Artificial Intelligence Techniques in Crop Yield Estimation Based on Sentinel-2 Data: A Comprehensive Survey

Sustainability, Journal Year: 2024, Volume and Issue: 16(18), P. 8277 - 8277

Published: Sept. 23, 2024

This review explores the integration of Artificial Intelligence (AI) with Sentinel-2 satellite data in context precision agriculture, specifically for crop yield estimation. The rapid advancements remote sensing technology, particularly through Sentinel-2’s high-resolution multispectral imagery, have transformed agricultural monitoring by providing critical on plant health, soil moisture, and growth patterns. By leveraging Vegetation Indices (VIs) derived from these images, AI algorithms, including Machine Learning (ML) Deep (DL) models, can now predict yields high accuracy. paper reviews studies past five years that utilize techniques to estimate crops like wheat, maize, rice, others. Various approaches are discussed, Random Forests, Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), ensemble methods, all contributing refined forecasts. identifies a notable gap standardization methodologies, researchers using different VIs similar crops, leading varied results. As such, this study emphasizes need comprehensive comparisons more consistent methodologies future research. work underscores significant role advancing offering valuable insights aim enhance sustainability efficiency management advanced predictive models.

Language: Английский

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An Over-Actuated Hexacopter Tilt-Rotor UAV Prototype for Agriculture of Precision: Modeling and Control

Sensors, Journal Year: 2025, Volume and Issue: 25(2), P. 479 - 479

Published: Jan. 15, 2025

This paper focuses on the modeling, control, and simulation of an over-actuated hexacopter tilt-rotor (HTR). configuration implies that two six actuators are independently tilted using servomotors, which provide high maneuverability reliability. approach is predicted to maintain zero pitch throughout trajectory expected improve aircraft's steering accuracy. arrangement particularly beneficial for precision agriculture (PA) applications where accurate monitoring management crops critical. The enhanced allows precise navigation in complex vineyard environments, enabling unmanned aerial vehicle (UAV) perform tasks such as imaging crop health monitoring. employed control architecture consists cascaded proportional (P)-proportional, integral derivative (PID) controllers successive loop closure (SLC) method five controlled degrees freedom (DoFs). Simulated results Gazebo demonstrate HTR achieves stability flight path, significantly improving practices. Furthermore, a comparison with traditional validates proposed approach.

Language: Английский

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SugarViT—Multi-objective regression of UAV images with Vision Transformers and Deep Label Distribution Learning demonstrated on disease severity prediction in sugar beet

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0318097 - e0318097

Published: Feb. 13, 2025

Remote sensing and artificial intelligence are pivotal technologies of precision agriculture nowadays. The efficient retrieval large-scale field imagery combined with machine learning techniques shows success in various tasks like phenotyping, weeding, cropping, disease control. This work will introduce a framework for automatized plant-specific trait annotation the use case severity scoring CLS sugar beet. With concepts DLDL, special loss functions, tailored model architecture, we develop an Vision Transformer based called SugarViT. One novelty this is combination remote data environmental parameters experimental sites prediction. Although evaluated on case, it held as generic possible to also be applicable image-based classification regression tasks. our framework, even learn models multi-objective problems, show by pretraining metadata. Furthermore, perform several comparison experiments state-of-the-art methods constitute modeling preprocessing choices.

Language: Английский

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HVIOnet: A deep learning based hybrid visual–inertial odometry approach for unmanned aerial system position estimation

Neural Networks, Journal Year: 2022, Volume and Issue: 155, P. 461 - 474

Published: Sept. 7, 2022

Language: Английский

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A Survey on UAV Computing Platforms: A Hardware Reliability Perspective

Sensors, Journal Year: 2022, Volume and Issue: 22(16), P. 6286 - 6286

Published: Aug. 21, 2022

This study describes the Computing Platforms (CPs) and hardware reliability issues of Unmanned Aerial Vehicles (UAVs), or drones, which recently attracted significant attention in mission safety-critical applications demanding a failure-free operation. While rapid development UAV technologies was reviewed by survey reports focusing on architecture, cost, energy efficiency, communication, civil application aspects, computing platforms’ perspective overlooked. Moreover, due to rising complexity diversity today’s CPs, their is becoming prominent issue up-to-date solutions tailored specifics. The objective this work address gap, aspect. research studies CPs deployed for representative applications, specific fault failure modes, existing approaches assessment enhancement indicates how faults failures occur various system layers UAVs analyzes open challenges. We advocate concept cross-layer model UAVs’ onboard intelligence identify directions future area.

Language: Английский

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