AIMC Topic: Herbicides

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QSAR Model Development for the Environmental Risk Limits and High-Risk List Identification of Phenylurea Herbicides in Aquatic Environments.

Journal of agricultural and food chemistry Jun 9, 2025
Due to the extensive residues of phenylurea herbicides (PUHs) in the environment, it is important for the ecological risk assessment of PUHs to determine their environmental risk limits and identify the high-risk PUHs. This study derived the environm...
Quantitative Structure-Activity Relationship Phenylurea Compounds Water Pollutants, Chemical Herbicides Risk Assessment Environmental Monitoring
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Deep learning-based weed detection for precision herbicide application in turf.

Pest management science Feb 28, 2025
BACKGROUND: Precision weed mapping in turf according to its susceptibility to selective herbicides allows the smart sprayer to spot-spray the most pertinent herbicides onto the susceptible weeds. The objective of this study was to evaluate the feasib...
Neural Networks, Computer Plant Weeds Algorithms Herbicides Deep Learning Weed Control
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Artificial neural networks to estimate the sorption and desorption of the herbicide linuron in Brazilian soils.

Environmental pollution (Barking, Essex : 1987) Jan 15, 2025
Generally, herbicides used in Brazil follow manufacturer's recommendations, which often do not consider soil attributes. Statistical models that include the physicochemical properties of the soil involved in herbicide retention processes could enable...
Neural Networks, Computer Soil Models, Chemical Adsorption Herbicides Soil Pollutants Brazil Linuron Environmental Monitoring
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A green and efficient method for detecting nicosulfuron residues in field maize using hyperspectral imaging and deep learning.

Journal of hazardous materials Nov 30, 2024
Accurate and rapid detection of nicosulfuron herbicide residues in field-grown maize is essential for implementing chemical remediation and optimizing spraying strategies. However, current detection methods are costly and time-consuming. This study a...
Pyridines Zea mays Herbicides Deep Learning Plant Leaves Sulfonylurea Compounds Hyperspectral Imaging Pesticide Residues Neural Networks, Computer
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Integrating automated machine learning and metabolic reprogramming for the identification of microplastic in soil: A case study on soybean.

Journal of hazardous materials Aug 22, 2024
The accumulation of polyethylene microplastic (PE-MPs) in soil can significantly impact plant quality and yield, as well as affect human health and food chain cycles. Therefore, developing rapid and effective detection methods is crucial. In this stu...
Microplastics Herbicides Glycine max Polyethylene Metabolic Reprogramming Soil Pollutants Soil Environmental Monitoring Machine Learning
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Dual-Mode Fluorescent/Intelligent Lateral Flow Immunoassay Based on Machine Learning Algorithm for Ultrasensitive Analysis of Chloroacetamide Herbicides.

Analytical chemistry Jul 11, 2024
Given the harmful effect of pesticide residues, it is essential to develop portable and accurate biosensors for the analysis of pesticides in agricultural products. In this paper, we demonstrated a dual-mode fluorescent/intelligent (DM-f/DM-i) latera...
Immunoassay Fluorescent Dyes Algorithms Herbicides Acetamides Limit of Detection Machine Learning Zea mays
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Early detection of nicosulfuron toxicity and physiological prediction in maize using multi-branch deep learning models and hyperspectral imaging.

Journal of hazardous materials May 28, 2024
The misuse of herbicides in fields can cause severe toxicity in maize, resulting in significant reductions in both yield and quality. Therefore, it is crucial to develop early and efficient methods for assessing herbicide toxicity, protecting maize p...
Deep Learning Zea mays Pyridines Herbicides Sulfonylurea Compounds
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Detection and coverage estimation of purple nutsedge in turf with image classification neural networks.

Pest management science Mar 12, 2024
BACKGROUND: Accurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing informatio...
Neural Networks, Computer Cynodon Deep Learning Herbicides Weed Control Image Processing, Computer-Assisted Plant Weeds
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Semi-supervised learning methods for weed detection in turf.

Pest management science Jan 24, 2024
BACKGROUND: Accurate weed detection is a prerequisite for precise automatic precision herbicide application. Previous research has adopted the laborious and time-consuming approach of manually labeling and processing large image data sets to develop ...
Plant Weeds Supervised Machine Learning Deep Learning Image Processing, Computer-Assisted Weed Control Poaceae Neural Networks, Computer Herbicides
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Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning.

Sensors (Basel, Switzerland) Mar 19, 2023
Weeds can cause significant yield losses and will continue to be a problem for agricultural production due to climate change. Dicamba is widely used to control weeds in monocot crops, especially genetically engineered dicamba-tolerant (DT) dicot crop...
Glycine max Crops, Agricultural Unmanned Aerial Devices Deep Learning Herbicides Dicamba Plant Breeding Plant Weeds
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