Effectiveness of drone-based thermal sensors in optimizing controlled environment agriculture performance under arid conditions DOI Creative Commons

Rawan Al-Najadi,

Yaseen Al-Mulla,

Ibtisam Al-Abri

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 16, 2025

Abstract Controlled environmental agriculture (CEA), integrated with internet of things and wireless sensor network (WSN) technologies, offers advanced tools for real-time monitoring assessment microclimate plant health/stress. Drone applications have emerged as transformative technology significant potential CEA. However, adoption practical implementation such technologies remain limited, particularly in arid regions. Despite their advantages agriculture, drones yet to gain widespread utilization CEA systems. This study investigates the effectiveness drone-based thermal imaging (DBTI) optimizing performance health under conditions. Several WSN sensors were deployed track microclimatic variations within environment. A novel method was developed assessing canopy temperature (Tc) using thermocouples DBTI. The crop water stress index (CWSI) computed based on Tc extracted from Findings revealed that DBTI effectively distinguished between all treatments, detection exhibiting a strong correlation (R 2 = 0.959) sensor-based measurements. Results confirmed direct relationship CWSI Tc, well association soil moisture content CWSI. research demonstrates can enhance irrigation scheduling accuracy provide precise evapotranspiration (ETc) estimates at specific spatiotemporal scales, contributing improved food security.

Language: Английский

Integrating digital technologies in agriculture for climate change adaptation and mitigation: State of the art and future perspectives DOI
Carlos Parra-López, Saker Ben Abdallah, Guillermo Garcia‐Garcia

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 226, P. 109412 - 109412

Published: Sept. 7, 2024

Language: Английский

Citations

15
Cotton Verticillium wilt monitoring based on UAV multispectral-visible multi-source feature fusion DOI
Rui Ma, Nannan Zhang, Xiao Zhang

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 217, P. 108628 - 108628

Published: Jan. 21, 2024

Language: Английский

Citations

14
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
Monitoring Maize Canopy Chlorophyll Content throughout the Growth Stages Based on UAV MS and RGB Feature Fusion DOI Creative Commons
Wenfeng Li,

Kun Pan,

Wenrong Liu

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(8), P. 1265 - 1265

Published: Aug. 1, 2024

Chlorophyll content is an important physiological indicator reflecting the growth status of crops. Traditional methods for obtaining crop chlorophyll are time-consuming and labor-intensive. The rapid development UAV remote sensing platforms offers new possibilities monitoring in field To improve efficiency accuracy maize canopies, this study collected RGB, multispectral (MS), SPAD data from canopies at jointing, tasseling, grouting stages, constructing a dataset with fused features. We developed canopy models based on four machine learning algorithms: BP neural network (BP), multilayer perceptron (MLP), support vector regression (SVR), gradient boosting decision tree (GBDT). results showed that, compared to single-feature methods, MS RGB feature method achieved higher accuracy, R² values ranging 0.808 0.896, RMSE between 2.699 3.092, NRMSE 10.36% 12.26%. SVR model combined MS–RGB outperformed BP, MLP, GBDT content, achieving 2.746, 10.36%. In summary, demonstrates that by using model, can be effectively improved. This approach reduces need traditional measuring facilitates real-time management nutrition.

Language: Английский

Citations

10
Advancements in Utilizing Image-Analysis Technology for Crop-Yield Estimation DOI Creative Commons
Yu Feng, Ming Wang, Jun Xiao

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(6), P. 1003 - 1003

Published: March 12, 2024

Yield calculation is an important link in modern precision agriculture that effective means to improve breeding efficiency and adjust planting marketing plans. With the continuous progress of artificial intelligence sensing technology, yield-calculation schemes based on image-processing technology have many advantages such as high accuracy, low cost, non-destructive calculation, they been favored by a large number researchers. This article reviews research crop-yield remote images visible light images, describes technical characteristics applicable objects different schemes, focuses detailed explanations data acquisition, independent variable screening, algorithm selection, optimization. Common issues are also discussed summarized. Finally, solutions proposed for main problems arisen so far, future directions predicted, with aim achieving more wider popularization image technology.

Language: Английский

Citations

8
Harnessing artificial intelligence and remote sensing in climate-smart agriculture: the current strategies needed for enhancing global food security DOI Creative Commons
Gideon Sadikiel Mmbando

Cogent Food & Agriculture, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 20, 2025

Global food security is seriously threatened by climate change, which calls for creative agricultural solutions. However, little known about how different smart technologies are integrated to enhance security. As a strategic reaction these difficulties, this review investigates the incorporation of remote sensing (RS) as well artificial intelligence (AI) into climate-smart agriculture (CSA). This demonstrates advances can improve resilience, productivity, and sustainability utilizing AI's capacity predictive analytics, crop modelling, precision agriculture, along with RS's strengths in projections, land management, continuous surveillance. Several important tactics were covered, such combining AI RS regulate risks, maximize resource utilization, practice choices. The also discusses issues like policy frameworks, building, accessibility that prevent from being widely adopted. highlights further CSA offers insights they help ensure systems remain secure changing climates.

Language: Английский

Citations

1
Optimal band selection and transfer in drone-based hyperspectral images for plant-level vegetable crops identification using statistical-swarm intelligence (SSI) hybrid algorithms DOI Creative Commons

Anagha S. Sarma,

‪Rama Rao Nidamanuri

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103051 - 103051

Published: Jan. 1, 2025

Language: Английский

Citations

1
Advancing food security through drone-based hyperspectral imaging: applications in precision agriculture and post-harvest management DOI

Debashish Kar,

Sambandh Bhusan Dhal

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(3)

Published: Feb. 13, 2025

Language: Английский

Citations

1
Field-Deployed Spectroscopy from 350 to 2500 nm: A Promising Technique for Early Identification of Powdery Mildew Disease (Erysiphe necator) in Vineyards DOI Creative Commons
Sergio Vélez, Enrique Barajas, J.A. Rubio

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(3), P. 634 - 634

Published: March 21, 2024

This study explores spectroscopy in the 350 to 2500 nm range for detecting powdery mildew (Erysiphe necator) grapevine leaves, crucial precision agriculture and sustainable vineyard management. In a controlled experimental setting, spectral reflectance on leaves with varying infestation levels was measured using FieldSpec 4 spectroradiometer during July September. A detailed assessment conducted following guidelines recommended by European Mediterranean Plant Protection Organization (EPPO) quantify level of infestation; categorising into five distinct grades based percentage leaf surface area affected. Subsequently, data were collected contact probe tungsten halogen bulb connected spectroradiometer, taking three measurements across different areas each leaf. Partial Least Squares Regression (PLSR) analysis yielded coefficients determination R2 = 0.74 0.71, Root Mean Square Errors (RMSEs) 12.1% 12.9% calibration validation datasets, indicating high accuracy early disease detection. Significant differences noted between healthy infected especially around 450 700 visible light, 1050 nm, 1425 1650 2250 near-infrared spectrum, likely due tissue damage, chlorophyll degradation water loss. Finally, Powdery Mildew Vegetation Index (PMVI) introduced, calculated as PMVI (R755 − R675)/(R755 + R675), where R755 R675 are reflectances at 755 (NIR) 675 (red), effectively estimating severity (R2 0.7). The demonstrates that spectroscopy, combined PMVI, provides reliable, non-invasive method managing promoting healthier vineyards through practices.

Language: Английский

Citations

7
Advancements of UAV and Deep Learning Technologies for Weed Management in Farmland DOI Creative Commons
Jinmeng Zhang, Yu Feng, Qian Zhang

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(3), P. 494 - 494

Published: Feb. 28, 2024

With the continuous growth of global population and increasing demand for crop yield, enhancing productivity has emerged as a crucial research objective on scale. Weeds, being one primary abiotic factors impacting contribute to approximately 13.2% annual food loss. In recent years, Unmanned Aerial Vehicle (UAV) technology developed rapidly its maturity led widespread utilization in improving reducing management costs. Concurrently, deep learning become prominent tool image recognition. Convolutional Neural Networks (CNNs) achieved remarkable outcomes various domains, including agriculture, such weed detection, pest identification, plant/fruit counting, grading, etc. This study provides an overview development UAV platforms, classification platforms their advantages disadvantages, well types characteristics data collected by common vision sensors used discusses application detection. The manuscript presents current advancements CNNs tasks while emphasizing existing limitations future trends process assist researchers working applying techniques management.

Language: Английский

Citations

6