Abstract: Machine learning techniques in smart agriculture for yield prediction involve using algorithms to analyze historical and real-time data to forecast crop yields. These approaches aim to optimize agricultural practices, improve resource efficiency and enhance productivity, this paper reviews the application of machine learning techniques in smart agriculture for predicting agricultural yields. With the advent of data-driven technologies, machine learning algorithms have become instrumental in analyzing vast amounts of agricultural data to forecast crop yields accurately. Various machine learning models such as regression, classification, and ensemble methods have been employed to process historical and real-time data on weather patterns, soil conditions, crop types, and farming practices. These models enable farmers and stakeholders to make informed decisions, optimize resource allocation, and mitigate risks associated with agricultural production. Furthermore, the integration of Internet of Things devices and remote sensing technologies has facilitated data collection and improved the precision of yield predictions, this paper discusses the key machine learning approaches, challenges, and future directions in leveraging data analytics for enhancing agricultural productivity and sustainability in smart farming systems. to ensure stability and tracking. Simulations is carried out to verify the theoretical results, The study found that different machine learning techniques had varying accuracy for predicting agricultural yields. ViT-B16 achieved the highest F1-SCORE (99.40%), followed by ResNet-50 (99.54%) and CNN (97.70%), while RPN algorithms had lower accuracy (91.83%). Correlation analysis showed a strong positive relationship between humidity and soil moisture, favoring crop growth, while production had minimal correlation with temperature and area. The AdaBoost Regressor was the best performer, with the lowest MAE (0.22), MSE (0.1), and RMSE (0.31), and Random Forest showed strong predictive power with an R2 score of 0.89, Seasonal data indicated that autumn had the highest agricultural production, followed by spring, while summer and winter had much lower yields due to weather conditions. Seasonal temperature variations from 1997 to 2014 showed autumn was the warmest (34.43°C), boosting crop production, and winter the coldest (34.31°C), reducing yields. These temperature shifts significantly impacted agricultural productivity, with warm seasons enhancing growth and extreme temperatures in summer and winter limiting it, machine learning techniques in smart agriculture are pivotal for predicting crop yields by leveraging historical and real-time data, thus optimizing practices and resource use while boosting productivity. This involves deploying diverse machine learning models like regression, classification, and ensembles to analyze extensive data on weather, soil, crops, and farming methods. Such models empower stakeholders with insights for informed decisions, efficient resource allocation, and risk mitigation in agricultural operations. The integration of Internet of Things and remote sensing further refines data accuracy, aiding precise yield predictions. Despite advancements, challenges persist, including data quality assurance, model complexity, scalability, and interoperability, driving ongoing research and simulations to validate and improve ML applications for sustainable and productive smart farming systems.

Abourabia. Imade, Ounacer. Soumaya, Elghoumari. Mohammed yassine and Azzouazi. Mohamed, “Machine Learning Approaches Applied in Smart Agriculture for the Prediction of Agricultural Yields” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151085

@article{Imade2024,
title = {Machine Learning Approaches Applied in Smart Agriculture for the Prediction of Agricultural Yields},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151085},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151085},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Abourabia. Imade and Ounacer. Soumaya and Elghoumari. Mohammed yassine and Azzouazi. Mohamed}
}