World Journal of Engineering Research and Technology (WJERT) has indexed with various reputed international bodies like : Google Scholar , Index Copernicus , Indian Science Publications , SOCOLAR, China , International Institute of Organized Research (I2OR) , Cosmos Impact Factor , Research Bible, Fuchu, Tokyo. JAPAN , Scientific Indexing Services (SIS) , Jour Informatics (Under Process) , UDLedge Science Citation Index , International Impact Factor Services , International Scientific Indexing, UAE , International Society for Research Activity (ISRA) Journal Impact Factor (JIF) , International Innovative Journal Impact Factor (IIJIF) , Science Library Index, Dubai, United Arab Emirates , Scientific Journal Impact Factor (SJIF) , Science Library Index, Dubai, United Arab Emirates , Eurasian Scientific Journal Index (ESJI) , Global Impact Factor (0.342) , IFSIJ Measure of Journal Quality , Web of Science Group (Under Process) , Directory of Research Journals Indexing , Scholar Article Journal Index (SAJI) , International Scientific Indexing ( ISI ) , Scope Database , 

World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

News & Updation

  • Article Invited for Publication

    Article are invited for publication in WJERT Coming Issue

  • ICV

    WJERT Rank with Index Copernicus Value 79.45 due to high reputation at International Level

  • New Issue Published

    Its Our pleasure to inform you that, WJERT March 2023 Issue has been Published, Kindly check it on

  • WJERT New Impact Factor

    Its our Pleasure to Inform you that WJERT Impact Factor has been increased from  5.549 to 5.924 due to high quality Publication at International Level


    MARCH 2023 Issue has been successfully launched on 1 March 2023.




Dr. M. Saranya*, Dr. S. Sathappan


Our recent study using crop yield prediction associated with climate, weather and soil for crop yield prediction. By combining regression models with Neural Networks (NN), can able to release highly satisfactory forecasting of crop yield. Prediction of crop yield accurately for tracking crop production is a trendy issue and it is a main area of research for agriculture studies. Multi-Model Ensemble Modified Depth Adaptive Deep Neural Network (MME-MDADNN) was an effective crop yield prediction method where the variation of climate, weather and soil parameters were learned through DNN. The existing Support Vector Regression (SVR) model with Deep Neural Network, the slow convergence speed, possibility of stuck in local minima and risk of over-fitting problems are resolved. Here the Ridge Regression (RR) model was applied to analyze multicollinearity in multiple regression data which enrich the better solution. Hence by applying ridge regression along with DNN, the crop yield prediction Accuracy is improved. The effectiveness of the proposed MME-MDADNN is tested in terms of Accuracy, Precision, Recall and F-measure.

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