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

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 224, С. 109158 - 109158

Опубликована: Июнь 16, 2024

Язык: 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, Год журнала: 2025, Номер unknown, С. 100888 - 100888

Опубликована: Март 1, 2025

Язык: Английский

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

Plants, Год журнала: 2025, Номер 14(6), С. 907 - 907

Опубликована: Март 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.

Язык: 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, Год журнала: 2025, Номер unknown, С. 100381 - 100381

Опубликована: Март 1, 2025

Язык: Английский

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

Field Crops Research, Год журнала: 2025, Номер 327, С. 109883 - 109883

Опубликована: Апрель 5, 2025

Язык: 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, Год журнала: 2025, Номер 252, С. 106576 - 106576

Опубликована: Апрель 22, 2025

Язык: Английский

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Digital Twin technology advancing Industry 4.0 and Industry 5.0 across Sectors

Results in Engineering, Год журнала: 2025, Номер unknown, С. 105583 - 105583

Опубликована: Июнь 1, 2025

Язык: Английский

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

Journal of Plant Growth Regulation, Год журнала: 2024, Номер unknown

Опубликована: Авг. 24, 2024

Язык: Английский

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

Molecular Breeding, Год журнала: 2024, Номер 44(9)

Опубликована: Сен. 1, 2024

Язык: Английский

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Advanced Image Preprocessing and Integrated Modeling for UAV Plant Image Classification

Drones, Год журнала: 2024, Номер 8(11), С. 645 - 645

Опубликована: Ноя. 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

Язык: Английский

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