Leveraging Artificial Intelligence for Enhancing Wheat Yield Resilience Amidst Climate Change in Sub-Saharan Africa

LatIA, Journal Year: 2025, Volume and Issue: 3, P. 88 - 88

Published: Feb. 19, 2025

The introduction of a deep learning-based method for non-destructive leaf area index (LAI) assessment has enhanced rapid estimation wheat and similar crops, aiding crop growth monitoring, water, nutrient management. Convolutional Neural Network (CNN)-based algorithms enable accurate, quantification seedling areas assess LAI across diverse genotypes environments, demonstrating adaptability. Transfer learning, known efficiency in plant phenotyping, was tested as resource-saving approach training the model. These advancements support breeding, facilitate genotype selection varied accelerate genetic gains, enhance genomic LAI. By capturing this can improve resilience to climate change. Additionally, advances machine learning data science better prediction distribution mapping global rust pathogens, major agricultural challenge. Accurate risk identification allows timely effective control measures. Moreover, lodging models using CNNs lodging-prone varieties, influencing decisions yield stability. artificial intelligence-driven techniques contribute sustainable enhancement, especially context change increasing food demand.

Language: Английский

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Comparative Analysis of Five African Traditional Multipurpose Crops Using a Food Systems Approach

Food Reviews International, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 37

Published: April 7, 2025

Language: Английский

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Towards Soil Security: Understanding Soil Erosion Footprints and Their Implications in NSW

Soil Security, Journal Year: 2025, Volume and Issue: unknown, P. 100184 - 100184

Published: April 1, 2025

Language: Английский

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A Machine Learning Approach to Genome-Wide Association Mapping of Disease Resistance and Geographic Origin in Sorghum

Published: April 18, 2025

Background Sorghum, often considered the fifth most important cereal crop globally, faces significant production constraints caused by various fungal diseases. Understanding genetic basis of disease resistance and adaptation to geographic origin is crucial for developing improved varieties. This study investigates these aspects in a diverse panel 377 sorghum accessions using machine learning-enabled genome-wide association (GWAS). Results The analyzed accessions, including mini core collection additional from Senegal. Phenotypic evaluation anthracnose, head smut, downy mildew was conducted on collection. Genotypic data comprising nearly 300,000 SNP markers were used GWAS with Bootstrap Forest models. While phenotypic clustering based did not directly correlate origin, differentiation observed origin. Machine learning-driven identified SNPs associated particularly chromosome 10, candidate genes transcription factors. near known or predicted roles plant defense resistance, such as zinc-binding proteins anthracnose LRR- NB-ARC-containing smut. Conclusions research provides insights into complex architecture sorghum. In addition previously resistant through traditional GWAS, offer valuable resources enhancing breeding programs marker-assisted selection other advanced techniques.

Language: Английский

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Environmental Impact on Grain Quality in Ethiopian Sorghum Landraces

Journal of Agriculture and Food Research, Journal Year: 2025, Volume and Issue: 21, P. 101916 - 101916

Published: April 30, 2025

Language: Английский

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