A Review on UAV-Based Applications for Precision Agriculture
TL;DR: The most common applications, the types of UAVs exploited, and the most popular processing methods of aerial imagery are discussed, to discuss the outcomes of each method and the potential applications of each one in the farming operations.
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Abstract: Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.
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Citations
Predictive Autonomy for UAV Remote Sensing: A Survey of Video Prediction
Zhan Chen,En-Ze Zhu,Zile Guo,Peirong Zhang,Xiaoxuan Liu,Lei Wang,Yi-Dan Zhang,Zhan Chen,En-Ze Zhu,Zile Guo,Peirong Zhang,Xiaoxuan Liu,Lei Wang,Yi-Dan Zhang +13 more
Abstract: The analysis of dynamic remote sensing scenes from unmanned aerial vehicles (UAVs) is shifting from reactive processing to proactive, predictive intelligence. Central to this evolution is video prediction—forecasting future imagery from past observations—which enables critical remote sensing applications like persistent environmental monitoring, occlusion-robust object tracking, and infrastructure anomaly detection under challenging aerial conditions. Yet, a systematic review of video prediction models tailored for the unique constraints of aerial remote sensing has been lacking. Existing taxonomies often obscure key design choices, especially for emerging operators like state-space models (SSMs). We address this gap by proposing a unified, multi-dimensional taxonomy with three orthogonal axes: (i) operator architecture; (ii) generative nature; and (iii) training/inference regime. Through this lens, we analyze recent methods, clarifying their trade-offs for deployment on UAV platforms that demand processing of high-resolution, long-horizon video streams under tight resource constraints. Our review assesses the utility of these models for key applications like proactive infrastructure inspection and wildlife tracking. We then identify open problems—from the scarcity of annotated aerial video data to evaluation beyond pixel-level metrics—and chart future directions. We highlight a convergence toward scalable dynamic world models for geospatial intelligence, which leverage physics-informed learning, multimodal fusion, and action-conditioning, powered by efficient operators like SSMs.
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References
•Journal Article
The European commission
TL;DR: This research examines the interaction between demand and socioeconomic attributes through Mixed Logit models and the state of art in the field of automatic transport systems in the CityMobil project.
4.8K
‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications
TL;DR: The Structure-from-Motion (SfM) method as mentioned in this paper solves the camera pose and scene geometry simultaneously and automatically, using a highly redundant bundle adjustment based on matching features in multiple overlapping, offset images.
3.8K
Deep learning in agriculture: A survey
TL;DR: A survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges indicates that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.
3.3K
Machine Learning in Agriculture: A Review.
TL;DR: A comprehensive review of research dedicated to applications of machine learning in agricultural production systems is presented, demonstrating how agriculture will benefit from machine learning technologies.
2.2K
Significant remote sensing vegetation indices: A review of developments and applications
Jinru Xue,Baofeng Su +1 more
TL;DR: The spectral characteristics of vegetation are introduced and the development of VIs are summarized, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision.