Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weed Recognition using Deep Learning Techniques on Class-imbalanced
  Imagery   Most weed species can adversely impact agricultural productivity by competing
for nutrients required by high-value crops. Manual weeding is not practical for
large cropping areas. Many studies have been undertaken to develop automatic
weed management systems for agricultural crops. In this process, one of the
major tasks is to recognise the weeds from images. However, weed recognition is
a challenging task. It is because weed and crop plants can be similar in
colour, texture and shape which can be exacerbated further by the imaging
conditions, geographic or weather conditions when the images are recorded.
Advanced machine learning techniques can be used to recognise weeds from
imagery. In this paper, we have investigated five state-of-the-art deep neural
networks, namely VGG16, ResNet-50, Inception-V3, Inception-ResNet-v2 and
MobileNetV2, and evaluated their performance for weed recognition. We have used
several experimental settings and multiple dataset combinations. In particular,
we constructed a large weed-crop dataset by combining several smaller datasets,
mitigating class imbalance by data augmentation, and using this dataset in
benchmarking the deep neural networks. We investigated the use of transfer
learning techniques by preserving the pre-trained weights for extracting the
features and fine-tuning them using the images of crop and weed datasets. We
found that VGG16 performed better than others on small-scale datasets, while
ResNet-50 performed better than other deep networks on the large combined
dataset.

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The categories of this paper are: hep-ph
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
19
The categories of this paper are: eess
9
The categories of this paper are: nlin