AIMC Topic: Crops, Agricultural

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A fusion transfer learning framework for intelligent pest recognition in sustainable agriculture.

Scientific reports
With the fast growth in the population of the world, there is a constantly increasing requirement for sustainable food supplies. Agriculture is the backbone of the global food supply, with vegetables and fruits being essential for a balanced intake. ...

Towards smart agriculture: AI-driven prediction of key genes for revolutionizing crop breeding.

Planta
AI-driven key gene prediction is revolutionizing crop breeding, enhancing precision, efficiency, and sustainability while paving the way for intelligent, data-driven agricultural innovation. The integration of artificial intelligence (AI) into crop b...

Hybrid deep learning for smart paddy disease diagnosis using self supervised hierarchical reconstruction and attention based temporal analysis.

Scientific reports
Accurate and early disease detection in paddy crops is essential for maximizing crop yield which ensures food security. Traditional methods are often labor-intensive, time-consuming, and domain-specific expertise. Feed-forward deep-learning models wi...

Predicting crop disease severity using real time weather variability through machine learning algorithms.

Scientific reports
Integrating disease severity with real-time meteorological variables and advanced machine learning techniques has provided valuable predictive insights for assessing disease severity in wheat. This study emphasizes the potential of machine learning m...

When crops fail, forests follow: Agricultural shocks and deforestation in Zambia.

Proceedings of the National Academy of Sciences of the United States of America
As climate change makes agricultural production shocks more frequent and severe, it is vital to understand their effect on farmer welfare, land use, and deforestation. Theoretically, a change in agricultural productivity could increase or decrease de...

Next-generation translational genomics for developing future crops.

Functional & integrative genomics
Advancements in translational genomics have revolutionized crop breeding, driving us from traditional breeding methods towards next-generation strategies that integrate genomic, transcriptomic, and phenotypic data to expedite crop improvement. There ...

Associations among weed communities, management practices, and environmental factors in U.S. snap bean (Phaseolus vulgaris) production.

PloS one
Weed species that escape control (hereafter called residual weeds) coupled with changing weather patterns are emerging challenges for snap bean processors and growers. Field surveys were conducted to identify associations among crop/weed management p...

Recent advances in breeding systems and their improvement in forage crops.

Molecular biology reports
Fodder crops are crops which are mainly grown as feed for livestocks and it consist of a variety of crops ranging from annual to perennial crops. Even with many crops involved, advance crop improvement practices are done less in these crops. With the...

Revolution and advances in gene editing and genomics technology for developing climate-resilient legume crops: developments and prospects.

Plant molecular biology
Legumes are essential for agriculture and food security. Biotic and abiotic stresses pose significant challenges to legume production, lowering productivity levels. Most legumes must be genetically improved by introducing alleles that give pest and d...

Multiple model visual feature embedding and selection method for an efficient pest classification supporting precision agriculture.

Scientific reports
Agriculture 5.0 is a principal economic activity in the world with major workforce dependent crops cultivation. An automated system for crops field insect pest identification can help decrease labour, while also improving the speed and precision in c...