Broad-Leaf Weed Detection in Pasture

Zhang, Wenhao; Hansen, Mark F.; Volonakis, Timothy N.; Smith, Melvyn L.; Smith, Lyndon; Wilson, J. A.; Ralston, Graham; Broadbent, Laurence; Wright, Glynn C.

doi:10.1109/icivc.2018.8492831

Cited by 31 publications

(16 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Weeds detection and control is an important application of precision agriculture [87], and also an important application of dense scenes in agriculture. Weeds generally have the characteristics of wide distribution, high density and low nutritional value, which do not make use of the growth and development of crops.…”

Section: Detectionmentioning

confidence: 99%

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Zhang

Liu

Gong

et al. 2020

Sensors

111

View full text Add to dashboard Cite

Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized.

show abstract

Section: Detectionmentioning

confidence: 99%

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Zhang

Liu

Gong

et al. 2020

Sensors

111

View full text Add to dashboard Cite

show abstract

“…Machine learning has many mechanisms to detect the type of weed on every crop and intimate. A study [30] used to Support Vector Machine and Conventional Neural Network for detecting Broad-leaf weed detection in the pasture from the images. Another study [23] used SVM, ANN and CNN for classifying the 4-different crops and 2-different weeds, but CNN gives better results compared with the remaining methods.…”

Section: Weed Detectionmentioning

confidence: 99%

A Review on the Role of Machine Learning in Agriculture

Veeragandham

Santhi

2020

SCPE

View full text Add to dashboard Cite

Machine learning is a promising domain which is widely used now a days in the field of agriculture. The availability of manpower for agriculture is not enough and skill full farmers are less. Understanding the situation of the crop is not that much easy to detect and prevent the diseases in the crop. It is also widely employed in various agricultural fields such as topsoil management, yield management, water management, disease management and climate conditions. The machine learning models facilitate very fast and optimal decisions. The model of machine learning involves with training and testing to predict the accuracy of the result. The use of machine learning in agriculture helps to increase the productivity and better management on soil classification, disease detection, species management, water management, yield prediction, crop quality and weed detection. This article aims at providing detailed information on various machine learning approaches proposed in the past five years by emphasizing the advantage and disadvantages. It also compares different machine learning algorithms used in the modern agricultural field.

show abstract

“…The network parameters adjusted using a method with progressive resizing, with the speed of cyclic learning and the function of focus loss. Zhang et al [37] made a detailed comparison of VGG and CCD networks for finding weed.…”

Section: Image Preprocessing In Weed Recognition Tasksmentioning

confidence: 99%