Automated Fruit Harvesting Stem Detection and Cutting
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Solution Overview
Problem
Current fruit harvesting methods, especially for grapes, face challenges in preserving the integrity of the berries and bunches while efficiently identifying and cutting ripe fruits, often requiring significant labor and struggling to differentiate between stems and other elongated objects.
Innovation Solution
An automated method and mechanical equipment that use image processing and multispectral or hyperspectral imaging to detect and locate stems, allowing for precise identification and cutting of fruits at the peduncle level, while also determining maturity and health status, and navigating obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mechanical shaking or beating is used to harvest fruit, then harvesting speed and productivity are improved, but the integrity of berries and bunches deteriorates due to oxidation and juice alteration
Solution Approach 1:
The patent replaces mechanical shaking or beating systems with an automated cutting system that uses image processing to locate and precisely cut individual stems. This substitution eliminates the mechanical impact that causes berry damage while maintaining harvesting efficiency through automation.
Solution Approach 2:
The system uses image processing and analysis to automatically identify, locate, and cut stems without human intervention. The automated detection and cutting mechanism serves itself by processing visual data to guide the harvesting action, eliminating the need for manual labor while preserving fruit quality.
2Reliability
If manual cutting of bunches is used to preserve berry integrity, then berry integrity is improved, but labor requirements and time consumption increase significantly
Solution Approach 1:
The patent replaces manual cutting operations with an automated robotic system that uses image processing to identify and cut stems. This substitution maintains the gentle cutting action that preserves bunch integrity while eliminating the labor-intensive nature of manual harvesting.
Solution Approach 2:
The automated system performs the entire harvesting process independently by detecting stems through image processing, calculating cutting positions, and executing cuts without human assistance. This self-service capability maintains quality standards while dramatically improving harvesting efficiency.
3Measurement precision
If tracking means are manually placed on peduncles to identify ripe fruits, then harvesting precision is improved, but the quantity of skilled labor required increases
Solution Approach 1:
The patent replaces manual placement of tracking means with an automated image processing system that detects and identifies ripe fruits and their stems through visual analysis. This substitution eliminates the need for workers to manually attach tracking devices while maintaining precise identification accuracy.
Solution Approach 2:
The system creates a digital representation of the fruit and stem structure through image processing, allowing virtual identification and localization without physical tracking means. This copying approach eliminates manual intervention while preserving measurement precision.
4Extent of automation
If image analysis methods are used to identify fruits, then automation level is improved, but the ability to discriminate between stems and other elongated objects deteriorates
Solution Approach 1:
The patent applies different analysis strategies to different parts of the image: general fruit detection uses one approach while stem identification uses specialized elongated object detection with specific geometric and contextual criteria. This localized quality adjustment improves stem discrimination accuracy within the automated system.
Solution Approach 2:
The system changes detection parameters when transitioning from general fruit identification to specific stem localization, using parameters such as elongation ratio, orientation, and spatial relationship to fruits to distinguish stems from other elongated objects. This parameter adaptation maintains high automation while improving identification precision.
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 fully automated, labor-efficient fruit harvesting that preserves berry integrity, accurately identifies ripe fruits, and optimizes the harvesting process by integrating robotic arms and cameras for precise cutting and sorting, reducing manual labor and improving operational efficiency.
Implementation Method 1
Each pixel can in particular be associated with a radiation intensity in a band of wavelengths in the visible spectrum, defined as the electromagnetic spectrum with a wavelength between 390 nm (nanometers) and 780 nm
Data Source
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AI summary
The invention relates to the field of automated fruit harvesting, particularly for fruits in bunches such as grapes. It concerns a method for harvesting fruit borne on a stem comprising image processing carried out in two phases. According to the invention, the method (100) comprises: ▪ a step (120) of detecting areas of interest in an overall image representing a plant, in which one or more areas of interest are detected in the overall image using an image processing process, each area of interest comprising at least a part of a fruit, ▪ a step (130) of acquiring local images, in which a local image is acquired in the vicinity of each area of interest, each local image representing at least a part of the fruit in the area of interest considered, and ▪ a step (150) of identifying stems, in which a stem is identified in each local image.