AIMC Topic: Phaseolus

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Quantum machine learning driven optimization of nutrient-hormone interactions for enhanced in vitro regeneration of common bean.

BMC plant biology
Common bean (Phaseoulus vulgarsis) is an important edible legume crop, but its improvement through modern biotechnological tools has been limited due to the lack of efficient and reproducible in vitro regeneration protocols. This bottleneck restricts...

Enhancing enviromics based predictions in common bean multi-environment trials.

Scientific reports
Enviromic approaches enhance predictive models by incorporating environmental data into selection frameworks. By integrating factor analytic (FA) models, enviromics, and Geographic Information Systems (GIS), the GIS-FA method was proposed to improve ...

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...

Predicting yellow mosaic disease severity in yardlong bean using visible imaging coupled with machine learning model.

Scientific reports
Accurate estimation of plant disease severity is pivotal for effective management and decision-making. Field experiments were conducted to understand the correlation and predict the yellow mosaic disease severity in yard-long beans using visible imag...

Detection of kidney bean leaf spot disease based on a hybrid deep learning model.

Scientific reports
Rapid diagnosis of kidney bean leaf spot disease is crucial for ensuring crop health and increasing yield. However, traditional machine learning methods face limitations in feature extraction, while deep learning approaches, despite their advantages,...

Research on variety identification of common bean seeds based on hyperspectral and deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Accurate, fast and non-destructive identification of varieties of common bean seeds is important for the cultivation and efficient utilization of common beans. This study is based on hyperspectral and deep learning to identify the varieties of common...

Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI.

Scientific reports
Common beans (CB), a vital source for high protein content, plays a crucial role in ensuring both nutrition and economic stability in diverse communities, particularly in Africa and Latin America. However, CB cultivation poses a significant threat to...

Robotic Sensing and Stimuli Provision for Guided Plant Growth.

Journal of visualized experiments : JoVE
Robot systems are actively researched for manipulation of natural plants, typically restricted to agricultural automation activities such as harvest, irrigation, and mechanical weed control. Extending this research, we introduce here a novel methodol...

Determination of soyasaponins in Fagioli di Sarconi beans (Phaseolus vulgaris L.) by LC-ESI-FTICR-MS and evaluation of their hypoglycemic activity.

Analytical and bioanalytical chemistry
Soyasaponins are oleanene-type triterpenoid saponins, naturally occurring in many edible plants that have attracted a great deal of attention for their role in preventing chronic diseases. The aim of this study was to establish the distribution and t...

Artificial intelligence in the selection of common bean genotypes with high phenotypic stability.

Genetics and molecular research : GMR
Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean g...