Identification of Crop Diseases Based on Improved Genetic Algorithm and Extreme Learning Machine

As an indispensable task in crop protection, the detection of crop diseases directly impacts the income of farmers. To address the problems of low crop-disease identification precision and detection abilities, a new method of detection is proposed based on improved genetic algorithm and extreme learning machine. Taking five different typical diseases with common crops as the objects, this method first preprocesses the images of crops and selects the optimal features for fusion. Then, it builds a model of crop disease identification for extreme learning machine, introduces the hill-climbing algorithm to improve the traditional genetic algorithm, optimizes the initial weights and thresholds of the machine, and acquires the approximately optimal solution. And finally, a data set of crop diseases is used for verification, demonstrating that, compared with several other common machine learning methods, this method can effectively improve the crop-disease identification precision and detection abilities and provide a basis for the identification of other crop diseases.     

国外重大慢性非传染性疾病防治对策研究

重大慢性非传染性疾病包括心脑血管疾病、癌症、呼吸系统疾病、糖尿病等,其流行给全球居民健康造成了重大威胁。为进一步对慢性病进行有效预防控制,促进健康、推动经济和社会发展,2014年中国工程院立项并开展了"重大慢性非传染性疾病防治的国际对比研究"项目,对国内外慢性病预防控制情况以及可供我们借鉴的经验进行了详细分析。本文从慢性病发病概况入手,分析了各种疾病的预防控制措施,并对不同国家的慢性病预防控制体系进行比较,凝练有益经验和方法,提出了增强我国慢性病预防控制的措施和建议。     

卫生经济学应用于慢性病防控决策的战略研究

为提高当前慢性病防控体系的效率,更好地遏制慢性病的流行,保护公众健康,2015年中国工程院设立了"卫生经济学应用于慢性病防控决策的战略研究"重大咨询项目。课题组调研发现,作为一种重要的卫生决策工具,卫生经济学在慢性病防控决策过程中尚处于初级应用阶段。对卫生经济学的重要性认识不足,对卫生经济学的方法的掌握和应用能力有限,制约了其在慢性病防控决策中的应用,因此建议建立多元主体参与的卫生经济学研究力量,加强卫生经济学应用于慢性病防控决策的大数据积累及应用研究,提出将卫生经济学应用于我国慢性病防控决策的战略框架。