Weather-based maize yield forecast in Saudi Arabia using statistical analysis and machine learning

Jayagopal, Prabhu; Muthukumaran, V.; Koti, Manjula Sanjay; Kumar, S. Satheesh; Rajendran, Sukumar; Mathivanan, Sandeep Kumar

doi:10.1007/s11600-022-00854-z

Cited by 4 publications

(1 citation statement)

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“…This would allow them to improve existing agricultural and irrigation systems and assist in better decision-making in exporting and importing food products. Saudi Arabia produced approximately 89 million metric tons of maize in 2019, increasing from 84 million tons in previous years [2]. It is worth noting that the demand for food production in the Kingdom of Saudi Arabia (KSA) is changing, although expanding.…”

Section: Introductionmentioning

confidence: 99%

A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron

Ahmed

2023

Sustainability

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Predicting crop yields is one of agriculture’s most challenging issues. It is crucial in making national, provincial, and regional choices and estimates the government to meet the food demands of its citizens. Crop production is anticipated based on various factors such as soil conditions and meteorological, environmental, and crop variables. This study intends to develop an effective model that can accurately anticipate agricultural production in advance, assisting farmers in better planning. In the current study, the Crop Yield Prediction Dataset is normalized initially, and then feature engineering is performed to determine the significance of the feature in assessing the crop yield. Crop yield forecasting is performed using the Multi-Layer Perceptron model and the Spider Monkey Optimization method. The Multi-Layer Perceptron technique is efficient in dealing with the non-linear relations among the features in the data, and the Spider Monkey Optimization technique would assist in optimizing the corresponding feature weights. The current study uses data from the Food and Agriculture Organization and the World Data Bank to forecast maize yield in the Saudi Arabia region based on factors such as average temperature, average rainfall, and Hg/Ha production in past years. The suggested MLP-SMO model’s prediction effectiveness is being evaluated using several evaluation metrics such as Root-Mean-Square Error, R-Squared, Mean Absolute Error, and Mean Bias Error, where the model has outperformed in the prediction process with a Root-Mean-Square Error value of 0.11, which is lowest among all the techniques that are considered in the statical analysis in the current study.

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Section: Introductionmentioning

confidence: 99%