path, image_dir, output_dir):
"""Sparse reconstruction"""
Path(output_dir).mkdir(parents=True, exist_ok=True)
cmd = [
self.colmap_path, "mapper",
"--database_path", database_path,
"--image_path", image_dir,
"--output_path", output_dir
]
subprocess.run(cmd, check=True)
def dense_reconstruction(self, image_dir, sparse_dir, dense_dir):
"""Dense reconstruction"""
Path(dense_dir).mkdir(parents=True, exist_ok=True)
# Image undistortion
cmd_undistort = [
self.colmap_path, "image_undistorter",
"--image_path", image_dir,
"--input_path", os.path.join(sparse_dir, "0"),
"--output_path", dense_dir,
"--output_type", "COLMAP"
]
subprocess.run(cmd_undistort, check=True)
# Stereo matching
cmd_stereo = [
self.colmap_path, "patch_match_stereo",
"--workspace_path", dense_dir,
"--workspace_format", "COLMAP",
"--PatchMatchStereo.geom_consistency", "1"
]
subprocess.run(cmd_stereo, check=True)
# Stereo fusion
cmd_fusion = [
self.colmap_path, "stereo_fusion",
"--workspace_path", dense_dir,
"--workspace_format", "COLMAP",
"--input_type", "geometric",
"--output_path", os.path.join(dense_dir, "fused.ply")
]
subprocess.run(cmd_fusion, check=True)
Step 3: 3D Gaussian Splatting Reconstruction
import torch
import torch.nn as nn
import numpy as np
from scipy.spatial.transform import Rotation
class GaussianSplattingReconstruction:
def __init__(self, device="cuda"):
self.device = device
self.gaussians = None
def initialize_gaussians_from_sfm(self, point_cloud_path, num_gaussians=100000):
"""Initialize Gaussians from SfM point cloud"""
# Load SfM point cloud
points, colors = self.load_point_cloud(point_cloud_path)
# Initialize Gaussian parameters
positions = torch.tensor(points, dtype=torch.float32, device=self.device)
colors = torch.tensor(colors, dtype=torch.float32, device=self.device)
# Initialize scales and rotations
scales = torch.ones((len(points), 3), device=self.device) * 0.01
rotations = torch.zeros((len(points), 4), device=self.device)
rotations[:, 0] = 1.0 # Unit quaternion
# Initialize opacities
opacities = torch.ones((len(points), 1), device=self.device) * 0.5
self.gaussians = {
'positions': nn.Parameter(positions),
'colors': nn.Parameter(colors),
'scales': nn.Parameter(scales),
'rotations': nn.Parameter(rotations),
'opacities': nn.Parameter(opacities)
}
return self.gaussians
def train_gaussians(self, images, camera_poses, num_iterations=30000):
"""Train 3D Gaussians"""
optimizer = torch.optim.Adam([
{'params': [self.gaussians['positions']], 'lr': 0.00016},
{'params': [self.gaussians['colors']], 'lr': 0.0025},
{'params': [self.gaussians['scales']], 'lr': 0.005},
{'params': [self.gaussians['rotations']], 'lr': 0.001},
{'params': [self.gaussians['opacities']], 'lr': 0.05}
])
for iteration in range(num_iterations):
# Randomly select training view
cam_idx = np.random.randint(0, len(images))
target_image = images[cam_idx]
camera_pose = camera_poses[cam_idx]
# Render image
rendered_image = self.render_gaussian_splatting(camera_pose)
# Compute loss
loss = self.compute_loss(rendered_image, target_image)
# Backpropagation
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Adaptive density control
if iteration % 100 == 0:
self.adaptive_density_control()
if iteration % 1000 == 0:
print(f"Iteration {iteration}, Loss: {loss.item():.6f}")
def extract_mesh_from_gaussians(self, resolution=512):
"""Extract mesh from Gaussians"""
# Use Marching Cubes algorithm
from skimage import measure
# Create voxel grid
x = np.linspace(-2, 2, resolution)
y = np.linspace(-2, 2, resolution)
z = np.linspace(-2, 2, resolution)
X, Y, Z = np.meshgrid(x, y, z)
# Calculate density for each voxel
density = self.evaluate_gaussian_density(X, Y, Z)
# Extract isosurface
vertices, faces, _, _ = measure.marching_cubes(density, level=0.1)
return vertices, faces
Step 4: Mesh Processing and Plant Model Construction
import trimesh
import open3d as o3d
from scipy.spatial import ConvexHull
class PlantMeshProcessor:
def __init__(self):
self.mesh = None
self.leaf_segments = []
self.stem_segments = []
def process_point_cloud_to_mesh(self, point_cloud_path):
"""Convert point cloud to mesh"""
# Load point cloud
pcd = o3d.io.read_point_cloud(point_cloud_path)
# Point cloud preprocessing
pcd = self.preprocess_point_cloud(pcd)
# Poisson reconstruction
mesh, _ = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(
pcd, depth=9, width=0, scale=1.1, linear_fit=False
)
# Mesh post-processing
mesh = self.postprocess_mesh(mesh)
self.mesh = mesh
return mesh
def preprocess_point_cloud(self, pcd):
"""Point cloud preprocessing"""
# Remove outliers
pcd, _ = pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=2.0)
# Estimate normals
pcd.estimate_normals(
search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.1, max_nn=30)
)
# Orient normals consistently
pcd.orient_normals_consistent_tangent_plane(100)
return pcd
def postprocess_mesh(self, mesh):
"""Mesh post-processing"""
# Remove duplicate vertices
mesh.remove_duplicated_vertices()
# Remove duplicate triangles
mesh.remove_duplicated_triangles()
# Remove degenerate triangles
mesh.remove_degenerate_triangles()
# Remove non-manifold edges
mesh.remove_non_manifold_edges()
# Smooth mesh
mesh = mesh.filter_smooth_simple(number_of_iterations=5)
return mesh
def segment_plant_organs(self, mesh):
"""Plant organ segmentation"""
vertices = np.asarray(mesh.vertices)
faces = np.asarray(mesh.triangles)
# Geometry-based segmentation
leaf_indices, stem_indices = self.geometric_segmentation(vertices, faces)
# Create leaf and stem meshes
leaf_mesh = self.create_submesh(mesh, leaf_indices)
stem_mesh = self.create_submesh(mesh, stem_indices)
self.leaf_segments = self.extract_individual_leaves(leaf_mesh)
self.stem_segments = [stem_mesh]
return self.leaf_segments, self.stem_segments
def geometric_segmentation(self, vertices, faces):
"""Geometry-based segmentation"""
# Calculate vertex geometric features
curvatures = self.compute_curvature(vertices, faces)
normals = self.compute_normals(vertices, faces)
# Classify based on curvature and normals
leaf_threshold = 0.5
leaf_indices = np.where(curvatures > leaf_threshold)[0]
stem_indices = np.where(curvatures <= leaf_threshold)[0]
return leaf_indices, stem_indices
def compute_curvature(self, vertices, faces):
"""Calculate vertex curvature"""
# Simplified curvature calculation
mesh_trimesh = trimesh.Trimesh(vertices=vertices, faces=faces)
curvatures = trimesh.curvature.discrete_gaussian_curvature_measure(
mesh_trimesh, vertices, radius=0.05
)
return np.abs(curvatures)
Step 5: Canopy Construction with Random Perturbation
class CanopyBuilder:
def __init__(self, plant_mesh, plant_segments):
self.base_plant = plant_mesh
self.leaf_segments = plant_segments['leaves']
self.stem_segments = plant_segments['stems']
self.canopy_plants = []
def build_canopy(self, canopy_config):
"""Build canopy structure"""
# Generate plant positions
positions = self.generate_plant_positions(canopy_config)
# Create plant instance for each position
for i, pos in enumerate(positions):
plant_instance = self.create_plant_instance(pos, i)
self.canopy_plants.append(plant_instance)
# Merge all plants
canopy_mesh = self.merge_plants()
return canopy_mesh, self.canopy_plants
def generate_plant_positions(self, config):
"""Generate plant positions"""
row_spacing = config['row_spacing'] # Row spacing
plant_spacing = config['plant_spacing'] # Plant spacing
num_rows = config['num_rows']
plants_per_row = config['plants_per_row']
positions = []
for row in range(num_rows):
for plant in range(plants_per_row):
x = plant * plant_spacing
y = row * row_spacing
z = 0 # Ground level
# Add random perturbation
x += np.random.normal(0, plant_spacing * 0.1)
y += np.random.normal(0, row_spacing * 0.1)
positions.append([x, y, z])
return np.array(positions)
def create_plant_instance(self, position, plant_id):
"""Create individual plant instance"""
# Copy base plant
plant_mesh = self.base_plant.copy()
# Apply random transformation
transform_matrix = self.generate_random_transform(position, plant_id)
plant_mesh.transform(transform_matrix)
# Apply morphological variation
plant_mesh = self.apply_morphological_variation(plant_mesh, plant_id)
return {
'mesh': plant_mesh,
'position': position,
'id': plant_id,
'transform': transform_matrix
}
def generate_random_transform(self, position, plant_id):
"""Generate random transformation matrix"""
# Set random seed for reproducibility
np.random.seed(plant_id)
# Random rotation (mainly around Z-axis)
rotation_z = np.random.uniform(0, 2 * np.pi)
rotation_x = np.random.normal(0, 0.1) # Slight tilt
rotation_y = np.random.normal(0, 0.1)
# Random scaling
scale_factor = np.random.normal(1.0, 0.15)
scale_factor = np.clip(scale_factor, 0.7, 1.3)
# Build transformation matrix
transform = np.eye(4)
# Apply scaling
transform[:3, :3] *= scale_factor
# Apply rotation
from scipy.spatial.transform import Rotation
rotation = Rotation.from_euler('xyz', [rotation_x, rotation_y, rotation_z])
transform[:3, :3] = rotation.as_matrix() @ transform[:3, :3]
# Apply translation
transform[:3, 3] = position
return transform
def apply_morphological_variation(self, mesh, plant_id):
"""Apply morphological variation"""
vertices = np.asarray(mesh.vertices)
# Leaf shape variation
leaf_variation = self.generate_leaf_variation(plant_id)
vertices = self.apply_leaf_deformation(vertices, leaf_variation)
# Stem variation
stem_variation = self.generate_stem_variation(plant_id)
vertices = self.apply_stem_deformation(vertices, stem_variation)
# Update mesh
mesh.vertices = o3d.utility.Vector3dVector(vertices)
mesh.compute_vertex_normals()
return mesh
def generate_leaf_variation(self, plant_id):
"""Generate leaf variation parameters"""
np.random.seed(plant_id + 1000)
return {
'length_factor': np.random.normal(1.0, 0.2),
'width_factor': np.random.normal(1.0, 0.15),
'curvature_factor': np.random.normal(1.0, 0.3),
'angle_variation': np.random.normal(0, 0.2)
}
Step 6: Ray Tracing Algorithm Implementation
import numpy as np
from numba import jit, cuda
import matplotlib.pyplot as plt
class RayTracingEngine:
def __init__(self, canopy_mesh, light_config):
self.canopy_mesh = canopy_mesh
self.light_config = light_config
self.acceleration_structure = None
self.build_acceleration_structure()
def build_acceleration_structure(self):
"""Build acceleration structure (BVH tree)"""
from rtree import index
# Create spatial index
idx = index.Index()
faces = np.asarray(self.canopy_mesh.triangles)
vertices = np.asarray(self.canopy_mesh.vertices)
for i, face in enumerate(faces):
# Calculate triangle bounding box
triangle_vertices = vertices[face]
min_coords = np.min(triangle_vertices, axis=0)
max_coords = np.max(triangle_vertices, axis=0)
# Insert into spatial index
idx.insert(i, (*min_coords, *max_coords))
self.acceleration_structure = idx
self.faces = faces
self.vertices = vertices
def simulate_light_distribution(self, sun_angles, num_rays=1000000):
"""Simulate light distribution"""
results = {}
for time_step, sun_angle in enumerate(sun_angles):
print(f"Computing light distribution for time step {time_step}")
# Generate rays
rays = self.generate_sun_rays(sun_angle, num_rays)
# Ray tracing
intersections = self.trace_rays(rays)
# Calculate light intensity distribution
light_map = self.compute_light_intensity_map(intersections)
results[time_step] = {
'sun_angle': sun_angle,
'light_map': light_map,
'intersections': intersections
}
return results
def generate_sun_rays(self, sun_angle, num_rays):
"""Generate sun rays"""
# Sun direction vector
elevation, azimuth = sun_angle
sun_direction = np.array([
np.cos(elevation) * np.sin(azimuth),
np.cos(elevation) * np.cos(azimuth),
-np.sin(elevation) # Downward
])
# Generate parallel rays
# Create ray origin grid above canopy
canopy_bounds = self.get_canopy_bounds()
# Extend bounds to ensure full canopy coverage
x_min, x_max = canopy_bounds[0] - 1, canopy_bounds[1] + 1
y_min, y_max = canopy_bounds[2] - 1, canopy_bounds[3] + 1
z_start = canopy_bounds[5] + 2 # 2m above canopy top
# Generate random origins
origins = np.random.uniform(
[x_min, y_min, z_start],
[x_max, y_max, z_start],
(num_rays, 3)
)
# All rays have same direction (parallel light)
directions = np.tile(sun_direction, (num_rays, 1))
return {
'origins': origins,
'directions': directions
}
@jit(nopython=True)
def ray_triangle_intersection(self, ray_origin, ray_direction, v0, v1, v2):
"""Ray-triangle intersection test (Möller-Trumbore algorithm)"""
epsilon = 1e-8
edge1 = v1 - v0
edge2 = v2 - v0
h = np.cross(ray_direction, edge2)
a = np.dot(edge1, h)
if abs(a) < epsilon:
return False, 0.0, 0.0, 0.0
f = 1.0 / a
s = ray_origin - v0
u = f * np.dot(s, h)
if u < 0.0 or u > 1.0:
return False, 0.0, 0.0, 0.0
q = np.cross(s, edge1)
v = f * np.dot(ray_direction, q)
if v < 0.0 or u + v > 1.0:
return False, 0.0, 0.0, 0.0
t = f * np.dot(edge2, q)
if t > epsilon:
return True, t, u, v
return False, 0.0, 0.0, 0.0
def trace_rays(self, rays):
"""Ray tracing"""
origins = rays['origins']
directions = rays['directions']
intersections = []
for i in range(len(origins)):
ray_origin = origins[i]
ray_direction = directions[i]
# Use spatial index to accelerate intersection tests
intersection = self.find_closest_intersection(ray_origin, ray_direction)
if intersection is not None:
intersections.append({
'ray_id': i,
'point': intersection['point'],
'normal': intersection['normal'],
'face_id': intersection['face_id'],
'distance': intersection['distance']
})
return intersections
def compute_light_intensity_map(self, intersections):
"""Calculate light intensity distribution map"""
# Create 3D grid
bounds = self.get_canopy_bounds()
resolution = 100
x = np.linspace(bounds[0], bounds[1], resolution)
y = np.linspace(bounds[2], bounds[3], resolution)
z = np.linspace(bounds[4], bounds[5], resolution)
light_intensity = np.zeros((resolution, resolution, resolution))
# Map intersection points to grid
for intersection in intersections:
point = intersection['point']
# Find corresponding grid indices
xi = int((point[0] - bounds[0]) / (bounds[1] - bounds[0]) * (resolution - 1))
yi = int((point[1] - bounds[2]) / (bounds[3] - bounds[2]) * (resolution - 1))
zi = int((point[2] - bounds[4]) / (bounds[5] - bounds[4]) * (resolution - 1))
# Ensure indices are within valid range
xi = np.clip(xi, 0, resolution - 1)
yi = np.clip(yi, 0, resolution - 1)
zi = np.clip(zi, 0, resolution - 1)
# Increase light intensity
light_intensity[xi, yi, zi] += 1
return {
'intensity': light_intensity,
'bounds': bounds,
'resolution': resolution,
'coordinates': (x, y, z)
}
Step 7: Light Response Curve Model
class PhotosynthesisModel:
def __init__(self):
# Farquhar-von Caemmerer-Berry model parameters
self.vcmax25 = 60.0 # Maximum carboxylation rate at 25°C (μmol m⁻² s⁻¹)
self.jmax25 = 120.0 # Maximum electron transport rate at 25°C (μmol m⁻² s⁻¹)
self.rd25 = 1.5 # Dark respiration rate at 25°C (μmol m⁻² s⁻¹)
# Temperature response parameters
self.ha_vcmax = 65330.0 # Vcmax activation energy (J mol⁻¹)
self.ha_jmax = 43540.0 # Jmax activation energy (J mol⁻¹)
self.ha_rd = 46390.0 # Rd activation energy (J mol⁻¹)
# Other parameters
self.kc25 = 404.9 # CO2 Michaelis constant at 25°C (μmol mol⁻¹)
self.ko25 = 278.4 # O2 Michaelis constant at 25°C (mmol mol⁻¹)
self.cp25 = 42.75 # CO2 compensation point at 25°C (μmol mol⁻¹)
self.r_gas = 8.314 # Gas constant (J mol⁻¹ K⁻¹)
def compute_photosynthesis_rate(self, light_intensity, temperature, co2_conc, leaf_area):
"""Calculate photosynthesis rate"""
# Temperature correction
vcmax = self.temperature_correction(self.vcmax25, self.ha_vcmax, temperature)
jmax = self.temperature_correction(self.jmax25, self.ha_jmax, temperature)
rd = self.temperature_correction(self.rd25, self.ha_rd, temperature)
# Temperature correction for Michaelis constants
kc = self.temperature_correction(self.kc25, 79430.0, temperature)
ko = self.temperature_correction(self.ko25, 36380.0, temperature)
cp = self.temperature_correction(self.cp25, 37830.0, temperature)
# Calculate electron transport rate
j = self.compute_electron_transport_rate(light_intensity, jmax)
# Calculate RuBisCO-limited photosynthesis rate
wc = (vcmax * (co2_conc - cp)) / (co2_conc + kc * (1 + 210 / ko))
# Calculate RuBP regeneration-limited photosynthesis rate
wj = (j * (co2_conc - cp)) / (4 * (co2_conc + 2 * cp))
# Take minimum (limiting factor)
gross_photosynthesis = min(wc, wj)
# Net photosynthesis rate
net_photosynthesis = gross_photosynthesis - rd
# Multiply by leaf area to get total photosynthesis rate
total_rate = net_photosynthesis * leaf_area
return {
'net_rate': net_photosynthesis,
'total_rate': total_rate,
'gross_rate': gross_photosynthesis,
'respiration': rd,
'electron_transport': j,
'rubisco_limited': wc,
'rubp_limited': wj
}
def temperature_correction(self, rate25, activation_energy, temperature):
"""Temperature correction function"""
temp_k = temperature + 273.15
temp25_k = 25.0 + 273.15
return rate25 * np.exp(activation_energy * (temp_k - temp25_k) / (self.r_gas * temp_k * temp25_k))
def compute_electron_transport_rate(self, light_intensity, jmax):
"""Calculate electron transport rate"""
# Light response curve parameters
alpha = 0.24 # Quantum efficiency
theta = 0.7 # Curvature factor
# Non-rectangular hyperbola model
a = theta
b = -(alpha * light_intensity + jmax)
c = alpha * light_intensity * jmax
# Solve quadratic equation
discriminant = b**2 - 4*a*c
if discriminant >= 0:
j = (-b - np.sqrt(discriminant)) / (2*a)
else:
j = 0
return max(0, j)
def compute_canopy_photosynthesis(self, light_distribution, leaf_area_distribution,
temperature_map, co2_concentration=400):
"""Calculate canopy photosynthesis rate"""
total_photosynthesis = 0
detailed_results = []
# Get light distribution data
light_intensity = light_distribution['intensity']
coordinates = light_distribution['coordinates']
# Iterate through each voxel
for i in range(light_intensity.shape[0]):
for j in range(light_intensity.shape[1]):
for k in range(light_intensity.shape[2]):
if light_intensity[i, j, k] > 0:
# Get parameters for this voxel
x, y, z = coordinates[0][i], coordinates[1][j], coordinates[2][k]
light = light_intensity[i, j, k]
leaf_area = leaf_area_distribution.get((i, j, k), 0)
temperature = temperature_map.get((i, j, k), 25.0)
if leaf_area > 0:
# Calculate photosynthesis rate for this voxel
result = self.compute_photosynthesis_rate(
light, temperature, co2_concentration, leaf_area
)
total_photosynthesis += result['total_rate']
detailed_results.append({
'position': (x, y, z),
'voxel_index': (i, j, k),
'light_intensity': light,
'leaf_area': leaf_area,
'temperature': temperature,
'photosynthesis_rate': result['total_rate'],
'details': result
})
return {
'total_canopy_photosynthesis': total_photosynthesis,
'voxel_results': detailed_results,
'average_rate': total_photosynthesis / len(detailed_results) if detailed_results else 0
}
Step 8: Complete Workflow Integration
class CanopyPhotosynthesisSimulator:
def __init__(self):
self.capture_system = None
self.reconstruction_engine = None
self.mesh_processor = None
self.canopy_builder = None
self.ray_tracer = None
self.photosynthesis_model = None
def run_complete_simulation(self, config):
"""Run complete canopy photosynthesis simulation"""
print("Starting canopy photosynthesis simulation...")
# 1. Image capture
print("Step 1: Multi-view image capture")
if config['use_existing_images']:
image_dir = config['image_directory']
else:
image_dir = self.capture_multi_view_images(config['capture_config'])
# 2. 3D reconstruction
print("Step 2: 3D reconstruction")
if config['reconstruction_method'] == 'sfm':
point_cloud = self.run_sfm_reconstruction(image_dir)
elif config['reconstruction_method'] == '3dgs':
point_cloud = self.run_3dgs_reconstruction(image_dir)
else:
raise ValueError("Unsupported reconstruction method")
# 3. Mesh generation
print("Step 3: Mesh generation")
plant_mesh = self.generate_plant_mesh(point_cloud)
# 4. Organ segmentation
print("Step 4: Organ segmentation")
plant_segments = self.segment_plant_organs(plant_mesh)
# 5. Canopy construction
print("Step 5: Canopy construction")
canopy_mesh, canopy_plants = self.build_canopy(plant_mesh, plant_segments, config['canopy_config'])
# 6. Ray tracing
print("Step 6: Ray tracing simulation")
light_distribution = self.simulate_light_distribution(canopy_mesh, config['light_config'])
# 7. Photosynthesis calculation
print("Step 7: Photosynthesis calculation")
photosynthesis_results = self.calculate_canopy_photosynthesis(
light_distribution, canopy_plants, config['environmental_config']
)
# 8. Results analysis and visualization
print("Step 8: Results analysis and visualization")
self.analyze_and_visualize_results(photosynthesis_results, config['output_dir'])
return photosynthesis_results
def analyze_and_visualize_results(self, results, output_dir):
"""Analyze and visualize results"""
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Create output directory
os.makedirs(output_dir, exist_ok=True)
# 1. Photosynthesis rate distribution plot
self.plot_photosynthesis_distribution(results, output_dir)
# 2. Light distribution visualization
self.plot_light_distribution(results, output_dir)
# 3. Statistical analysis
self.generate_statistical_report(results, output_dir)
# 4. 3D visualization
self.create_3d_visualization(results, output_dir)
def generate_statistical_report(self, results, output_dir):
"""Generate statistical report"""
report = {
'total_canopy_photosynthesis': results['total_canopy_photosynthesis'],
'average_rate': results['average_rate'],
'num_active_voxels': len(results['voxel_results']),
'statistics': {}
}
if results['voxel_results']:
rates = [r['photosynthesis_rate'] for r in results['voxel_results']]
light_intensities = [r['light_intensity'] for r in results['voxel_results']]
report['statistics'] = {
'photosynthesis_rate': {
'mean': np.mean(rates),
'std': np.std(rates),
'min': np.min(rates),
'max': np.max(rates),
'median': np.median(rates)
},
'light_intensity': {
'mean': np.mean(light_intensities),
'std': np.std(light_intensities),
'min': np.min(light_intensities),
'max': np.max(light_intensities),
'median': np.median(light_intensities)
}
}
# Save report
with open(os.path.join(output_dir, 'simulation_report.json'), 'w') as f:
json.dump(report, f, indent=2)
# Generate text report
with open(os.path.join(output_dir, 'simulation_report.txt'), 'w') as f:
f.write("Canopy Photosynthesis Simulation Report\n")
f.write("=" * 40 + "\n\n")
f.write(f"Total Canopy Photosynthesis: {report['total_canopy_photosynthesis']:.2f} μmol s⁻¹\n")
f.write(f"Average Rate: {report['average_rate']:.2f} μmol m⁻² s⁻¹\n")
f.write(f"Number of Active Voxels: {report['num_active_voxels']}\n\n")
if 'photosynthesis_rate' in report['statistics']:
stats = report['statistics']['photosynthesis_rate']
f.write("Photosynthesis Rate Statistics:\n")
f.write(f" Mean: {stats['mean']:.2f} μmol m⁻² s⁻¹\n")
f.write(f" Std: {stats['std']:.2f} μmol m⁻² s⁻¹\n")
f.write(f" Min: {stats['min']:.2f} μmol m⁻² s⁻¹\n")
f.write(f" Max: {stats['max']:.2f} μmol m⁻² s⁻¹\n")
# Usage example
def main():
# Configuration parameters
config = {
'use_existing_images': True,
'image_directory': './plant_images',
'reconstruction_method': 'sfm', # 'sfm' or '3dgs'
'canopy_config': {
'row_spacing': 0.75,
'plant_spacing': 0.25,
'num_rows': 5,
'plants_per_row': 10
},
'light_config': {
'sun_angles': [(60, 180), (45, 180), (30, 180)], # (elevation, azimuth)
'num_rays': 100000
},
'environmental_config': {
'temperature': 25.0,
'co2_concentration': 400.0,
'humidity': 0.6
},
'output_dir': './simulation_results'
}
# Run simulation
simulator = CanopyPhotosynthesisSimulator()
results = simulator.run_complete_simulation(config)
print(f"Simulation completed. Total canopy photosynthesis: {results['total_canopy_photosynthesis']:.2f} μmol s⁻¹")
if __name__ == "__main__":
main()
Conclusion
This comprehensive tutorial presents a complete workflow for canopy photosynthesis modeling based on 3D reconstruction, including:
- Multi-view Image Capture: Spherical capture strategy and quality control
- 3D Reconstruction: Both SfM and 3D Gaussian Splatting methods
- Mesh Processing: Point cloud to mesh conversion and organ segmentation
- Canopy Construction: Plant replication with random perturbation
- Ray Tracing: Efficient light distribution simulation algorithms
- Photosynthesis Calculation: Farquhar model-based photosynthesis rate computation
- Results Analysis: Statistical analysis and visualization
This system provides powerful tools for precision agriculture, plant breeding, and ecological research, helping researchers understand canopy photosynthetic characteristics and optimize cultivation management strategies.
Last updated: September 2025