[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119226-en":3,"doc-seo-119226-105":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},119226,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Models Comparison for Water Stress Detection Based on Stem Electrical Impedance Measurements - Research paper","Smart agriculture leverages electronics and sensing to monitor crop and environment parameters and to transform the resulting data into actionable insights. This study compares multiple machine learning models trained to detect plant water stress using stem electrical impedance, a directly measured parameter. Models are evaluated with three metrics, with mean accuracy exceeding 85%. Removing stem electrical impedance degrades performance, highlighting its practical impact for reliable water-stress classification.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nMachine Learning Models Comparison for Water Stress Detection Based on Stem Electrical Impedance Measurements  \nOriginal  \nMachine Learning Models Comparison for Water Stress Detection Based on Stem Electrical Impedance Measurements / Cum, Federico; Calvo, Stefano; Demarchi, Danilo; Garlando, Umberto. -ELETTRONICO. - (2023), pp. 108-112.(Intervento presentato al convegno 2023 IEEE Conference on AgriFood Electronics (CAFE) tenutosi a Torino (Italy) nel 25-27 September 2023) [10 . 1109/CAFE58535 .2023. 10291805] .  \nAvailability:  \nThis version is available at: 11583/2985342 since: 2024-01-26T12:36:59Z  \nPublisher: IEEE  \nPublished  \nDOI:10.1109/CAFE58535.2023.10291805  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \nIEEE postprint/Author's Accepted Manuscript  \n©2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.  \n(Article begins on next page)  \n07 November 2024  \nMachine Learning Models Comparison for Water Stress Detection based on Stem Electrical Impedance Measurements  \nFederico Cum∗ , Stefano Calvo∗ , Danilo Demarchi∗ and Umberto Garlando∗  \n∗ Department of Electronics and Telecommunications (DET), Politecnico di Torino, Torino, Italy  \nEmail: umberto.garlando@polito.it  \nAbstract—Smart agriculture is a promising solution to improve food production and reduce waste of resources. The idea is to adopt electronics and sensors to monitor key parameters of the crops and integrate these data with farmer knowledge. Sensors monitor both the environment and the plant itself, generating a huge amount of data. Data processing is a key aspect of smart agriculture, and machine learning can help to understand the data and extract the needed feature. In this paper, we present a performance comparison of several machine learning models trained to detect the water stress condition of plants. The dataset used for this study includes the stem electrical impedance, a novel parameter directly measured on the plants. The machine learning models are compared based on three different metrics, and the average accuracy is higher than 85% . The effect of removing the stem electrical impedance results in worse performance of the models, indicating its impact in the application.  \nIndex Terms—Machine learning, support vector machines, decision tree, random forest, smart agriculture, food security  \nI. INTRODUCTION  \nIn recent years, the world has been facing the effects of global warming. The progressive rise in global temperatures, primarily triggered by the release of greenhouse gases into the atmosphere, is exacerbating the desertification phenomenon, placing an increasing number of lands at risk and reducing the available grounds for cultivation [1] . Moreover, the global population is constantly growing, and the projections suggest it is expected to surpass 10 Billion by 2050 [2] . Global food security is a big concern as climate change, and the subsequent lack of water affect crop production’s potential yield [3] . Smart agriculture, also known as precision agriculture or digital farming, offers advanced technologies and data-driven approaches in agricultural practices to optimize and enhance various aspects of crop production and livestock management. It leverages innovative technologies such as the Internet of Things (IoT), artificial intelligence (AI), drones, sensors, and big data analytics to monitor, collect, and analyze real-time data about soil conditions, weather patterns, crop growth, and livestock health [4] . One of the main aspects to consider when building an","cbCaicPsINs7RteO","https://ap.wps.com/l/cbCaicPsINs7RteO","pdf",323450,1,6,"English","en",105,"# Introduction\n## Smart agriculture and monitoring needs\n## Using stem impedance for water-stress evaluation\n# Related Work\n## Machine learning in smart agriculture\n## Disease and pest detection with computer vision","[{\"question\":\"What data source is used to detect water stress in this study?\",\"answer\":\"The study uses stem electrical impedance measurements from tobacco plants, combined with environmental measurements as model features.\"},{\"question\":\"Which machine learning models are compared for water-stress detection?\",\"answer\":\"The paper compares Support Vector Machines (SVM), Decision Trees, Random Forests, and Artificial Neural Networks.\"},{\"question\":\"How does removing stem electrical impedance affect model performance?\",\"answer\":\"Removing stem electrical impedance leads to worse performance across the evaluated models, indicating its importance for the application.\"}]","Machine Learning Models Comparison for Water Stress Detection Based on Stem Electrical Impedance Measurements - 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