[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119916-en":3,"doc-seo-119916-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119916,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Highlighting Water Stress in Apple Seedlings Using HSI Texture with Machine Learning Technique","Apples rely on timely, precise water control, and early identification of seedling water status is essential for precision irrigation. The study proposes a rapid, non-destructive method to estimate leaf-level water content in apple seedlings using hyperspectral imaging in the 400–1000 nm range. A PIKA L system captures hyperspectral images, extracts gray-level co-occurrence matrix (GLCM) spatial texture features from wavelength images, and applies machine learning to classify leaves under dry, normal, and overwatered treatments. Chlorophyll is measured to relate hyperspectral and physiological changes, showing that texture-fused hyperspectral imaging with machine learning enables promising detection of leaf water stress.","Highlighting Water Stress in Apple Seedlings Using HSI Texture with Machine Learning Technique  \nYanying An  \nSchool of Information Technology, Murdoch University, Australia Qingdao Agricultural University, China  \nRan Wang  \nQingdao Agricultural University, China  \nDoi:10.19044/esj.2024.v20n6p1  \n\n| Submitted: 01 January 2024\u003Cbr>Accepted: 15 February 2024\u003Cbr>Published: 29 February 2024 | Copyright 2024 Author(s)\u003Cbr>Under Creative Commons CC-BY 4.0 OPEN ACCESS |\n| --- | --- |\n| Cite As:\u003Cbr>An Y. & Wang R. (2024). Highlighting Water Stress in Apple Seedlings Using HSI Texture with Machine Learning Technique. European Scientific Journal, ESJ, 20 (6), 1.\u003Cbr>[https://doi.org/10.19044/esj.2024.v20n6p1](https://doi.org/10.19044/esj.2024.v20n6p1) |  |\n| Abstract\u003Cbr>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. 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. |  |\n\nKeywords: Hyperspectral imaging; machine learning; plant water stress; plant leaf; plant physiology  \n1. Introduction  \nApple (Malus domestica Borkh) is one of the world's most widely planted and nutritionally significant fruit crops (Duan et al., 2017) . Apples are rich in nutrients vital for good health and disease prevention, making them a valuable choice each day for enhancing the quality of our diet. With increased income and public awareness of balanced nutrition, Global apple consumption is increasing annually (Li et al., 2013) . Apple seedlings are tiny and propagated tree stems grafted onto a hardy rootstock (Loucks, 2021). They are hugely profitable. In 2021, according to Willis Orchard Co. (2021), ten seedling trees at the height of 1-2 feet made a profit of $34.95, whereas ten  \nseedling trees at the height of 2-3 feet earned $59.95.  \nWater management is vital for apple orchards (Lim and Nam, 2007) . As a result of drought and increasing competition for water, orchardists need to adopt efficient water management strategies (PIRSA, 2006). Newly planted apple trees require weekly watering. Ideal apple tree irrigation involves deep root soaking (Ellis, 2021). The key is to let the water flow into the soil slowly to allow for deep watering until it is established (The Home Depot, 2021) . A plant induces leaf senescence prematurely if water shortage exceeds a critical level (Lim and Nam, 2007) . This process is characterised by loss of chlorophyll and leaf yellowing (Yamaguchi et al., 2010) . If wilting leaves are observable, irreversible damage to plants and yield occurs (Behmann et al., 2014) . On the other hand, creating standing water and soggy roots can be as damaging as drought conditions for the apple seedlings. Too much water depletes oxygen from the soil, prevents the roots from absorbing necessary minerals, and makes a tree susceptible to rotting and infections (Ellis, 2021) . The symptoms of overwatering also include wilting, yellowing of leaves, root rot ","cbCaipzr0UiOmlte","https://ap.wps.com/l/cbCaipzr0UiOmlte","pdf",2002887,1,35,"English","en",105,"# Introduction\n## Water management importance for apple orchards\n## Water stress impacts and visible symptom limitations\n## Existing detection methods for plant water stress\n## Chlorophyll as an indicator of plant physiological status","[{\"question\":\"What problem does the study address for apple seedlings?\",\"answer\":\"The study targets the need for accurate, rapid, and non-destructive diagnosis of water status at the leaf level to support precision water management in apple seedlings.\"},{\"question\":\"How are hyperspectral images and texture features used?\",\"answer\":\"A PIKA L system collects hyperspectral images (400–1000 nm), then extracts spatial texture information using the gray-level co-occurrence matrix (GLCM) from feature wavelength images.\"},{\"question\":\"How does machine learning contribute to water-stress identification?\",\"answer\":\"Machine learning models are applied to the spatial feature matrices to classify apple leaves under different water stresses, including dry, normal, and overwatering conditions.\"}]","Highlighting Water Stress in Apple Seedlings Using HSI Texture with Machine Learning Technique | 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problem does the study address for apple seedlings?","Question",{"text":75,"@type":76},"The study targets the need for accurate, rapid, and non-destructive diagnosis of water status at the leaf level to support precision water management in apple seedlings.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are hyperspectral images and texture features used?",{"text":80,"@type":76},"A PIKA L system collects hyperspectral images (400–1000 nm), then extracts spatial texture information using the gray-level co-occurrence matrix (GLCM) from feature wavelength images.",{"name":82,"@type":73,"acceptedAnswer":83},"How does machine learning contribute to water-stress identification?",{"text":84,"@type":76},"Machine learning models are applied to the spatial feature matrices to classify apple leaves under different water stresses, including dry, normal, and overwatering 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