Enhancing Multi-Flight Unmanned-Aerial-Vehicle-Based Detection of Wheat Canopy Chlorophyll Content Using Relative Radiometric Correction DOI Creative Commons
Jiale Jiang, Qianyi Zhang,

Shuai Gao

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(9), P. 1557 - 1557

Published: April 27, 2025

Unmanned aerial vehicle (UAV) remote sensing has emerged as a powerful tool for precision agriculture, offering high-resolution crop monitoring capabilities. However, multi-flight UAV missions introduce radiometric inconsistencies that hinder the accuracy of vegetation indices and physiological trait estimation. This study investigates efficacy relative correction in enhancing canopy chlorophyll content (CCC) estimation winter wheat. Dual sensor configurations captured imagery across three experimental sites key wheat phenological stages (the green-up, heading, grain filling stages). Sentinel-2 data served an external reference. The results indicate significantly improved spectral consistency, reducing RMSE values (in bands by >86% 38–96%) correlations with reflectance. predictive CCC models after correction, validation errors decreasing 17.1–45.6% different growth full-season integration yielding 44.3% reduction. These findings confirm critical role optimizing UAV-based estimation, reinforcing its applicability dynamic agricultural monitoring.

Language: Английский

Precision agriculture in the United States: A comprehensive meta-review inspiring further research, innovation, and adoption DOI
Marcelo Rodrigues Barbosa Júnior, Bruno Rafael de Almeida Moreira, Vinicius dos Santos Carreira

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 221, P. 108993 - 108993

Published: May 9, 2024

Language: Английский

Citations

29
Enhancing Hill Farming Efficiency Using Unmanned Agricultural Vehicles: A Comprehensive Review DOI
Mrutyunjay Padhiary,

Laxmi Narayan Sethi,

Avinash Kumar

et al.

Transactions of Indian National Academy of Engineering, Journal Year: 2024, Volume and Issue: 9(2), P. 253 - 268

Published: Feb. 19, 2024

Language: Английский

Citations

17
Estimation of Winter Wheat SPAD Values Based on UAV Multispectral Remote Sensing DOI Creative Commons
Yin Quan, Yuting Zhang, Weilong Li

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(14), P. 3595 - 3595

Published: July 18, 2023

Unmanned aerial vehicle (UAV) multispectral imagery has been applied in the remote sensing of wheat SPAD (Soil and Plant Analyzer Development) values. However, existing research yet to consider influence different growth stages UAV flight altitudes on accuracy estimation. This study aims optimize strategies incorporate multiple feature selection techniques machine learning algorithms enhance value estimation varieties across stages. sets two (20 40 m). Multispectral images were collected for four winter during green-up jointing Three methods (Pearson, recursive elimination (RFE), correlation-based (CFS)) regression models (elastic net, random forest (RF), backpropagation neural network (BPNN), extreme gradient boosting (XGBoost)) combined construct individual as well The CFS-RF (40 m) model achieved satisfactory results (green-up stage: R2 = 0.7270, RPD 2.0672, RMSE 1.1835, RRMSE 0.0259; 0.8092, 2.3698, 2.3650, 0.0487). For cross-growth stage modeling, optimal prediction values at a altitude m using Pearson-XGBoost (R2 0.8069, 2.3135, 2.0911, 0.0442). These demonstrate that UAVs significantly impacts accuracy, (with spatial resolution 2.12 cm) achieves better than 20 1.06 cm). also showed combination can more accurately estimate In addition, this includes varieties, enhancing generalizability facilitating future real-time rapid monitoring growth.

Language: Английский

Citations

28
Transforming Farming: A Review of AI-Powered UAV Technologies in Precision Agriculture DOI Creative Commons
Juhi Agrawal, Muhammad Yeasir Arafat

Drones, Journal Year: 2024, Volume and Issue: 8(11), P. 664 - 664

Published: Nov. 10, 2024

The integration of unmanned aerial vehicles (UAVs) with artificial intelligence (AI) and machine learning (ML) has fundamentally transformed precision agriculture by enhancing efficiency, sustainability, data-driven decision making. In this paper, we present a comprehensive overview the multispectral, hyperspectral, thermal sensors mounted on drones AI-driven algorithms to transform modern farms. Such technologies support crop health monitoring in real time, resource management, automated making, thus improving productivity considerably reduced consumption. However, limitations include high costs operation, limited UAV battery life, need for highly trained operators. novelty study lies thorough analysis comparison all UAV-AI research, along an existing related works gaps. Furthermore, practical solutions technological challenges are summarized provide insights into agriculture. This paper also discusses barriers adoption suggests overcome limitations. Finally, outlines future research directions, which will discuss advances sensor technology, energy-efficient AI models, how these aspects influence ethical considerations regarding use UAVs agricultural research.

Language: Английский

Citations

11
Critical regions identification and coverage using optimal drone flight path planning for precision agriculture DOI Creative Commons

Bharath Krishna Menon,

Tejas Deshpande,

Amrit Pal

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104081 - 104081

Published: Jan. 1, 2025

Language: Английский

Citations

1
A Review on Advancing Agricultural Efficiency through Geographic Information Systems, Remote Sensing, and Automated Systems DOI Creative Commons
Mrutyunjay Padhiary, Payaswini Saikia, Pankaj Roy

et al.

Cureus Journal of Engineering., Journal Year: 2025, Volume and Issue: unknown

Published: March 6, 2025

Language: Английский

Citations

1
Lesion-aware visual transformer network for Paddy diseases detection in precision agriculture DOI
Abdullah Ali Salamai, Nouran Ajabnoor, Waleed Eltayeb Omer Khalid

et al.

European Journal of Agronomy, Journal Year: 2023, Volume and Issue: 148, P. 126884 - 126884

Published: June 1, 2023

Language: Английский

Citations

21
A systematic review on the factors governing precision agriculture adoption among small-scale farmers DOI
Dah John, Norhayati Hussin, Mohd Sazili Shahibi

et al.

Outlook on Agriculture, Journal Year: 2023, Volume and Issue: 52(4), P. 469 - 485

Published: Oct. 11, 2023

The global agricultural paradigm is witnessing a transformative shift with the advent of precision agriculture. While large-scale enterprises have been swift in their embrace this innovation, its uptake among small-scale farmers remains nuanced and complex. This study elucidates multi-faceted determinants that influence adoption agriculture within farming sector. adopts systematic literature review approach, meticulously selecting analysing 29 relevant papers. Drawing upon an exhaustive empirical analyses, research presents composite framework weaving economic, technological, social, environmental factors. Among these, social dynamics emerge as most significant factor, shaped by awareness levels, knowledge dissemination pathways, entrenched cultural norms. These elements often intertwine ingrained traditional practices perceptions, forming intricate layer shaping attitudes. Notably, although economic factors like substantial initial investments calculus Return on Investment are present, they overshadowed considerations. technological landscape characterised challenges digital literacy, infrastructural readiness, interoperability. Lastly, imperatives, underscored resource scarcity, climate change resilience, ecosystem services, offer both motivations. Together, these delineate roadmap guiding journey toward adoption. comprehensive exploration provides stakeholders actionable insights, fostering informed decision-making strategic interventions to augment agriculture's integration tapestry.

Language: Английский

Citations

18
Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure DOI Creative Commons
Shaeden Gokool, Maqsooda Mahomed, Kiara Brewer

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(5), P. e26913 - e26913

Published: Feb. 25, 2024

Smallholder farms are major contributors to agricultural production, food security, and socio-economic growth in many developing countries. However, they generally lack the resources fully maximize their potential. Subsequently require innovative, evidence-based lower-cost solutions optimize productivity. Recently, precision practices facilitated by unmanned aerial vehicles (UAVs) have gained traction sector great potential for smallholder farm applications. Furthermore, advances geospatial cloud computing opened new exciting possibilities remote sensing arena. In light of these recent developments, focus this study was explore demonstrate utility using advanced image processing capabilities Google Earth Engine (GEE) platform process analyse a very high spatial resolution multispectral UAV mapping land use cover (LULC) within farms. The results showed that LULC could be mapped at 0.50 m with an overall accuracy 91%. Overall, we found GEE extremely useful conducting analysis on imagery rapid communication results. Notwithstanding limitations study, findings presented herein quite promising clearly how modern can implemented facilitate improved management farmers.

Language: Английский

Citations

8
The Impact of Digital Literacy on Farmers’ Green Production Behavior: Mediating Effects Based on Ecological Cognition DOI Open Access
Xiao Liu,

Zhenyu Wang,

Xiaoyan Han

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(17), P. 7507 - 7507

Published: Aug. 30, 2024

Farmers’ green production behavior is one of the main determinants sustainability agricultural economy. In this study, Ordered Logit, OLS, and 2SLS models were conducted to evaluate impact digital literacy on farmers’ behavior. On basis, Propensity Score Matching (PSM) method was deal with endogeneity bias that may result from sample self-selection problem. We also adopt mediation effect model test mediating mechanism ecological cognition between The results showed three different types significantly improved found by 19.87%, 15.92%, 24.16% through learning, social, transaction literacy. Meanwhile, improves increasing cognition. demonstrate Therefore, policies increase among farmers should be further promote

Language: Английский

Citations

5