AIMC Topic: Weed Control

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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...

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...

Comparative Life Cycle Assessment of intra-row and inter-row weeding practices using autonomous robot systems in French vineyards.

The Science of the total environment
Viticulture, as well as other crops, is facing obligation to reduce the use of herbicides and to develop alternatives solutions to chemical weed control. These alternatives can be achieved by mechanical weeding either using tractors or weeding robots...

Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning.

Sensors (Basel, Switzerland)
Crop and weed discrimination in natural field environments is still challenging for implementing automatic agricultural practices, such as weed control. Some weed control methods have been proposed. However, these methods are still restricted as they...

A novel deep learning-based method for detection of weeds in vegetables.

Pest management science
BACKGROUND: Precision weed control in vegetable fields can substantially reduce the required weed control inputs. Rapid and accurate weed detection in vegetable fields is a challenging task due to the presence of a wide variety of weed species at var...

Machine learning models as an alternative to determine productivity losses caused by weeds.

Pest management science
BACKGROUND: Weed control can be economically viable if implemented at the necessary time to minimize interference. Empirical mathematical models have been used to determine when to start the weed control in many crops. Furthermore, empirical models h...

A novel semi-supervised framework for UAV based crop/weed classification.

PloS one
Excessive use of agrochemicals for weed controlling infestation has serious agronomic and environmental repercussions associated. An appropriate amount of pesticide/ chemicals is essential for achieving the desired smart farming and precision agricul...

Real-time recognition of spraying area for UAV sprayers using a deep learning approach.

PloS one
Agricultural production is vital for the stability of the country's economy. Controlling weed infestation through agrochemicals is necessary for increasing crop productivity. However, its excessive use has severe repercussions on the environment (dam...

Testing the ability of unmanned aerial systems and machine learning to map weeds at subfield scales: a test with the weed Alopecurus myosuroides (Huds).

Pest management science
BACKGROUND: It is important to map agricultural weed populations to improve management and maintain future food security. Advances in data collection and statistical methodology have created new opportunities to aid in the mapping of weed populations...

Recognition pest by image-based transfer learning.

Journal of the science of food and agriculture
BACKGROUND: Plant pests mainly refers to insects and mites that harm crops and products. There are a wide variety of plant pests, with wide distribution, fast reproduction and large quantity, which directly causes serious losses to crops. Therefore, ...