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2024 | OriginalPaper | Buchkapitel

Plant Data Generation with Generative AI: An Application to Plant Phenotyping

verfasst von : Swati Bhugra, Siddharth Srivastava, Vinay Kaushik, Prerana Mukherjee, Brejesh Lall

Erschienen in: Applications of Generative AI

Verlag: Springer International Publishing

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Abstract

Plant phenotyping is the study of plants’ physiological, morphological and biochemical traits resulting from their interaction with the environment. These traits (e.g., leaf area, leaf count, tillering, wilting etc.) are crucial in current plant research, focused on improving plant quality i.e., disease resistance, drought resistance and productivity. With the advancement in sensor technologies, image based analysis via various computer vision methods (e.g., image classification, segmentation, object detection etc.) have emerged in plant phenotyping. Specifically, state-of-the-art deep learning models have been employed for high-throughput study of plant traits. However, the application of deep learning models is currently limited due to the high variability in plant traits among various plant species and unstructured plant imaging. Additionally, complex plant traits pose high data collection and annotation costs. In this context, generative artificial intelligence (AI) based on the evolution of generative adversarial networks (GANs) for data synthesis can relieve the current bottleneck of data scarcity and plant species gap. This chapter reviews the application of state-of-the-art GANs for plant image datasets such as leaf, weed, disease etc. It also discusses the current Generative AI challenges and future directions for agriculture data synthesis.

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Vorheriges Kapitel The Economics of Generative AI

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Titel: Plant Data Generation with Generative AI: An Application to Plant Phenotyping
verfasst von: Swati Bhugra
Siddharth Srivastava
Vinay Kaushik
Prerana Mukherjee
Brejesh Lall
Verlag: Springer International Publishing
Buch: Applications of Generative AI
Print ISBN: 978-3-031-46237-5

Electronic ISBN: 978-3-031-46238-2

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-46238-2_26

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