Detection and monitoring wheat diseases using unmanned aerial vehicles (UAVs)

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 224, P. 109158 - 109158

Published: June 16, 2024

Language: Английский

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Unmanned aerial systems (UAS)-based field high throughput phenotyping (HTP) as plant breeders’ toolbox: a comprehensive review

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100888 - 100888

Published: March 1, 2025

Language: Английский

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Advancing Crop Resilience Through High-Throughput Phenotyping for Crop Improvement in the Face of Climate Change

Plants, Journal Year: 2025, Volume and Issue: 14(6), P. 907 - 907

Published: March 14, 2025

Climate change intensifies biotic and abiotic stresses, threatening global crop productivity. High-throughput phenotyping (HTP) technologies provide a non-destructive approach to monitor plant responses environmental offering new opportunities for both stress resilience breeding research. Innovations, such as hyperspectral imaging, unmanned aerial vehicles, machine learning, enhance our ability assess traits under various including drought, salinity, extreme temperatures, pest disease infestations. These tools facilitate the identification of stress-tolerant genotypes within large segregating populations, improving selection efficiency programs. HTP can also play vital role by accelerating genetic gain through precise trait evaluation hybridization enhancement. However, challenges data standardization, management, high costs equipment, complexity linking phenotypic observations improvements limit its broader application. Additionally, variability genotype-by-environment interactions complicate reliable selection. Despite these challenges, advancements in robotics, artificial intelligence, automation are precision scalability analyses. This review critically examines dual assessment tolerance performance, highlighting transformative potential existing limitations. By addressing key leveraging technological advancements, significantly research, discovery, parental selection, scheme optimization. While current methodologies still face constraints fully translating insights into practical applications, continuous innovation high-throughput holds promise revolutionizing ensuring sustainable agricultural production changing climate.

Language: Английский

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A high-throughput phenome-based analysis of morphological variation and environmental adaptation in extremely high-altitude schizothoracine fishes

Water Biology and Security, Journal Year: 2025, Volume and Issue: unknown, P. 100381 - 100381

Published: March 1, 2025

Language: Английский

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Integrating UAV and satellite LAI data into a modified DSSAT-rapeseed model to improve yield predictions

Field Crops Research, Journal Year: 2025, Volume and Issue: 327, P. 109883 - 109883

Published: April 5, 2025

Language: Английский

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An adaptive recognition method for crop row orientation in dry land by combining morphological and texture features

Soil and Tillage Research, Journal Year: 2025, Volume and Issue: 252, P. 106576 - 106576

Published: April 22, 2025

Language: Английский

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Digital Twin technology advancing Industry 4.0 and Industry 5.0 across Sectors

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105583 - 105583

Published: June 1, 2025

Language: Английский

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Plant Phenomics: The Force Behind Tomorrow’s Crop Phenotyping Tools

Journal of Plant Growth Regulation, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 24, 2024

Language: Английский

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Genomic selection for crop improvement in fruits and vegetables: a systematic scoping review

Molecular Breeding, Journal Year: 2024, Volume and Issue: 44(9)

Published: Sept. 1, 2024

Language: Английский

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Advanced Image Preprocessing and Integrated Modeling for UAV Plant Image Classification

Drones, Journal Year: 2024, Volume and Issue: 8(11), P. 645 - 645

Published: Nov. 6, 2024

The automatic identification of plant species using unmanned aerial vehicles (UAVs) is a valuable tool for ecological research. However, challenges such as reduced spatial resolution due to high-altitude operations, image degradation from camera optics and sensor limitations, information loss caused by terrain shadows hinder the accurate classification UAV imagery. This study addresses these issues proposing novel preprocessing pipeline evaluating its impact on model performance. Our approach improves quality through multi-step that includes Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) enhancement, Contrast-Limited Adaptive Histogram Equalization (CLAHE) contrast improvement, white balance adjustments color representation. These steps ensure high-quality input data, leading better For feature extraction classification, we employ pre-trained VGG-16 deep convolutional neural network, followed machine learning classifiers, including Support Vector Machine (SVM), random forest (RF), Extreme Gradient Boosting (XGBoost). hybrid approach, combining with not only enhances accuracy but also reduces computational resource requirements compared relying solely models. Notably, + SVM achieved an outstanding 97.88% dataset preprocessed ESRGAN adjustments, precision 97.9%, recall 97.8%, F1 score 0.978. Through comprehensive comparative study, demonstrate proposed framework, utilizing extraction, images achieves superior performance in

Language: Английский

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