维普期刊资源整合服务平台（V6.5）

维普中文期刊产品整合服务

1篇 您的检索式：作者名="Ravuri Daniel"

选中清除导出查看参考文献查看引证文献引用追踪共1页首页上一页第1页下一页末页 /1 跳转

		题名	作者	年代	出处	被引量
	1	Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops显示文摘Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged,and due to the pest attacks,the quality is degraded.They are the major reason behind crop quality degradation and diminished crop productivity.Hence,accurate pest detection is essential to guarantee safety and crop quality.Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features.Lately,some progress has been made in agriculture by employing machine learning(ML)to classify and detect pests.This study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops(MMTL-IPCAC)technique.The presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization(CLAHE)approach for image enhancement.The neural architectural search network(NASNet)model is applied for feature extraction,and a modified grey wolf optimization(MGWO)algorithm is employed for the hyperparameter tuning process,showing the novelty of the work.At last,the extreme gradient boosting(XGBoost)model is utilized to carry out the insect classification procedure.The simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.	Saud Yonbawi Sultan Alahmari T.Satyanarayana murthy Ravuri Daniel E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa	2023	Computer Systems Science & Engineering2023，46，9：	0

返回顶部每页显示：

共1页首页上一页第1页下一页末页 /1 跳转

goolge学术搜索-维普

网站首页 | 关于我们 | 联系我们 | 产品服务 | 客服中心 | 广告服务 | 版权声明 | 网站联盟 | 友情链接 | 售卡网点

版权所有© 渝B2-20050021-1　渝公网安备 50019002500403号　违法和不良信息举报中心

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费