Journal ArticleDOI
A Survey of Weed Identification Using Convolutional Neural Networks
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TLDR
In this paper , a short overview of some significant agricultural research endeavours using convolution neural networks (CNNs) for classification and detection of weeds is presented. But, the authors do not discuss the use of CNNs in the field of agriculture.Abstract:
Weeds are the major source of concern for farmers, who anticipate that weeds may lower crop productivity. Thus, it is essential and vital to detect weeds. Traditional weed classification methods such as hand cultivation with hoes have many hindrances such as labour cost and time consumption. Currently, weed reduction farmers are using herbicides, but they have a negative impact on farmer health as well as on the environment. So, farmers want to lower the use of herbicides. Precise spraying is one of the methods in present-day agriculture to lower the usage of herbicides and to destroy the weeds with the assistance of new technologies. Deep learning approaches are already being employed in a variety of agricultural and farming applications and gave better results. This chapter uses convolution neural networks to provide a short overview of some significant agricultural research endeavours. Different architectures of CNN for classification and detection were used. In the sector of agriculture, the authors have outlined the notion of CNNs. read more
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Proceedings ArticleDOI
Densely Connected Convolutional Networks
TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
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