AIMC Topic: Plant Weeds

Clear Filters Showing 11 to 20 of 37 articles

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...

Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning.

Sensors (Basel, Switzerland)
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...

Use of synthetic images for training a deep learning model for weed detection and biomass estimation in cotton.

Scientific reports
Site-specific treatment of weeds in agricultural landscapes has been gaining importance in recent years due to economic savings and minimal impact on the environment. Different detection methods have been developed and tested for precision weed manag...

A deep learning-based method for classification, detection, and localization of weeds in turfgrass.

Pest management science
BACKGROUND: Precision spraying of synthetic herbicides can reduce herbicide input. Previous research demonstrated the effectiveness of using image classification neural networks for detecting weeds growing in turfgrass, but did not attempt to discrim...

An Improved Deep Neural Network Model of Intelligent Vehicle Dynamics via Linear Decreasing Weight Particle Swarm and Invasive Weed Optimization Algorithms.

Sensors (Basel, Switzerland)
We propose an improved DNN modeling method based on two optimization algorithms, namely the linear decreasing weight particle swarm optimization (LDWPSO) algorithm and invasive weed optimization (IWO) algorithm, for predicting vehicle's longitudinal-...