AIMC Topic: Herbicides

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Developing a support vector machine based QSPR model for prediction of half-life of some herbicides.

Ecotoxicology and environmental safety
The half-life (t1/2) of 58 herbicides were modeled by quantitative structure-property relationship (QSPR) based molecular structure descriptors. After calculation and the screening of a large number of molecular descriptors, the most relevant those o...

Deep learning-based laser weed control compared to conventional herbicide application across three vegetable production systems.

Pest management science
BACKGROUND: Herbicides are the primary weed management method for processing vegetable growers, but challenges such as limited chemical options, herbicide resistance, crop injury risks, regulatory changes, and shifting consumer preferences are drivin...

Integrating bioassay and machine learning data for ecological risk assessments of herbicide use on Ulva australis.

Marine pollution bulletin
Herbicide contamination of aquatic ecosystems poses a critical risk to biodiversity. Bioassays provide useful ecological insights on responses to herbicides; however, they require a model organism. Ulva australis is an ideal candidate for herbicide t...

Evaluation of genotoxicity after acute and chronic exposure to 2,4-dichlorophenoxyacetic acid herbicide (2,4-D) in rodents using machine learning algorithms.

The Journal of toxicological sciences
2,4-Dichlorophenoxyacetic acid (2,4-D) is one of the most widely used herbicides in the world, but its mutagenic and carcinogenic potential is still controversial. We simulated environmental exposure to 2,4-D, with the objective of evaluating the gen...

Averting robo-bees: why free-flying robotic bees are a bad idea.

Emerging topics in life sciences
Food security and the sustainability of native ecosystems depends on plant-insect interactions in countless ways. Recently reported rapid and immense declines in insect numbers due to climate change, the use of pesticides and herbicides, the introduc...