ArticlePeerJ. Computer science2022
Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia.
Article in PeerJ. Computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Enhancing crop yield prediction accuracy with a novel interpretable deep learning model: MHCNN-LSTM-MHA.Scientific reports · 2026Article
- AI-enabled Barilai-Borwein-Blinder-Oaxaca-Bernoulli Deep Classifier for Enhanced Crop Yield Prediction.Scientific reports · 2025Article
- Revolutionizing agriculture: A comprehensive review on artificial intelligence applications in enhancing properties of agricultural produce.Food chemistry: X · 2025Review
- Pre-harvest mango yield prediction using artificial neural networks based on leaf nutrient variability.PeerJ · 2025Article
- Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI.Scientific reports · 2024Article
- Article
- Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis.Foods (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Predicting crop yields is a critical issue in agricultural production optimization and intensification research. Accurate foresights of natural circumstances a year in advance can have a considerable impact on management decisions regarding crop selection, rotational location in crop rotations, agrotechnical methods employed, and long-term land use planning. One of the most important aspects of precision farming is sustainability. The novelty of this study is to evidence the effective of the temperature, pesticides, and rainfall environment parameters in the influence sustainable agriculture and economic efficiency at the farm level in Saudi Arabia. Furthermore, predicting the future values of main crop yield in Saudi Arabia. The use of artificial intelligence (AI) to estimate the impact of environment factors and agrotechnical parameters on agricultural crop yields and to anticipate yields is examined in this study. Using artificial neural networks (ANNs), a highly effective multilayer perceptron (MLP) model was built to accurately predict the crop yield, temperature, insecticides, and rainfall based on environmental data. The dataset is collected from different Saudi Arabia regions from 1994 to 2016, including the temperature, insecticides, rainfall, and crop yields for potatoes, rice, sorghum, and wheat. For this study, we relied on five different statistical evaluation metrics: the mean square error (MSE), the root-mean-square error (RMSE), normalized root mean square error (NRMSE), Pearson's correlation coefficient (
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