首页|New Support Vector Machines Findings Has Been Reported by Investigators at Jilin Agricultural University [Detection and Classification of Pesticide Residues In Dandelion (Taraxacum Officinale L.) By Electronic Nose Combined With Chemometric ...]
New Support Vector Machines Findings Has Been Reported by Investigators at Jilin Agricultural University [Detection and Classification of Pesticide Residues In Dandelion (Taraxacum Officinale L.) By Electronic Nose Combined With Chemometric ...]
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NETL
NSTL
Chinese Acad Agricultural Engineering
A new study on Machine Learning - Support Vector Machines is now available. According to news reporting out of Changchun, People’s Republic of China, by NewsRx editors, research stated, “In this study, for the first time establish a suitable pesticide residue detection system for dandelion (Taraxacum officinale L.) based on electronic nose to determine and study the concentration of pesticide residue in dandelion. Dandelions were sprayed with different concentrations of pesticides (avermectin, trichlorfon, deltamethrin, and acetamiprid), respectively.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Science-Technology Development Plan Project of Jilin Province, The 13th Five-Year Plan Scientific Research Foundation of the Education Department of Jilin Province. Our news journalists obtained a quote from the research from Jilin Agricultural University, “Data collection was performed by application of an electronic nose equipped with 12 metal oxide semiconductor (MOS) sensors. Data analysis was conducted using different methods including BP neural network and random forest (RF) as well as the support vector machine (SVM). The results showed the superior effectiveness of SVM in discrimination and classification of non-exceeding maximum residue limits (MRLs) and exceeding MRLs standards. Moreover, the model trained by SVM has the best performance for the classification of pesticide categories in dandelion, and the classification accuracy was 91.7%.”
ChangchunPeople’s Republic of ChinaAsiaAgrochemicalsChemometricEmerging TechnologiesMachine LearningPesticidesSupport Vector MachinesJilin Agricultural University
NETL
NSTL
Chinese Acad Agricultural Engineering
