基于人工智能的小麦高效育种信息交互系统构建  

Construction of an efficient wheat breeding information exchange system based on artificial intelligence

作　　者：杨民安 孙雨 王凤超 杨晶 陈进 YANG Min'an;SUN Yu;WANG Fengchao;YANG Jing;CHEN Jin(College of Sciences,Shanghai Institute of Technology,Shanghai 201418,China)

机构地区：[1]上海应用技术大学理学院,上海201418

出　　处：《农业工程学报》2024年第13期117-123,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：上海市青年科技英才扬帆计划项目(20YF1447600);上海应用技术大学引进人才基金项目(YJ2021-60);上海应用技术大学协同创新基金项目(XTCX2023-22);上海应用技术大学中青年科技人才发展基金项目(ZQ2022-6)。

摘　　要：小麦是人类社会重要的粮食资源之一,因此基于人工智能技术构建高效育种信息交互平台对于高质高产的小麦种植具有重要的战略价值。高效育种信息交互平台的搭建关键在于核心数据的准确识别与分类,基于此该研究提出了一种Naive Bayes(朴素贝叶斯)-AdaBoost策略,应用于小麦育种信息数据的分类与识别,并实现构筑交互平台。在该策略中AdaBoost主要用于对Naive Bayes的弱分类器进行迭代,形成强分类器,同时过滤并优化核心词汇,达到提高分类识别准确度的目的。结果显示,与传统Naive Bayes方法相比该方法准确率提高了12.2个百分点,识别的准确率达到99.2%,而此时基于Naive Bayes、决策树、支持向量机3种方法的准确率分别为87.0%、86.6%和85.6%。结果表明,该研究所提方法在面对复杂数据分类识别的场景中具有较大的应用潜力。High-yield and superior-quality wheat varieties can greatly contribute to the escalating demand for food security.Artificial intelligence(AI)can be integrated into efficient breeding programs.AI-driven information exchange platforms can also facilitate the collection,analysis,and dissemination of breeding data in the genetic improvement of breeding.However,such platforms have hinged on the accurate identification and classification of core data.There are often some challenges,due to the complex and diverse nature of breeding information.In this study,a Naive Bayes-AdaBoost strategy was introduced to construct the wheat breeding interaction system.Naive Bayes,a probabilistic machine learning served as the foundation.The AdaBoost was then integrated to enhance the classification performance of the datasets.AdaBoost functions as a meta-learning iteratively boosted the performance of weak classifiers(in this case,Naive Bayes),where the misclassified data points were focused.As such,the strong classifier significantly improved the accuracy.Additionally,the vocabulary optimization was incorporated.The core vocabulary was then filtered and refined for the data classification.The noise and irrelevant information were reduced to further enhance the recognition accuracy.The efficacy of the Naive Bayes-AdaBoost strategy was evaluated to compare with traditional machine learning,including standalone Naive Bayes,decision tree,and support vector machine.The results demonstrated that the superior performance of the strategy was achieved in a remarkable 99.2%accuracy,in order to classify and recognize the wheat breeding information.There was a substantial 12.2 percentage point improvement over the conventional Naive Bayes.The highly accurate data classification and recognition were realized to construct efficient and reliable wheat breeding information platforms.The valuable tools were potential benefit to the data-driven decision-making in the development of superior wheat varieties for global food security.Meanwhile,a real-wor

关 键 词：小麦 交互平台 Naive Bayes ADABOOST 育种交互 核心词汇 

分 类 号：S126[农业科学—农业基础科学]