[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124496-en":3,"doc-seo-124496-105":30,"detail-sidebar-cat-0-en-105":90},{"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},124496,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Classification of Plant Media Based on NPK Content Using Machine Learning","The references compiled for this work focus on classifying plant-related conditions and nutrient status using machine learning, particularly artificial neural networks and related models. Multiple studies address NPK and soil nutrient classification, while others extend the same analytical approach to precision agriculture tasks such as stress phenotyping, soil property categorization, and agricultural recommendation systems. Several contributions discuss prediction accuracy improvements through optimized model parameters and integrated sensing or mechatronic measurement setups, supporting a data-driven methodology for agricultural decision-making and plant health assessment.","REFERENCES  \n[1]  M.S.Munir,“Klasifikasi kekurangan unsur hara N,P,K tanaman kedelai berdasarkan fiturdaun menggunakan jaringan syaraf tiruan,\"Inst.Teknol.Sepuluh Novemb.,pp.1-86,2016.https://repository.its.ac.id/41919/1/2210205209-Master-Thesis.pdf  \n[2]  Z.Al-Fa'izah,Y..Rahayu,and N.Hikmah,“Klasifikasi Kondisi Tanah BerdasarkanRekomendasi Tanaman Pertanian Dan Perkebunan Melalui Penggunaan Jaringan SyarafTiruan Klasifikasi Multinomial,\"Univ.Negeri Jember,vol.3,no.3,pp.69-70,2018.https://repository.unej.ac.id/handle/123456789/115541  \n[3]  S.M.Shafaei,A.Nourmohamadi-Moghadami,S.Kamgar,and M.Eghtesad,  \n\"Development and validation of an integrated mechatronic apparatus for measurement offriction coefficients of agricultural products,\"Inf.Process.Agric.,vol.7,no.1,pp.93-108,2020,doi:10.1016/j.inpa.2019.04.006.  \nhttps://www.sciencedirect.com/science/article/pii/S2214317318304694  \n[4]  M.S.Suchithra and M.L.Pai,\"Improving the prediction accuracy of soil nutrientclassification by optimizing extreme learning machine parameters,\"Inf.Process.Agric.,vol.7,no.1,pp.72-82,2020,doi:10.1016/j.inpa.2019.05.003.https://www.sciencedirect.com/science/article/pii/S2214317318304906  \n[5]  A.Singh,B.Ganapathysubramanian,A.K.Singh,and S.Sarkar,\"Machine Learning forHigh-Throughput Stress Phenotyping in Plants,\"Trends Plant Sci.,vol.21,no.2,pp.110-124,2016,doi:10.1016/j.tplants.2015.10.015.https://www.cell.com/trends/plant-science/fulltext/S1360-1385(15)00263-0  \n[6]  A.Sharma,A.Jain,P.Gupta,and V.Chowdary,\"Machine Learning Applications forPrecision Agriculture:A Comprehensive Review,”IEEE Access,vol.9,pp.4843-4873,2021,doi:10.1109/ACCESS.2020.3048415.  \nhttps://ieeexplore.ieee.org/abstract/document/9311735  \n[7]  A.Jakaria,S.Mu'minah,D.Riana,and S.Hadianti,“Klasifikasi Varietas Buah Kiwidengan Metode Convolutional Neural Networks Menggunakan Keras,”J.Media Inform.Budidarma,vol.5,no.4,p.1309,2021,doi:10.30865/mib.v5i4.3166.  \nhttps://pdfs.semanticscholar.org/f85f/77b68b804a2bdba7c5e222c3321c435f32fl.pdf[8]  K.Sandamurthy and K.Ramanujam,“A hybrid weed optimized coverage path planningtechnique for autonomous harvesting in cashew orchards,\"Inf.Process.Agric.,vol.7,no.  \n1,pp.152-164,2020,doi:10.1016/j.inpa.2019.04.002.  \nhttps://www.sciencedirect.com/science/article/pii/S2214317318303779  \n[9]  Z.Huang,A.Qin,J.Lu,A.Menon,and J.Gao,“Grape Leaf Disease Detection andClassification Using Machine Learning,\"Proc.-IEEE Congr.Cybermatics 2020 IEEEInt.Conf.Internet Things,iThings 2020,IEEEGreen Comput.Commun.GreenCom 2020,IEEE Cyber,Phys.Soc.Comput.CPSCom 2020 IEEE Smart Data,SmartD,no.January,pp.870-877,2020,doi:10.1109/iThings-GreenCom-CPSCom-SmartData-  \nCybermatics50389.2020.00150.https://www.researchgate net/publication/340221599[10]B.Dey,J.Ferdous,and R.Ahmed,\"Machine learning based recommendation ofagricultural and horticultural crop farming in India under the regime of NPK,soil pH andthree climatic variables,\"Heliyon,vol.10,no.3,p.e25112,2024,doi:  \n10.1016/j.heliyon.2024.e25112.https://www.cell.com/trends/plant-science/fulltext/S1360-1385(15)00263-0  \n[11]M.Borovcnik,H.-J.Bentz,and R.Kapadia,A Probabilistic Perspective.1991.http://dx.doi.org/10.1007/978-94-011-3532-0  \n[12]J.Pacheco,“CSC380:Principles of Data Science Introduction to Machine Learning,”p.2,2003,[Online].Available:  \nhttp://www.pachecoj.com/courses/csc380_fall21/lectures/mlintro.pdf.  \n[13]J.B.O.Mitchell B.O.,\"Machine learning methods in chemoinformatics,\"WileyInterdiscip.Rev.Comput.Mol.Sci.,vol.4,no.5,pp.468-481,2014,doi:  \n10.1002/wcms.1183.https://doi.org/10.3390/molecules25225277","cbCaip7Ju2PmPVXV","https://ap.wps.com/l/cbCaip7Ju2PmPVXV","pdf",749581,1,2,"English","en",105,"# References\n## Plant nutrient and NPK-related classification\n## Soil condition and parameter-based modeling\n## Precision agriculture and plant phenotyping applications\n## Related machine learning foundations","[{\"question\":\"哪些研究主题集中在NPK或土壤养分分类？\",\"answer\":\"多条文献围绕基于N、P、K或土壤养分特征进行分类与预测展开，包括利用人工神经网络对植物养分缺失与土壤状态进行判别。\"},{\"question\":\"文献中常用的机器学习方法有哪些？\",\"answer\":\"参考文献提到人工神经网络、极限学习机参数优化、卷积神经网络（Keras实现）以及用于预测或推荐的机器学习方法。\"},{\"question\":\"这些工作如何提升农业应用的效果？\",\"answer\":\"通过优化模型参数以提高预测精度，并结合测量装置或高通量表型研究，将数据分析用于作物健康评估、胁迫表型识别以及农业种植建议等任务。\"}]","Classification of Plant Media Based on NPK Content Using Machine Learning | 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