Clarification of Water Stress in Apple Seedlings Using HSI Texture with Machine Learning Technique
Keywords:
Hyperspectral imaging; machine learning; plant water stress; plant leaf; plant physiology
Abstract
Apples are known for their nutrition and economic value. Accurate and rapid diagnosis of water status in apple seedlings on an individual rootstock basis is a prerequisite for precision water management. This study presents a rapid and non-destructive approach for estimating water content in apple seedlings at leaf levels. A PIKA L system collects hyperspectral images(400-1000nm) of apple leaves. To the author's knowledge, no prior work was conducted using the spectral-texture approach in plant water stress. Our research extracts spatial information, gray-level co-occurrence matrix (GLCM), from feature wavelength images of hypercubes. Machine learning methods are applied to these spatial feature matrixs to identify apple leaves under different water stresses. In addition, differences in spectral responses were analysed using machine learning techniques for sorting apple seedlings with varying water treatments (dry, normal, and overwatering). Also, we measure chlorophyll to determine the relationship between hyperspectral characteristics and physiological changes. The achievements of the research indicate that the fusion of texture and hyperspectral imaging coupled with machine learning techniques is promising and presents a powerful potential to determine the water stress in the leaves of apple seedlings.
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Published
2024-01-23
How to Cite
An, Y., & Wang, R. (2024). Clarification of Water Stress in Apple Seedlings Using HSI Texture with Machine Learning Technique. European Scientific Journal, ESJ, 25, 518. Retrieved from https://eujournal.org/index.php/esj/article/view/17699
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ESI Preprints
Copyright (c) 2024 Yanying An, Ran Wang
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