Automated Plant Thinning Using Vision-Based Individual Plant Selection
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Solution Overview
Problem
Conventional crop thinning methods are costly, time-consuming, and lack flexibility in plant selection and removal, often treating contiguous plants as a single entity, leading to suboptimal yield maximization.
Innovation Solution
An automated system for plant necrosis inducement that identifies individual plants using machine learning and computer vision, allowing for selective plant retention and removal based on various parameters, optimizing plant spacing and yield through real-time image processing and virtual mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual crop thinning is performed by workers using hoes, then plant removal flexibility is maintained, but labor cost and time consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical hoeing with an automated system that uses optical detection (cameras) to identify plants and a mechanical elimination mechanism (such as a spraying device or cutting tool) to remove selected plants. This substitution maintains plant selection flexibility through computer vision while dramatically increasing operation speed and reducing labor requirements.
Solution Approach 2:
The system enables self-service automation where the machine autonomously performs plant identification, selection, and removal without continuous human intervention. The computer vision system automatically detects individual plants, the control system determines which plants to remove based on spacing requirements, and the elimination mechanism executes the removal, creating a self-contained automated thinning operation.
2Extent of automation
If conventional automated thinning systems remove plants at fixed intervals, then automation is achieved, but plant selection flexibility and yield optimization are lost
Solution Approach 1:
The system transitions from static fixed-interval removal to dynamic plant-by-plant selection. The computer vision system continuously captures images and processes individual plant positions in real-time, allowing the elimination mechanism to adapt its actions based on actual plant distribution, spacing requirements, and yield optimization criteria rather than following a predetermined fixed pattern.
Solution Approach 2:
The system applies different treatment decisions to different local positions in the field based on individual plant characteristics and local spacing conditions. Each plant is evaluated independently, and the elimination mechanism applies removal or retention decisions tailored to specific local contexts rather than applying a uniform fixed-interval pattern across the entire field.
3Device complexity
If system vision treats close-packed plants as a single plant, then detection simplicity is maintained, but plant identification accuracy decreases
Solution Approach 1:
The computer vision system segments the detected plant mass into individual plant instances by analyzing image data to distinguish separate plant structures even when they are close-packed or overlapping. Image processing algorithms divide the continuous plant coverage into discrete individual plant regions, enabling accurate identification and separate targeting of each plant for potential removal.
Data Source
AI summary
A method of real-time plant selection and removal from a plant field including capturing a first image of a first section of the plant field, segmenting the first image into regions indicative of individual plants within the first section, selecting the optimal plants for retention from the first image based on the first image and the previously thinned plant field sections, sending instructions to the plant removal mechanism for removal of the plants corresponding to the unselected regions of the first image from the second section before the machine passes the unselected regions, and repeating the aforementioned steps for a second section of the plant field adjacent the first section in the direction of machine travel.