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

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.

Highly sensitive Borophene-metal-Si based multilayered Terahertz frequency spectrum based refractive index sensor

“…The adjusted parameters determine how well the actual outcome matches the original specification. Following this calculation, the node may utilise the result of equation (9) [44] to evaluate the predicted output of the MLP network for, O .…”

“…The S h notation represents the neuron's output. The whole result may be calculated using equation (10) [44,45]. Based on the estimated loss, which is determined using equation (11), the network modifies the input neuron weights, the hidden node weights in the output layer, and the predicted value ( ) y .…”

Optimizing ensembles machine learning, genetic algorithms, and multivariate modeling for enhanced prediction of maize yield and stress tolerance index

“…Comparative metrics for assessing model robustness in grain yield prediction in terms of R 2 , MAE, RMSE and n-RMSE are shown in Figure 5. Ensemble algorithms demonstrated superior performance in crop yield prediction (Ahmed, 2023), while RF is the optimized algorithm for accurately forecasting maize yields at the county level through the integration of diverse data sources (Pham and Olafsson, 2018). Aqil et al (2022) reported that incorporating ensemble machine learning may enhance the accuracy of classifying maize plants.…”

Crop Yield Estimation Using Sentinel-3 SLSTR, Soil Data, and Topographic Features Combined with Machine Learning Modeling: A Case Study of Nepal

“…Regardless, our established model yielded more accurate predictions compared to their findings. Furthermore, recent research conducted by Ahmed [120] to predict maize yield in the Saudi Arabia region based on weather data has further proven the potential of XGBoost in enhancing estimation accuracy compared to GBM. The study noted a decrease in RMSE value by 0.01 t/ha, indicating the improved performance of XGBoost in accurately predicting crop yield.…”