Published: Sept. 18, 2024
Language: Английский
Published: Sept. 18, 2024
Language: Английский
Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 224, P. 109158 - 109158
Published: June 16, 2024
Language: Английский
Citations
15Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100888 - 100888
Published: March 1, 2025
Language: Английский
Citations
0Plants, 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
Citations
0Water Biology and Security, Journal Year: 2025, Volume and Issue: unknown, P. 100381 - 100381
Published: March 1, 2025
Language: Английский
Citations
0Field Crops Research, Journal Year: 2025, Volume and Issue: 327, P. 109883 - 109883
Published: April 5, 2025
Language: Английский
Citations
0Soil and Tillage Research, Journal Year: 2025, Volume and Issue: 252, P. 106576 - 106576
Published: April 22, 2025
Language: Английский
Citations
0Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105583 - 105583
Published: June 1, 2025
Language: Английский
Citations
0Journal of Plant Growth Regulation, Journal Year: 2024, Volume and Issue: unknown
Published: Aug. 24, 2024
Language: Английский
Citations
3Molecular Breeding, Journal Year: 2024, Volume and Issue: 44(9)
Published: Sept. 1, 2024
Language: Английский
Citations
1Drones, 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
Citations
1