Table 1 Summary of the related work... | Emerald Publishing

Table 1

Summary of the related work

Topic	Descriptions
Crop yield prediction using neural network models	Environmental factors considered include temperature, rainfall, soil type, tillage systems, elevation and irrigation techniques. (Van Klompenburg et al., 2020; Hara et al., 2021) ANN algorithm with one hidden layer is commonly used (Cheng et al., 2022). Deep neural networks (DNNs) have practical applications for food security (Malviya & Solanki, 2022). Other techniques include decision trees (Gupta et al., 2022), association rule mining (Bennett & Harms, 2011), linear regression (Haque et al., 2020), elastic net (Lontsi Saadio et al., 2022), k-nearest neighbor (Charoen-Ung & Mittrapiyanuruk, 2018), support vector regression (Haque et al., 2020), XGBoost and AdaBoost (Sunil et al., 2022)
Crop water requirements prediction using neural network models	Crop water requirements can be estimated using crop yield forecasts (Dalei et al., 2017). Neural network models have been used to predict effective rainfall and water needs (Abishek et al., 2017). NARNNs and GRNNs have been applied for accurate estimation (Ruiz et al., 2016; Ding et al., 2019). Hybrid neural networks have improved accuracy in crop water requirement predictions (Mokhtar et al., 2023; Habeeb et al., 2025; Zhao et al., 2024)
Prediction of crop yield, energy consumption, financial time series and food demand	NARNNs have been employed for agricultural yield prediction (Ruiz et al., 2016; Boussaada et al., 2018), wheat yield prediction in China (Boussaada et al., 2018) and food demand forecasting in India (Paidipati et al., 2021; Rathod et al., 2022). Hybrid classical-quantum multilayer neural networks are emerging for agricultural predictions (Liliopoulos et al., 2025)

Topic	Descriptions
Crop yield prediction using neural network models	Environmental factors considered include temperature, rainfall, soil type, tillage systems, elevation and irrigation techniques. (Van Klompenburg et al., 2020; Hara et al., 2021) ANN algorithm with one hidden layer is commonly used (Cheng et al., 2022). Deep neural networks (DNNs) have practical applications for food security (Malviya & Solanki, 2022). Other techniques include decision trees (Gupta et al., 2022), association rule mining (Bennett & Harms, 2011), linear regression (Haque et al., 2020), elastic net (Lontsi Saadio et al., 2022), k-nearest neighbor (Charoen-Ung & Mittrapiyanuruk, 2018), support vector regression (Haque et al., 2020), XGBoost and AdaBoost (Sunil et al., 2022)
Crop water requirements prediction using neural network models	Crop water requirements can be estimated using crop yield forecasts (Dalei et al., 2017). Neural network models have been used to predict effective rainfall and water needs (Abishek et al., 2017). NARNNs and GRNNs have been applied for accurate estimation (Ruiz et al., 2016; Ding et al., 2019). Hybrid neural networks have improved accuracy in crop water requirement predictions (Mokhtar et al., 2023; Habeeb et al., 2025; Zhao et al., 2024)
Prediction of crop yield, energy consumption, financial time series and food demand	NARNNs have been employed for agricultural yield prediction (Ruiz et al., 2016; Boussaada et al., 2018), wheat yield prediction in China (Boussaada et al., 2018) and food demand forecasting in India (Paidipati et al., 2021; Rathod et al., 2022). Hybrid classical-quantum multilayer neural networks are emerging for agricultural predictions (Liliopoulos et al., 2025)

Source(s): Prepared by the authors