3D Orchard Row Trajectory Identification for Autonomous Alignment

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Solution Overview

Problem

Existing agricultural machinery for harvesting crops like oranges faces challenges in maintaining alignment with tree rows, leading to damage due to misalignment, especially in autonomous operations where skilled personnel are scarce, and issues with recognizing missing or replaced trees disrupt trajectory calculation.

Innovation Solution

Utilizing a 3D sensor to acquire a point cloud of the field in front of the vehicle, sectioning it into slices for tree alignment recognition, and employing algorithms to correct vehicle trajectory through plant recognition or fallback procedures when trees are not detectable, ensuring minimal human intervention.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If the vehicle uses autonomous driving with standard trajectory recognition, then productivity increases and human intervention decreases, but reliability deteriorates when trees are missing or replaced causing trajectory calculation failure

Engineering Contradiction:

Improveautonomous operation capabilityVSAvoidtrajectory recognition stability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system pre-establishes a fallback mechanism by storing the last valid trajectory data before autonomous operation begins. When the 3D sensor fails to detect trees due to missing or replaced plants, the system automatically switches to using the pre-stored trajectory information, preventing complete system failure and maintaining reliable operation throughout the harvesting process.

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

2Manufacturing precision

If the vehicle maintains strict alignment with tree rows using conventional methods, then manufacturing precision improves, but object-affected harmful factors increase due to rotor misalignment damaging trees

Engineering Contradiction:

Improvevehicle alignment accuracyVSAvoidtree damage from rotor misalignment

Core Design Contradiction:

Manufacturing precisionVSObject-affected harmful factors

Solution Approach 1:

The system replaces conventional mechanical alignment methods with a vision-based approach using 3D sensors and image processing algorithms. The camera captures the tree row configuration, and computational algorithms calculate the optimal trajectory, substituting mechanical alignment mechanisms with optical sensing and digital processing to achieve both precision and tree safety.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces an intermediary computational layer between the vehicle control system and the physical tree row. Image processing algorithms act as intermediaries that analyze the 3D sensor data, calculate misalignment angles, and generate correction commands, thereby preventing direct mechanical contact issues and reducing tree damage from rotor misalignment.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Ease of operation

If the driver's cab is positioned for easy access, then ease of operation improves, but measurement precision deteriorates due to suboptimal viewing angle for aligning with row centerline

Engineering Contradiction:

Improvedriver accessibilityVSAvoidrow alignment accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system replaces the driver's visual alignment method with an automated optical sensing system. The 3D sensor and image processing algorithms objectively measure and calculate the row centerline position, eliminating the subjectivity and limitations of human visual judgment from the driver's cab position, thereby achieving high alignment precision regardless of cab placement.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Enables autonomous driving systems to maintain alignment with tree rows, reducing tree damage and ensuring consistent harvesting despite variations in tree presence, with enhanced accuracy and reduced reliance on human intervention.

Implementation Method 1

a 3D sensor (3DS) is used to acquire a so-called point cloud of the field in front of the vehicle

Methodology Applied

Scientific EffectLight reflection: Reflection

Data Source

PatentEP4122305B1Method for identifying a trajectory in a plantation of fruit trees such as orange groves

Publication Date: 2026.03.25 CNH IND ITALIA SPA

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Figure 1c
Figure 2~3

AI summary

A method of identifying a trajectory superimposed on a centerline (TAC) of a plantation to be processed, in particular of oranges, grapes, coffee, almonds and similar row crops, by means of a 3D sensor (3DS), the method including ( Step 1) the acquisition of a 3D point cloud from said 3D sensor corresponding to reflections detected by said 3D sensor (3DS) from a scenario in front of the vehicle (V), fitting of the points to obtain a pseudo-elliptical interpolating curve on the two-dimensional map from the 3D point cloud, (Step 5) identification of a peak (PK1) associated with said pseudo-elliptic curve by interpolating and calculating a lateral misalignment (OTE) between this peak (PK1) and a vertical line of the two-dimensional map corresponding to the vehicle center line (VC), (Step 6) control of a vehicle trajectory such as to minimize said distance.