Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting rice blast disease: machine learning versus process based
  models   Rice is the second most important cereal crop worldwide, and the first in
terms of number of people who depend on it as a major staple food. Rice blast
disease is the most important biotic constraint of rice cultivation causing
each year millions of dollars of losses. Despite the efforts for breeding new
resistant varieties, agricultural practices and chemical control are still the
most important methods for disease management. Thus, rice blast forecasting is
a primary tool to support rice growers in controlling the disease. In this
study, we compared four models for predicting rice blast disease, two
operational process-based models (Yoshino and WARM) and two approaches based on
machine learning algorithms (M5Rules and RNN), the former inducing a rule-based
model and the latter building a neural network. In situ telemetry is important
to obtain quality in-field data for predictive models and this was a key aspect
of the RICE-GUARD project on which this study is based. According to the
authors, this is the first time process-based and machine learning modelling
approaches for supporting plant disease management are compared.

29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph