Machine Learning Algorithms and Their Effects on Crop Production in Agriculture: A Review

Agricultural production faces challenges, such as water scarcity, climate change, and the rising demand for food to feed the growing global population. Incorporating wireless sensor networks (WSNs), Unmanned Aerial Vehicles (UAVs), and Machine Learning (ML) enables an efficient Synergistic approach for crop monitoring, predicting, and managing. WSNs prefer environmental data instantaneously, UAVs provide data collection for large-scale aerial, and ML processes this data to make actionable decisions. The primary objective of this review is to explore the comprehensive effect of WSNs, UAVs, and ML in the agriculture sector, emphasizing crop yield, environmental sustainability, and optimizing resources. In addition, this review aims to detail the benefits and limitations of ML technology and its impact on farming. Integrating state-of-the-art technologies has expressed several key areas of significant potential, such as crop health monitoring, precision irrigation, yield prediction, disease and pest detection, and resource efficiency. Hence, the collaboration of recent technologies in modern farming significantly enhances monitoring and management in real-time, improves productivity and sustainability, and addresses global food security challenges. At the same time, this would shape agriculture's future through innovative promotion and more sustainable farming practices.