Digital phenotyping has revolutionized the field of agriculture by providing novel ways to monitor and analyze plant growth and development. One fascinating application of digital phenotyping is in orchards, where the accurate counting and classification of fruits plays a crucial role in yield estimation, resource allocation, and overall orchard management. In this blog post, we will explore the significance of digital phenotyping in orchards and focus on a pipeline that employs terrestrial LiDAR scanners for counting and classifying fruits in apple tree orchards.


The Use of Digital Phenotyping in Orchards


Digital phenotyping involves the use of advanced technologies, such as remote sensing, computer vision, and machine learning, to extract meaningful information about plants. In orchards, digital phenotyping offers several advantages. It enables growers to monitor the health and productivity of trees, assess the effects of different input products or environmental conditions, and optimize resource allocation to produce varieties with improved yields. Accurate fruit counting and classification is particularly valuable, as it helps estimate crop yields, plan harvesting operations, and make informed decisions regarding fertilization, irrigation, and pest control.


Suitable Vectors and Sensors for Counting and Classifiaction Traits


As you may know, imaging solutions for phenotyping are actionable trhough different system types i.e., Vectors. You can discover a detailed list of systems that are suitable for most agricultural applications HERE. For orchards, since they can be quite dense, portable Handheld systems and machinery systems like PhenoMobile are of most interest, even though Drones can be of good help to create some data fusion from a bird eye's view.

In terms of sensors, terrestrial LiDAR scanners have proven to be a very reliable source of information while phenotyping for fruit counting and classification in orchards. These scanners emit laser beams that measure the distance to surrounding objects, creating a detailed 3D representation of the environment. They can be mounted on mobile platforms as mentioned before, such as drones, handheld systems or ground vehicles, and scan the orchard trees from multiple angles. The resulting point cloud data provides a rich source of information that can be processed to extract meaningful information i.e., Plant traits about the behavior of the trees and their organs.


Hiphen's R&D engineer Nathan guilhot, acquiring data on Apple trees, with Hiphen's new handheld system developed with Arvalis.


The Process of Counting and Classifying Fruits on Apple Trees


The process of counting fruits in orchard typically involves a pipeline consisting of 3 main steps: global tree segmentation, fruit semantical segmentation, and fruit clusterization.

  • The tree segmentation: In the segmentation step, the orchard point cloud is processed to separate individual trees from the background and neighboring trees. This process involves algorithms that analyze the point cloud data to identify distinct tree structures based on their shape, height, and other characteristics. Segmentation allows the subsequent steps to focus on analyzing individual trees separately.


Segemnted trees from above.


  • The semantical segmentation: The critical step in the pipeline involves semantical segmentation, where the point cloud data for each tree is further analyzed using 3D deep learning techniques. For that purpose we can use neural networks designed for point cloud analysis, this will ensure great capturing of the spatial relationships between individual points in the dense cloud. From that we can start segmenting trees into semantic categories such as branches, leaves, and fruits. But like every artificial intelligence model, it needs to be trained and enriched from annotated and validated datasets and this is a process that takes time to end up having a robust model, which will accurately label new point clouds and identify the points related to fruits within each tree's structure, and differentiate them from points related to branches or leaves.


In red color, we can see all 3D points (from dense cloud) identified as points constituing apple fruits


  • The clusterization and fruit counting: After semantical segmentation, the point cloud data is clustered to group points that belong to individual fruits. The clusterization process helps identify spatially connected points that represent individual fruits, primarily focusing on the labeled fruit points. Once the clusters have been identified, counting the number of fruit clusters provides an estimate of the total fruit count in the tree.


In this illustration, all points referring to the same apple fruit have been grouped to highlight every single fruit in the tree.




Digital phenotyping, with its ability to automate and phenotype larger orchards at higher speeds, brings significant advantages to fruit counting and classification and orchard management. By employing advanced technologies, such as 3D deep learning and point cloud analysis, researchers can efficiently assess large orchards, and estimate fruit counts. This frictionless phenotyping experience offers researchers the opportunity to make insightful data-driven decisions, optimize resource allocation, make precise yield prediction and enhance overall orchard productivity.

The integration of digital phenotyping into agriculture is a transformative approach, empowering growers with valuable insights and driving sustainable and efficient practices in the management of orchards globally.


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Your Hiphen Team.
Topic brought to you by Nathan Guilhot, R&D Engineer @Hiphen