AIMC Topic: Plant Weeds

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WeedSwin hierarchical vision transformer with SAM-2 for multi-stage weed detection and classification.

Scientific reports
Weed detection and classification using computer vision and deep learning techniques have emerged as crucial tools for precision agriculture, offering automated solutions for sustainable farming practices. This study presents a comprehensive approach...

Hybrid deep learning model for density and growth rate estimation on weed image dataset.

Scientific reports
Agriculture research is particularly essential since crop production is a challenge for farmers in India and around the world. 37% of the crop is impacted by invasive plants (weeds). Those unwelcome plants that interbreed with cultivated crops and de...

Deep learning-based weed detection for precision herbicide application in turf.

Pest management science
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...

Allelopathic effects of six alfalfa varieties at three stubbles on the germination, seedling and root growth of green foxtail and barnyardgrass.

PloS one
Alfalfa (Medicago sativa) is known to release allelopathic substances to affect the germination and growth of other plants, which have the potential to be applied in controlling weeds. Green foxtail (Setaria viridis) and barnyardgrass (Echinochloa cr...

Detection and coverage estimation of purple nutsedge in turf with image classification neural networks.

Pest management science
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...

Evaluation of two deep learning-based approaches for detecting weeds growing in cabbage.

Pest management science
BACKGROUND: Machine vision-based precision weed management is a promising solution to substantially reduce herbicide input and weed control cost. The objective of this research was to compare two different deep learning-based approaches for detecting...

Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa.

Pest management science
BACKGROUND: Accurate and reliable weed detection in real time is essential for realizing autonomous precision herbicide application. The objective of this research was to propose a novel neural network architecture to improve the detection accuracy f...

Semi-supervised learning methods for weed detection in turf.

Pest management science
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 ...

Application technology for bioherbicides: challenges and opportunities with dry inoculum and liquid spray formulations.

Pest management science
Bioherbicides offer many potential benefits as part of an integrated weed management system or a totally biological or organic cropping system. A key factor for success is the selection of appropriate formulation and delivery systems for each target ...

Weed Detection Using Deep Learning: A Systematic Literature Review.

Sensors (Basel, Switzerland)
Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this...