NEWS: Precision management for orchard growers a step closer using drones and LiDAR sensing

A major step forward has been achieved in precision orchard management, and the future-proofing of fruit production in the face of climate change, with new research from the UK crop research organisation Niab and Nanjing Agricultural University in China, alongside international partners.

The team has developed OrchardQuant 3D – a breakthrough pipeline that measures fruit trees in three dimensions with unprecedented accuracy. This new research demonstrates how complex 3D data collected from the air and from the ground using different types of sensors (drone cameras and LiDAR – which stands for light detection and ranging) can be accurately fused together and interpreted. This enables scalable 3D mapping and automated trait analysis, generating information of each tree within an entire orchard. 

The system creates detailed 3D models of apple and pear orchards to capture tree height, crown volume, branch structure and blossom density – traits that underpin fruit yield and quality. Successfully demonstrated in pear orchards in China and apple orchards at Niab in the UK, the pipeline can scale from tens to thousands of trees, automating processes that previously required weeks of manual assessment.

This enhanced phenotype data benefits growers and agronomists by allowing and improving the measurement of characteristics of each tree, for example in calculating the number of blossoms, fruitlets and fruit, and measuring the canopy size, structure, and density. These metrics can be used to improve agronomic decisions, such as thinning and pruning, and create dose prescription maps for variable rate spray machines, to optimise the crop load for each individual tree in the orchard, ultimately driving up orchard productivity.

Niab crop protection specialist Dr Charles Whitfield explains the importance of this new research. “This innovation addresses a critical bottleneck in horticulture: phenotyping at scale. Traditional methods are labour-intensive and often unable to keep pace with modern orchard systems or changing environmental conditions. By combining colour and spatial data, OrchardQuant 3D provides actionable insights for breeding, management and research, paving the way for more resilient, sustainable and high-quality fruit production.”

The codebase has been released openly, enabling rapid adoption by scientists and growers worldwide. Future developments aim to integrate yield prediction, disease monitoring and advanced decision support. The methodology is currently a research tool but may become more widely available in the future. 

Crop breeders will be able to use this method to precisely quantify tree traits, greatly improving the efficiency of breeding programmes and assisting in the development of new varieties better suited to the future, including changes in climate and orchards being maintained by increasingly automated systems. 

Professor Ji Zhou, who led the Niab research team in the UK alongside his team at Nanjing, emphasises that this groundbreaking research demonstrates that high precision mapping of complex 3D structures and blossom clusters/fruit detection can be achieved at orchard-scale. “It opens the door to improving the orchard breeding and agronomic knowledge available to breeders and growers which will drive improvements to orchard management and productivity, leading to more class one fruit per tree and ultimately a better return per hectare for the UK’s hard fruit production,” explains Professor Zhou.

Dr Rob Jackson, deputy programme leader for crop phenotyping at Niab, performed the drone mapping in the UK. “Our work demonstrates the scale of agronomic information that can now be efficiently collected by a single drone pilot, supported by a small data processing team. In the future it could also alleviate issues concerning staff availability.”

“Part of research was completed within the Precision Orchard Management and Environment (POME) project, funded by Defra and Innovate UK, and its output will be used within the project to continue to advance precision agriculture. Niab will use the research to support crop breeding and other endeavours for the UK horticultural sector,” concludes Dr Whitfield.

Notes

This research was funded by multiple grants and initiatives, including the Precision Orchard Management and Environment (POME) grant (IUK 10072930), One CGIAR’s SeedEqual Initiative (5507-CGIA-07), the BBSRC AI in Plant Research Grant (BB/Y513969/1), and the BBSRC International Partnership Grant (BB/Y514081/1).

The research, published in Plant Biotechnology Journal: https://onlinelibrary.wiley.com/doi/10.1111/pbi.70229