Treffer: Harnessing artificial intelligence for effective crop yield forecasting model.

A country's economy greatly benefits from agriculture, but environmental control methods have left numerous fields unexplored. Preventing this problem can be achieved by increasing productivity with Artificial Intelligence (AI)-based plant yield forecasting tools. Thus, for efficient agricultural yield forecasting, this paper provides a novel war strategy optimization-enabled cubic support vector machine (WSO-CSVM) approach. The WSO technique is used to improve the SVM classifier's prediction performance which is based on cubic kernels. The performance of the suggested WSO-CSVM technique is assessed using a public dataset. Pre-processing of the data is done using the min-max normalization technique. The important components are extracted from the normalized data using principal component analysis (PCA). Using the suggested approach, we forecast the crop yield based on the extracted data. This WSO-CSVM-based experiment is implemented using the Python platform, and the suggested approach is examined using several metrics such as f1-score (0.91), recall (0.95), accuracy (0.989), and precision (0.98). Our suggested approach outperformed other approaches in terms of crop yield prediction accuracy. [ABSTRACT FROM AUTHOR]

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