article · Journal of Ecological Engineering
Accurately predicting agricultural yields in Morocco is vital for national food security and effective resource management. An assessment of machine learning algorithms shows they offer greater accuracy than conventional statistical models when forecasting harvest outcomes. By incorporating critical environmental factors such as rainfall, soil moisture levels, and weather patterns, models including Decision Trees, Random Forests, and Artificial Neural Networks were tested against standard statistical approaches. Across evaluations, machine learning models demonstrated superior performance, achieving mean squared error values between 0.10 and 0.23 and coefficients of determination from 0.78 to 0.90, outperforming statistical models that scored between 0.16 to 0.24 and 0.76 to 0.84 respectively. A Feed Forward Artificial Neural Network delivered the highest accuracy, recording an R² value of 0.90, confirming the value of computational modelling for agricultural forecasting.
Agriculture is a cornerstone of Morocco's economy and food security. Improved crop yield predictions give farmers, policymakers, and agricultural stakeholders more reliable data to guide critical decisions on resource allocation. Adopting advanced forecasting methods helps mitigate uncertainty in farming outcomes and strengthens strategic planning for national food supply stability.
This research could support the development of yield forecasting tools and decision-support software for agricultural planners, farmers, and government agencies. Operating at an applied testing stage, the work demonstrates the capability of neural networks using environmental metrics. Transitioning to real-world use would require packaging these models into accessible software interfaces or integrating them into existing farm management systems.
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In Morocco, agriculture is an important sector that contributes to the country's economy and food security. Accurately predicting crop yields is crucial for farmers, policy makers, and other stakeholders to make informed decisions regarding resource allocation and food security. This paper investigates the potential of Machine Learning algorithms for improving the accuracy of crop yield predictions in Morocco. The study examines various factors that affect crop yields, including weather patterns, soil moisture levels, and rainfall, and how these factors can be incorporated into Machine Learning models. The performance of different algorithms, including Decision Trees, Random Forests, and Neural Networks, is evaluated and compared to traditional statistical models used for crop prediction. The study demonstrated that the Machine Learning algorithms outperformed the Statistical models in predicting crop yields. Specifically, the Machine Learning algorithms achieved mean squared error values between 0.10 and 0.23 and coefficient of determination values ranging from 0.78 to 0.90, while the Statistical models had mean squared error values ranging from 0.16 to 0.24 and coefficient of determination values ranging from 0.76 to 0.84. The Feed Forward Artificial Neural Network algorithm had the lowest mean squared error value (0.10) and the highest R² value (0.90), indicating that it performed the best among the three Machine Learning algorithms. These results suggest that Machine Learning algorithms can significantly improve the accuracy of crop yield predictions in Morocco, potentially leading to improved food security and optimized resource allocation for farmers.
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DOI: 10.12911/22998993/162769
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