[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290892-105":59,"doc-detail-290892-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","development-potential-of-nanoenabled-agriculture-projected-using-machine-learning","Development potential of nanoenabled agriculture projected using machine learning","","The study addresses uncertainty about how nanoenabled agriculture may develop, given that nanoparticle (NP) effects depend on NP type, plant species, and environmental conditions. Using a machine learning approach, researchers built an NP-plant database of 1,174 datasets and predicted plant response and NP uptake/transport with multiple random-forest models. Multiway feature importance indicates drivers including total NP exposure dose, duration, plant age, plus NP size and zeta potential, while feature interaction analysis improves interpretability. Results suggest Fe2O3 NP effects differ by region due to temperature, with oxidative stress risks varying across Africa and Europe.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/development-potential-of-nanoenabled-agriculture-projected-using-machine-learning/290892/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/development-potential-of-nanoenabled-agriculture-projected-using-machine-learning/290892.png","ImageObject",300,407,{"name":92,"@type":93},"Rowan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What dataset and modeling approach were used to project nanoenabled agriculture potential?","Question",{"text":112,"@type":113},"The study built an NP-plant database containing 1,174 datasets and used machine learning models, including random forest models, to predict plant response and NP uptake/transport.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which factors were identified as driving plant responses to nanoparticles?",{"text":117,"@type":113},"Plant responses were driven by total NP exposure dose and duration and by plant age at exposure, as well as NP size and zeta potential.",{"name":119,"@type":110,"acceptedAnswer":120},"How do the projections differ between Africa and Europe?",{"text":121,"@type":113},"The model suggests Fe2O3 NP application may inhibit bean growth in Europe due to low night temperatures, while oxidative stress risks are low in Africa due to high night temperatures, making Africa a suitable area for nanoenabled agriculture.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290892,1789642903,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":41,"preview_page_count":4},1099514067415,"https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502","Development potential of nanoenabled agriculture projected using machine learning  \n\n| Item Type | Article |\n| --- | --- |\n| Authors | Deng , Peng;Gao , Yiming;Mu , Li;Hu , Xiangang;Yu , Fubo;Jia , Yuying;Wang , Zhenyu;Xing , Baoshan |\n| Download date | 2026-06-06 06:37:18 |\n| Link to Item | [https://hdl.handle. net/20.500.14394/43940](https://hdl.handle. net/20.500.14394/43940) |\n\nDownloaded from [https://www.pnas.org by UNIVERSITY OF MASSACHUSETTS AMHERST SERIALS DEPT/ACQ DEPT on November 8](https://www.pnas.org by UNIVERSITY OF MASSACHUSETTS AMHERST SERIALS DEPT/ACQ DEPT on November 8), 2023 from IP address [128.119.201.49](128.119.201.49).  \nRESEARCH ARTICLE  \nAGRICULTURAL SCIENCES SUSTAINABILITY SCIENCE  \n OPEN ACCESS  \nDevelopment potential of nanoenabled agriculture projected using machine learning  \nPeng Denga, Yiming Gaoa, Li Mub,1, Xiangang Hua,1, Fubo Yua, Yuying Jiaa, Zhenyu Wangc , and Baoshan Xingd   \nEdited by Catherine Murphy, University of Illinois at Urbana-Champaign, Urbana, IL; received February 2, 2023; accepted May 10, 2023  \nThe controllability and targeting of nanoparticles (NPs) offer solutions for precise and sustainable agriculture. However, the development potential of nanoenabled agriculture remains unknown. Here, we build an NP-plant database containing 1,174 datasets and predict (R2 higher than 0.8 for 13 random forest models) the response and uptake/ transport of various NPs by plants using a machine learning approach. Multiway feature importance analysis quantitatively shows that plant responses are driven by the total NP exposure dose and duration and plant age at exposure, as well as the NP size and zeta potential. Feature interaction and covariance analysis further improve the interpretability of the model and reveal hidden interaction factors (e.g., NP size and zeta potential) . Integration of the model, laboratory, and field data suggests that Fe2O3 NP application may inhibit bean growth in Europe due to low night temperatures. In contrast, the risks of oxidative stress are low in Africa because of high night temperatures. According to the prediction, Africa is a suitable area for nanoenabled agriculture. The regional differences and temperature changes make nanoenabled agriculture complicated. In the future, the temperature increase may reduce the oxidative stress in African bean and European maize induced by NPs. This study projects the development potential of nanoenabled agriculture using machine learning, although many more field studies are needed to address the differences at the country and continental scales.  \nmachine learning | nanoparticle | artificial intelligence | risk | nanoenabled agriculture  \nNanoparticles (NPs) or nanomaterials are incorporated into a wide range of agricultural fields because of their extraordinary nanoscale properties (e.g., high reactivity, mobility, and biocompatibility) (1, 2) . For example, nanotechnology is applied to enhance soil fertility by supplying nutrients (nanofertilizers), controlling weeds and pests (nanoherbicides and nanopesticides), and remediating and improving soil properties and plant characteristics (3–6) . Nanotechnology for agricultural applications is undergoing increasing development and application (7), but development is still at an early stage (1) . However, there is substantial uncertainty regarding nanoenabled agriculture, such as the adverse effects of NPs, the cost and the development potential at the global scale (2, 8) . This uncertainty is affected by the types of NPs, plant species, and environmental and climate conditions (3). With the increasing global population and food security issues, it is urgent to develop smart methods to assess the above uncertainty, promote nanoenabled agricultural development and reduce risks to plant growth (9, 10) .  \nPrevious studies focused on experiments that evaluated the effect of NPs on plant growth, toxicity, translocation, and accumulation, and these studies were expensive and ","cbCaiiJLdyoWxTom","https://ap.wps.com/l/cbCaiiJLdyoWxTom","pdf",2765633,12,"English","# Significance\n## Research approach and motivation\n## Model construction and prediction strategy\n## Feature importance and interpretability\n## Regional projections and implications","[{\"question\":\"What dataset and modeling approach were used to project nanoenabled agriculture potential?\",\"answer\":\"The study built an NP-plant database containing 1,174 datasets and used machine learning models, including random forest models, to predict plant response and NP uptake/transport.\"},{\"question\":\"Which factors were identified as driving plant responses to nanoparticles?\",\"answer\":\"Plant responses were driven by total NP exposure dose and duration and by plant age at exposure, as well as NP size and zeta potential.\"},{\"question\":\"How do the projections differ between Africa and Europe?\",\"answer\":\"The model suggests Fe2O3 NP application may inhibit bean growth in Europe due to low night temperatures, while oxidative stress risks are low in Africa due to high night temperatures, making Africa a suitable area for nanoenabled agriculture.\"}]","Development potential of nanoenabled agriculture projected using machine learning | PDF"]