3D Object Classification and Biomass Analysis in Agriculture: Precision Through Digital Twins
Introduction
Artificial intelligence is transforming agriculture—now in 3D. While many AI applications have traditionally relied on 2D data such as satellite imagery, 3D point cloud classification is rapidly gaining attention. This technology opens up entirely new possibilities for capturing and analyzing agricultural structures with remarkable precision. Thanks to modern drones, scanners, and mobile devices, creating 3D scans is now easier and more cost-effective than ever, significantly advancing their use in agronomy. One particularly exciting application is the quantification of biomass—a key metric for growth, yield, and plant health. With rising demands for sustainability, resource efficiency, and climate resilience, 3D analysis offers farmers a major leap forward in innovation.
Several years ago, we developed a platform for analyzing 3D point clouds, which has since become a market leader. Pointly enables efficient processing of point clouds and extraction of valuable insights. We are now combining our deep expertise in agriculture and plant science with the world of 3D technology to expand our offering. Through this unique synergy and the capabilities of Pointly, we are unlocking new potential for precision agriculture.
Digital Twins in Agriculture
A digital twin is an exact virtual representation of a real-world object—in this case, a tree or plant. By combining 3D scans, multispectral data, and machine learning, each plant can be captured, analyzed, and monitored with high accuracy.
What makes this approach unique is that a digital twin goes beyond simple visual representation. It integrates multiple data sources such as weather conditions, soil properties, or spraying protocols to create a comprehensive picture of a plant’s health and growth. This fusion of data enables precise biomass calculations and supports data-driven optimization of agricultural processes.
Use Case: Biomass Quantification in Fruit Trees
A concrete example of 3D object classification in practice is the quantification of biomass in fruit trees. This method delivers detailed insights into plant growth by capturing and analyzing structural components like branches, leaves, and fruits separately.
Capturing the Tree as a Digital Twin
Using drones, LiDAR scanners, or multispectral cameras, a complete 3D model of a fruit tree can be created. This model contains a high-resolution point cloud that accurately represents the tree’s structure, forming the basis for automated classification.
Structural Classification and Analysis
Artificial intelligence can be used to precisely classify various components of a tree:
- Branches and canopy – for determining wood mass and structural stability
- Leaves – to calculate leaf area, an indicator of photosynthetic activity
- Fruits – for quantifying fruit mass and improving harvest forecasts
Based on these structural features, the total biomass of a tree can be estimated. The data can also be combined with historical growth patterns to forecast yields and enable timely interventions to improve plant health.
Integration with Additional Data Sources
3D scanning alone is only part of the analysis. Combining it with other data sources enables even deeper insights:
- Satellite and drone data enhance the 3D models with NDVI analyses to assess plant health.
- Weather and climate data are incorporated into forecasting models to detect stress-induced growth changes.
- Spraying and fertilization records are aligned with biomass analysis to improve efficiency in crop protection and fertilization.
Benefits of 3D Object Classification for Farmers
Using digital twins and 3D object classification in agriculture provides numerous benefits:
- More efficient use of crop protection products: Precise biomass measurement allows for targeted, reduced spraying—saving costs and minimizing environmental impact.
- Yield forecasting: Accurate quantification of plant components enables better yield predictions.
- Optimized resource use: Fertilization and irrigation can be tailored to the actual needs of each plant, reducing costs and ecological impact.
- Automated monitoring: Regular scans make it possible to continuously document plant growth and compare it with historical data.
- Early problem detection: Issues such as disease or nutrient deficiencies can be identified early and addressed proactively.
