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<p>Agriculture acts important task at each nation’s economy through creating crops. CYP is procedure of computing amount harvested from given agricultural area. Different methods are discussed for forecasting crop yield. However, the major challenging issues faced by existing predicting techniques are accuracy levels and time complexity. To overcome these issues, Mean Scaling Multi-colinearity Weighted Decay Regression (MSMWDR) is developed for accurate crop yield forecast through enhanced accuracy as well as minimum time complexity. MSMWDR method comprises three main processes. Initially in MSMWDR Method, numbers of information are gathered as of database in data acquisition phase. After that, input data gets pre-processed with help of Mean Scaling Normalization Pre-processing Process. When the dataset has missing values, weighted average method is used for identifying the missing value mean and the mean scaling normalization is carried out to carry out efficient pre-processing task. After data pre-processing, Levenberg–Marquardt Multi-colinearity Weighted Decay Regression method is employed for Feature Selection to choose relevant aspects as of input database. Through chosen aspects, accurate crop yield prediction is performed. The proposed MSMWDR method obtains 4% by crop yield prediction accuracy and4% by precision ,10% by crop yield prediction time and 34% by space complexity, recall by 6%. Results show MSMWDR method attains better performance results than the conventional methods in terms of different parameters.</p>

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Collinearity-based feature selection for improved crop yield prediction

  • 期刊论文

  • 发表日期：

    2025年10月15日

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  • C. Karkuzhali,
  • R. Padmapriya

摘要

Agriculture acts important task at each nation’s economy through creating crops. CYP is procedure of computing amount harvested from given agricultural area. Different methods are discussed for forecasting crop yield. However, the major challenging issues faced by existing predicting techniques are accuracy levels and time complexity. To overcome these issues, Mean Scaling Multi-colinearity Weighted Decay Regression (MSMWDR) is developed for accurate crop yield forecast through enhanced accuracy as well as minimum time complexity. MSMWDR method comprises three main processes. Initially in MSMWDR Method, numbers of information are gathered as of database in data acquisition phase. After that, input data gets pre-processed with help of Mean Scaling Normalization Pre-processing Process. When the dataset has missing values, weighted average method is used for identifying the missing value mean and the mean scaling normalization is carried out to carry out efficient pre-processing task. After data pre-processing, Levenberg–Marquardt Multi-colinearity Weighted Decay Regression method is employed for Feature Selection to choose relevant aspects as of input database. Through chosen aspects, accurate crop yield prediction is performed. The proposed MSMWDR method obtains 4% by crop yield prediction accuracy and4% by precision ,10% by crop yield prediction time and 34% by space complexity, recall by 6%. Results show MSMWDR method attains better performance results than the conventional methods in terms of different parameters.