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

Author

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  • Shakeel Ahmed

    (Department of Computer Science, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

Abstract

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.

Suggested Citation

  • Shakeel Ahmed, 2023. "A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3017-:d:1060684
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    References listed on IDEAS

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    1. Jacques Maritz & Foster Lubbe & Louis Lagrange, 2018. "A Practical Guide to Gaussian Process Regression for Energy Measurement and Verification within the Bayesian Framework," Energies, MDPI, vol. 11(4), pages 1-12, April.
    2. Rajaram Gana, 2022. "Ridge Regression and the Elastic Net: How Do They Do as Finders of True Regressors and Their Coefficients?," Mathematics, MDPI, vol. 10(17), pages 1-27, August.
    3. Li, Sheng & Wu, Feng & Guan, Zhengfei, 2020. "Machine learning techniques for strawberry yield forecasting," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304502, Agricultural and Applied Economics Association.
    4. El-Sayed M. El-Kenawy & Nima Khodadadi & Seyedali Mirjalili & Tatiana Makarovskikh & Mostafa Abotaleb & Faten Khalid Karim & Hend K. Alkahtani & Abdelaziz A. Abdelhamid & Marwa M. Eid & Takahiko Horiu, 2022. "Metaheuristic Optimization for Improving Weed Detection in Wheat Images Captured by Drones," Mathematics, MDPI, vol. 10(23), pages 1-30, November.
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    Cited by:

    1. Saim Khalid & Hadi Mohsen Oqaibi & Muhammad Aqib & Yaser Hafeez, 2023. "Small Pests Detection in Field Crops Using Deep Learning Object Detection," Sustainability, MDPI, vol. 15(8), pages 1-19, April.

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