On one hand, the emergence of cutting-edge technologies like AI, Cloud Computing, and IoT holds immense potential in Smart Farming and Precision Agriculture. These technologies enable real-time data collection, including highresolution crop imagery, using Unmanned Aerial Vehicles (UAVs). Leveraging these advancements can revolutionize agriculture by facilitating faster decision-making, cost reduction, and increased yields. Such progress aligns with precision agriculture principles, optimizing practices for the right locations, times, and quantities. On the other hand, integrating UAVs in Smart Farming faces obstacles related to technology selection and deployment, particularly in data acquisition and image processing. The relative novelty of UAV utilization in Precision Agriculture contributes to the lack of standardized workflows. Consequently, the widespread adoption and implementation of UAV technologies in farming practices are hindered. This paper addresses these challenges by conducting a comprehensive review of recent UAV applications in Precision Agriculture. It explores common applications, UAV types, data acquisition techniques, and image processing methods to provide a clear understanding of each technology's advantages and limitations. By gaining insights into the advantages and challenges associated with UAV-based applications in Precision Agriculture, this study aims to contribute to the development of standardized workflows and improve the adoption of UAV technologies.
Regression analysis is a powerful statistical method that support to inspect the relationship between two or more variables of interest. While there are many types of regression analysis, at their core they all examine the impact of one or more independent variables on a dependent variable. It is one of the most commonly used methods in many scientific fields. Satisfying the assumptions such as collinearity between variables ought to be a significant issue in data science. Advanced level tools such as Linear, Lasso, Ridge and ElasticNet regression are methods designed to overcoming a problem of overfitting a model. This study discusses comparing regression and regularization algorithms. It also deals with how the concept of model complexity unfolds for each of these models and provides an overview of how each algorithm builds a model. Moreover, it examines the strengths and weaknesses of each algorithm, as well as the type of data to which they can best be applied to irrigation water use efficiency under climate change. Finally, this work aims also to explain the meaning of the most important regularization criteria. It remains to say that the main contributions of this study are (1) Comparing linear and multilinear regression methods: case of climate change dataset using regression metrics (2) comparing regularization methods: Ridge, Lasso and ElasticNet.
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