Veröffentlichung von "Synthesizing Training Data for Intelligent Weed Control Systems Using Generative AI" / Publication of "Synthesizing Training Data for Intelligent Weed Control Systems Using Generative AI"   [10.10.24]

ENGLISH VERSION BELOW.

Ein weiteres Paper ist aus der Arbeit des KI-Fachgebiets erschienen.

Hier kommen die Hard-Facts zur Veröffentlichung.

Titel:
Synthesizing Training Data for Intelligent Weed Control Systems Using Generative AI

Autoren:
S. Modak und A. Stein

Venue:
Architecture of Computing Systems (ARCS 2024)

Abstract:
Deep Learning already plays a pivotal role in technical systems performing various crop protection tasks, including weed detection, disease diagnosis, and pest monitoring. However, the efficacy of such data-driven models heavily relies on large and high-quality datasets, which are often scarce and costly to acquire in agricultural contexts. To address the overarching challenge of data scarcity, augmentation techniques have emerged as a popular strategy to expand training data amount and variation. Traditional data augmentation methods, however, often fall short in reliably replicating real-world conditions and also lack diversity in the augmented images, hindering robust model training. In this paper, we introduce a novel methodology for synthetic image generation designed specifically for object detection tasks in the agricultural context of weed control. We propose a pipeline architecture for synthetic image generation that incorporates a foundation model called Segment Anything Model (SAM), which allows for zero-shot transfer to new domains, along with the recent generative AI-based Stable Diffusion Model. Our methodology aims to produce synthetic training images that accurately capture characteristic weed and background features while replicating the authentic style and variability inherent in real-world images with high fidelity. In view of the integration of our approach into intelligent technical systems, such a pipeline paves the way for continual self-improvement of the perception modules when put into a self-reflection loop. First experiments on real weed image data from a current research project reveal our method’s capability to reconstruct the innate features of real-world weed infested scenes from an outdoor experimental setting.

Unter folgendem Link kommen Sie zur gesamten Übersicht der bisherig veröffentlichten Forschungsarbeiten des Fachgebiets: ki-agrartechnik.uni-hohenheim.de/veroeffentlichungen

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Another paper has been published with the participation of the AI department.

Here are the hard facts about the publication.

Title:
Synthesizing Training Data for Intelligent Weed Control Systems Using Generative AI

Authors:
S. Modak and A. Stein

Venue:
Architecture of Computing Systems (ARCS 2024)

Summary:
Deep Learning already plays a pivotal role in technical systems performing various crop protection tasks, including weed detection, disease diagnosis, and pest monitoring. However, the efficacy of such data-driven models heavily relies on large and high-quality datasets, which are often scarce and costly to acquire in agricultural contexts. To address the overarching challenge of data scarcity, augmentation techniques have emerged as a popular strategy to expand training data amount and variation. Traditional data augmentation methods, however, often fall short in reliably replicating real-world conditions and also lack diversity in the augmented images, hindering robust model training. In this paper, we introduce a novel methodology for synthetic image generation designed specifically for object detection tasks in the agricultural context of weed control. We propose a pipeline architecture for synthetic image generation that incorporates a foundation model called Segment Anything Model (SAM), which allows for zero-shot transfer to new domains, along with the recent generative AI-based Stable Diffusion Model. Our methodology aims to produce synthetic training images that accurately capture characteristic weed and background features while replicating the authentic style and variability inherent in real-world images with high fidelity. In view of the integration of our approach into intelligent technical systems, such a pipeline paves the way for continual self-improvement of the perception modules when put into a self-reflection loop. First experiments on real weed image data from a current research project reveal our method’s capability to reconstruct the innate features of real-world weed infested scenes from an outdoor experimental setting.

The following link will take you to the complete overview of the research work published to date by the department: ki-agrartechnik.uni-hohenheim.de/veroeffentlichungen

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