Multimodal Machine Learning Based Crop Recommendation and Yield Prediction Model

作     者：P.S.S.Gopi M.Karthikeyan 

作者机构：Department of Computer and Information ScienceFaculty of ScienceAnnamalai UniversityChidambaram608002India 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第36卷第4期

页      面：313-326页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Agriculture crop recommendation yield prediction machine learning artificial intelligence 

摘      要：Agriculture plays a vital role in the Indian *** recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic *** the same time,crop yield prediction was based on several features like area,irrigation type,temperature,*** recent advancements of artificial intelligence(AI)and machine learning(ML)models pave the way to design effective crop recommendation and crop pre-diction *** this view,this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction(MMML-CRYP)*** proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop *** the initial stage,equilibrium optimizer(EO)with kernel extreme learning machine(KELM)technique is employed for effectual recommendation of ***,random forest(RF)tech-nique was executed for predicting the crop yield *** reporting the improved performance of the MMML-CRYP system,a wide range of simulations were carried out and the results are investigated using benchmark ***-mentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.