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ORIGINAL RESEARCH
published: 09 April 2021
doi: 10.3389/fmicb.2021.661132
Edited by:
Alicia Rodríguez,
University of Extremadura, Spain
Reviewed by:
Alejandro Hernández,
University of Extremadura, Spain
Naresh Magan,
Cranfield University, United Kingdom
*Correspondence:
Paola Battilani
paola.battilani@unicatt.it
Specialty section:
This article was submitted to
Food Microbiology,
a section of the journal
Frontiers in Microbiology
Received: 30 January 2021
Accepted: 16 March 2021
Published: 09 April 2021
Citation:
Camardo Leggieri M, Mazzoni M
and Battilani P (2021) Machine
Learning for Predicting Mycotoxin
Occurrence in Maize.
Front. Microbiol. 12:661132.
doi: 10.3389/fmicb.2021.661132
Machine Learning for Predicting
Mycotoxin Occurrence in Maize
Marco Camardo Leggieri, Marco Mazzoni and Paola Battilani*
Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, Italy
Meteorological conditions are the main driving variables for mycotoxin-producing
fungi and the resulting contamination in maize grain, but the cropping system used
can mitigate this weather impact considerably. Several researchers have investigated
cropping operations’ role in mycotoxin contamination, but these findings were
inconclusive, precluding their use in predictive modeling. In this study a machine
learning (ML) approach was considered, which included weather-based mechanistic
model predictions for AFLA-maize and FER-maize [predicting aflatoxin B1(AFB1) and
fumonisins (FBs), respectively], and cropping system factors as the input variables. The
occurrence of AFB1and FBs in maize fields was recorded, and their corresponding
cropping system data collected, over the years 2005–2018 in northern Italy. Two deep
neural network (DNN) models were trained to predict, at harvest, which maize fields
were contaminated beyond the legal limit with AFB1and FBs. Both models reached an
accuracy >75% demonstrating the ML approach added value with respect to classical
statistical approaches (i.e., simple or multiple linear regression models). The improved
predictive performance compared with that obtained for AFLA-maize and FER-maize
was clearly demonstrated. This coupled to the large data set used, comprising a 13-year
time series, and the good results for the statistical scores applied, together confirmed
the robustness of the models developed here.
Keywords: aflatoxins, Aspergillus flavus, cropping system, deep learning, Fusarium verticillioides, fumonisins,
predictive models
INTRODUCTION
Mycotoxin contamination of maize is a major concern worldwide (Eskola et al., 2020). The
colonization of maize ears by Aspergillus section Flavi and Fusarium spp. can lead to ear rots whose
impact on the amount of grain yield is minor or negligible yet their mycotoxin contamination levels
are high; therefore, the mains impact of mycotoxin producing fungi in maize regards grain safety
and its compliance with the legal limits. Concerning those mycotoxins produced by Aspergillus
section Flavi, among the aflatoxins (AFs), aflatoxin B1(AFB1) is classified by IARC (International
Agency for Research on Cancer) as a class-1A, human carcinogen. Such AFs were first detected in
Italy in the early 2000s (Piva et al., 2006;Battilani et al., 2008a), but since 2012 they have spread
all over southeastern Europe, presumably aided by warmer and drier conditions during summer
attributed to ongoing climate change (Dobolyi et al., 2013;Levic et al., 2013;Battilani et al., 2016),
and their incidence and severity can vary markedly among years. Fusarium spp. can produce a
wide range of mycotoxins, of which the fumonisins B1, B2, and B3(FBs)—predominantly produced
by F. verticillioides—are the key ones reported in maize grain worldwide, thus posing a serious
risk of possible human carcinogenicity (IARC, 1993). Other mycotoxins produced by Fusarium
genus are the trichothecenes (TCTs) and zearalenone (ZEN), these being prevalent in temperate
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Camardo Leggieri et al. Maize Mycotoxins and Machine Learning
and wet areas especially in rainy years, optimal conditions for
their main producer, F. graminearum (Pietri et al., 2004).
These main mycotoxins threaten the maize supply chain
worldwide, in all producing areas; nevertheless, the prevailing
mycotoxin and level of contamination depends both on the
growing area and year, intended as the meteorlogical conditions
occurring during the crop growing season (Logrieco et al.,
2021). Support for farmers coming from predictive modeling,
using meteorological data as input variables (Battilani, 2016),
has been pursued in Europe in the form of two mechanistic
models for AFB1and FBs predictions: respectively, AFLA-maize
(Battilani et al., 2013) and FER-maize (Battilani et al., 2003).
They both aim to predict the risk of contamination above current
legal limits in force in Europe (European Commission, 2006b,
2007), and they strongly support stakeholders in the maize chain
management (Battilani and Camardo Leggieri, 2015;Battilani,
2016;Palumbo et al., 2020). However, mounting uncertainty
of climate conditions and extreme events, often emphasized as
issues in climate change, has recently increased the importance of
deriving reliable predictions at the farm level (Camardo Leggieri
et al., 2020a). Addressing the variability in mycotoxin occurrence
among years and geographic areas, even those quite close to
each other, in addition to the emerging issue of co-occurring
mycotoxins (Camardo Leggieri et al., 2019;Giorni et al., 2019),
will require making reliable predictions to support the maize
chain management.
Weather variables are the leading factors contributing to
mycotoxin occurrence, but the cropping system used is a
powerful tool of farmers to mitigate grain contamination.
Accordingly, several authors have studied the role of the cropping
system and the rationale behind its impact on mycotoxin
contamination (Munkvold, 2003;Battilani et al., 2008a;Blandino
et al., 2009;Kos et al., 2013;Palumbo et al., 2020). A rationale
crop rotation, leaving the land fallow (unsown with maize),
is recommended to reduce mycotoxin contamination in maize
fields, even if the impact on it cannot be readily demonstrated,
especially in intensive maize-growing areas (Guo et al., 2005;
Marocco et al., 2008;Munkvold, 2014). A significant impact of
the season length of maize hybrids, frequently reported as FAO
class (Food and Agriculture Organization classification), upon
FBs and AFB1contamination was reported (Pietri and Bertuzzi,
2012). Scientist do not all agree on this statement (Battilani et al.,
2008a;Mazzoni et al., 2011), but the number of days elapsed
from sowing to harvest was positively related to mycotoxin
contamination (Battilani et al., 2008a;Torelli et al., 2012). The
sowing date has been confirmed to influence the likelihood and
extent of mycotoxin contamination (Jones, 1981;Alma et al.,
2005), with late sowing generally associated with a higher content
of mycotoxins at harvest (Alma et al., 2005;Blandino et al.,
2009;Mazzoni et al., 2011). Nonethless, irrigation has a strong
impact as well, particularly upon AFs occurrence (Palumbo et al.,
2020). Both the severity of European corn borer (ECB) (Jones,
1981) and the use of insecticide treatments may also significantly
affect contamination, especially from FBs (Alma et al., 2005;
Saladini et al., 2008;Mazzoni et al., 2011). Harvest time, or rather
the kernel moisture at harvest, can also be crucial, notably for
AFs contamination of maize (Munkvold, 2003;Battilani et al.,
2008a); in fact, AFs production increases significantly from maize
physiological maturity, when kernel moisture is lower than 28–
30% (Payne et al., 1988;Giorni et al., 2016). Therefore, delays
in maize harvest after that stage means giving the fungus time
to efficiently increase the contamination. Then, keep kernel
moisture below 14% is mandatory in the postharvest stages, from
drying to the whole storage period (Danso et al., 2018).
The above research findings have contributed to developing
guidelines for mitigating mycotoxin contamination, but
a quantitative evaluation of cropping system’s impact on
mycotoxins remains an unresolved issue. The little work done
so far to predict the effect of cropping system on mycotoxin
contamination (Battilani et al., 2008b;Camardo Leggieri et al.,
2015) is neither complete nor satisfactory; however, it does clarify
that cropping factors cannot be considered in isolation and that
applying conventional statistical methods is not suitable for the
task (Battilani, 2016;Palumbo et al., 2020). Hence, alternative
approaches should be explored and possibly used.
Machine learning’s emergence, alongside big data technologies
and high-performance computing, introduces new opportunities
for data-intensive science in precion farming and sustainable
agriculture (Liakos et al., 2018). ML is the scientific field in
which machines are trained to learn without being strictly
programmed (Samuel, 2000), which has three main categories:
(1) supervised learning (SL), (2) unsupervised learning (UL),
and (3) reinforcement learning (RL). The SL algorithms use a
training data set of labeled data to infer a function that is used
to map new data. The UL algorithms directly look at the data
and learn patterns from them, without human supervision. Last
is RL, the ML branch in which entities called “software agents”
take action, in a specific context, to optimize a given function.
These ML approaches are increasingly applied in different subject
areas to solve complex problems, often those with many factors
involved, to which agriculture is no exception. In fact, ML is
used in a variety of contexts and all the three main categories
are now applicable (Liakos et al., 2018;Elavarasan and Vincent,
2020). Recently, Liakos et al. (2018) reviewed the ML approach
in agriculture, highlighting that ML models had been applied
in the multi-disciplinary agri-technologies domain for crop
management (61%), yield prediction (20%), and disease detection
(22%), but never accounting specifically for mycotoxins’ co-
occurrence. In crop yield prediction, which depends on many
different factors operating simultaneously, deep neural networks
(DNN), a type of artificial neural network (ANN) for SL models,
are the most used (Khaki and Wang, 2019;Khaki et al., 2019;
Nevavuori et al., 2019;Niedbała, 2019). DNNs are also very useful
for plant disease identification, which is done via convolutional
neural networks (CNN), which is a specific DNN-architecture
used for image recognition (Boulent et al., 2019). Mycotoxins are
mainly detected via high-performance liquid chromatography
(HPLC) and mass spectrometry; however, DNNs coupled with
rapid analytical tools, such as the electronic nose or infrared
attenuated total reflection spectroscopy, have been recently
applied and found to improve the assessment reliability (Evans
et al., 2000;Jia et al., 2019;Öner et al., 2019;Camardo Leggieri
et al., 2020b).
Torelli et al. (2012), in the first example of ML applied to
mycotoxins, performed a 2-year study (2007–2008) that included
seven cropping system variables—FAO class, sowing and harvest
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Camardo Leggieri et al. Maize Mycotoxins and Machine Learning
dates, crop duration, kernel moisture, ECB treatment, and
irrigation—as input for an ANN to classify maize samples based
on their contamination with FBs. A fair correlation between
the predicted and observed contaminated samples was reported
(R2= 0.67 and R2= 0.57, respectively, for the training and
validation data sets), so the approach seems promising.
Therefore, this study aimed to develop ML models by
combining the AFLA-maize and FER-maize predictions, together
with cropping system information, as input to improve the
mycotoxin risk predictions for AFB1and FBs. For this, A 13-year
data set was considered and two ML models, one for each type of
mycotoxin, were trained and validated (5-fold cross-validation)
and their respective performance discussed.
MATERIALS AND METHODS
Data Collection
The data used in this study came from several surveys managed
in the Emilia Romagna region (northern Italy) during 2004–2018,
partially published (Battilani et al., 2013;Camardo Leggieri et al.,
2015, 2020b). The protocol for data collection was the same both
for the published and unpublished data.
Briefly, meteorological data were downloaded from the
Emilia Romagna meteorological service, available on request for
research applications. They were based on a grid of squares, each
5 km wide, that encompassed the Emilia Romagna territory; all
sources (both meteorological stations and radar) are interpolated
for each square, to deliver reliable data (Bottarelli and Zinoni,
2002). Hourly data on air temperature (T, ◦C), relative humidity
(RH, %), and rain (R, mm), during the period of January through
September, were downloaded. These squares were filtered, to
locare those corresponding to the maize field site sampled.
Maize fields sampling was performed at harvest, managed
between mid August and September, during the combine
machine discharge, according to European Commission
Regulation (EU) 401/2006 (European Commission, 2006a).
Relevant cropping system data were collected in each
georeferenced field, based on a questionnaire filled by farmers,
supported by extension services. Empirical information
of different site variables were collected: the type of soil
(percentages of sand, clay, and silt), maize hybrid FAO class,
preceding crop, type of tillage, sowing date, plants per m2, silk
emission date, harvest date, damage caused by hail or wind
and ECB, fertilization type and dose, the number of irrigation
intervention with relative volumes of water used, pest and disease
control practices, and kernel moisture at harvest. Mycotoxin
analysis was performed for all the sampled fields according to
Bertuzzi et al. (2012) for the AFs [limit od detection (LOD): 0.05
µg/kg and limit of quantification (LOQ): 0.15 µg/kg], and by
following Pietri and Bertuzzi (2012) for the FBs (LOD: 10 µg/kg
and LOQ: 30 µg/kg).
Data Processing
AFB1and FBs content allowed in maize grain, according to
legal limits, were used as a threshold to separate the field
samples in two classes: (1) contaminated, consisting of those
samples equal or exceeding the respective legal limit; (0) non-
contaminated comprising all samples below the legal limit.
Thresholds were therefore set to 5 µg/Kg for AFB1and 4,000
µg/Kg for FBs (FB1+FB2), the legal limits currently set by
the European Commission for unprocessed maize destined for
human consumption (European Commission, 2006b, 2007).
Meteorological data were used as input for the two predictive
models, AFLA-maize and FER-maize, and cumulative risk
indexes were obtained as the output, AFI for AFB1and FK for
FBs, for each station and year. DNN models were implemented
within the frame of Scikit-Learn (v0.21.3) in the Python module
library (Pedregosa et al., 2011).
Input Features
After the exclusion of those variables with many missing data
points, eight different variables were considered as input for the
ML approach; sowing date and harvest date were grouped on
a per week basis. Of these eight, five variables were categorical
(maize hybrid FAO class, preceding crop, sowing week, harvest
week, ECB damage; Table 1) and three were continuous variables
(growing days, days from crop sowing to harvest, calculated
variable, kernel moisture at harvest, and mycotoxin cumulative
indices AFI and FK, the output of predictive models).
For each continuous variable, its average (µ) and standard
deviation (σ) were computed for the data standardization, using
Eq. (1).
X=X0−µ
σ
(1)
TABLE 1 | Summary of categorical data used for the two pathosystems analyzed:
A. flavus-maize and F. verticilloides-maize.
Variable N. of categories Categorical value Integer
encoding
Maize hybrid FAO class 4 200–300 1
400 2
500 3
600–700 4
Preceding crop 3 arable crops 1
small grain 2
maize 3
Sowing week* 4 10–12 1
13–14 2
15–16 3
17+4
Harvest week* 4 32–35 1
36–37 2
38–39 3
40+4
Severity of ECB attack 3 No/Minor-damage 1
Medium damage 2
Severe damage 3
Variables (hybrid FAO classes, preceding crop, and sowing and harvest weeks of
the year (Julian date) and European corn borer [ECB] damage) and numbers of
utilized categories are reported.
*nth week of the year.
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Even if this procedure is entirely facultative in neural network
training, it is nonetheless useful for reducing the variance,
speeding up the computational process, and improving the
model’s accuracy (Jin et al., 2015).
To deal with categorical data, the integer encoding procedure
was applied. This assigns to a specific category an integer value
that ranges from 1 to N, where N is the last category.
Deep Neural Network (DNN)
Development
A typical ANN consists of a network of connected computational
units called neurons; these units are organized in layers, with
input data passing through the network, and an activation
function used to produce an output. DNNs are a particular
class of ANNs in which, between the input and the output
layer, there is an arbitrary number of hidden layers. The fully
connected architecture has been adopted, meaning that each
neuron in each layer is connected to each neuron in the
next adjacent layer.
The development of a DNN model is a two-step process:
(i) training and (ii) validation. The final aim of the training is
to minimize a given error function by using an optimization
algorithm. The training phase ends when the error converges
to a pre-determined value, or when it does not decrease for
a specific number of cycles, both decided a priori by the
user (Camardo Leggieri et al., 2020b). A “batch training”
mode was applied in this work. Briefly, during the training
phase, training data (i.e., the mycotoxin contamination data
in this study), were split into subgroups called batches. Neural
network weights are updated when every sample inside a
batch passes through the network. The iteration ends when all
batches have passed through the network. After each iteration,
a penalization term, called the weight decay (L2 regularization
term), is introduced into the model to avoid overfitting the
model to the data.
The final output is the result of a linear or non-linear
activation function. In our study, a non-linear activation function
between the input layer and the hidden layers, called Rectified
Linear Unit (ReLU, Eq. 2), was applied:
f(x)=0,x≤0
x,x>0(2)
where, xrepresents the weighted sum in a given input to a neuron
(Dahl et al., 2013;Zeiler et al., 2013;LeCun et al., 2015).
The activation function used between the last hidden layer and
the output layer (classification function) took the logistic form
(Eq. 3):
f(x)=1
1−e−ϑPN
i=1xiwij
(3)
where, jis relative to the jth output neuron, and irepresents the ith
input neuron. The numerical result is between 0 and 1, for which
0.5 served as a threshold to discriminate between the positive and
negative classes.
Different DL models were tested following a grid search
procedure, done as described in Camardo Leggieri et al. (2020b).
Briefly, each hyperparameter was tested with a combination of
every other hyperparameter. The hyperparameters tested were
weight decay, number of hidden layers, number of neurons per
hidden layer, and the optimization algorithm. In this work, two
algorithms where tested; the first approximates the Broyden–
Fletcher–Goldfarb–Shanno algorithm and is called LBFGS (Byrd
et al., 1995;Kingma and Ba, 2014), while the second is an
optimization of the classical stochastic gradient descent, called
Adam (Kingma and Ba, 2014). Matthew’s correlation coefficient
(MCC) and accuracy were used as metrics to select the best
combination of hyperparameters, for both NN models relevant
to the pathosystem A. flavus-maize (DNN-A. flavus-maize) and
F. verticillioides-maize (DNN-F. verticilloides-maize).
DNN Validation
As in Camardo Leggieri et al. (2020b), both AFB1and FBs
original datasets were splitted into two “sub-dataset.” These four
“sub-datasets” (two for AFB1and two for FBs) were generated by
random sampling, but keeping the proportion of contaminated
vs. non-contaminated samples constant. The first “sub-data set”
accounted for 75% of the original data set and was used to
perform a 5-fold cross-validation (CV). The other, called the
“blind set” in this work, accounted for the rest of the original
data set (25% of the original data set), and was used for a
further validation of the models. The goodness-of-fit of each
DNN-A. flavus-maize and DNN-F. verticilloides-maize model
was assessed by computing several statistical scores, which were
also applied to the blind set:
•True positive and true negative rates (TPR, TNR; Kohavi
and Provost, 1998);
TNR =TN
TN +FP (4)
where, TP and TN denote the number of true positives and
true negatives, respectively, and likewise FP and FN denote the
number of false positives and false negatives.
•Positive predictive value (PPV): the PPV (Eq. 5) index
represents the proportion of positives samples identified as
true positives. It ranges from 0 to 1 (Kohavi and Provost,
1998)
PPV =TP
TP +FP (5)
•Receiver operator characteristic (ROC) curve and area under
the curve (AUC): the ROC curve and its AUC measure the
quality of a binary classifier: the higher the area under the
curve, the better the model performs. The AUC value ranges
from 0 to 1(Bradley, 1997).
•The MCC (Eq. 6) is used to assess the quality of binary
classification (Matthews, 1975). This index takes into
consideration TPR, TPN, and both false discoveries (false
positives and negatives). The MCC ranges between –1
(complete disagreement between predicted and observed
values) and +1 (perfect agreement). The MCC is
considered a balanced measure, and it can be used
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TABLE 2 | Descriptive statistics of aflatoxin B1 (AFB1) and fumonisins (FBs,
intended as the sum of FB1+FB2) levels of contamination (µg/kg) in maize grain
samples collected in Emilia Romagna, Italy, over the years 2005–2018 (with some
exceptions both for AFB1and for FBs).
Data set Year N.* %positives§Mean StDev Minimum Maximum
AFB1
2005 70 41.4 13.8 29.65 <0.05 154.9
2006 25 24.0 18.8 52.78 <0.05 258.3
2007 29 27.6 8.34 18.50 <0.05 68.43
2008 40 40.0 9.11 16.99 <0.05 93.79
2009 31 29.0 23.3 88.51 <0.15 494.3
2010 35 28.0 14.4 36.60 <0.05 173.3
2011 31 12.9 14.9 61.03 <0.05 334.8
2014 26 23.1 10.2 23.17 <0.15 93.77
2015 15 20.0 11.2 32.34 <0.05 129.3
2016 20 45.0 30.4 62.33 0.42 208.3
2017 28 50.0 22.3 29.15 <0.05 116.2
2018 28 25.0 5.76 13.09 <0.05 65.00
Total 378 35.54 14.66 42.59 <0.05 494.3
FB1+FB2
2009 31 16.1 2,721.93 2,423.335 139.3 8,829.7
2010 36 40.0 3,975.79 3,221.642 142.7 12,637.0
2011 30 6.67 2,344.87 3,689.235 74.0 21,007.0
2014 45 84.4 17,586.88 18,300.650 1718.4 106,053.5
2016 21 19.0 3,035.33 3,251.853 204.3 14,020.8
2017 29 13.8 2,643.91 3,107.761 <10.0 14,767.4
2018 33 21.2 3,846.22 6,298.140 51.8 29,632.7
Total 225 38.22 6,029.34 10,566.241 <10.0 106,053.5
The percentage of positive samples above the legal limits (5 and 4,000 µg/kg for
AFB1and FBs, respectively; European Commission, 2006a,2007) is reported.
*N. = number of samples collected in the year; §% of positive = % of fields with
contamination above the legal limits.
even if the two classes differ in size (Boughorbel et al.,
2017).
MCC =TP ×TN −FP ×FN
2
√(TP +FP)(TP +FN)(TN +FP)(TN +FN)(6)
All the scores were computed using a home-built Python (v3.6.9)
script that implemented the equations reported above. The ROC
curves and AUCs, the grid search procedure, the MCCs, and the
DNN architecture were implemented in the framework of scikit-
learn (v0.23.2; Pedregosa et al., 2011).
The output indexes of the two mechanistic models (AFI and
FK) were used to classify the blind data set, as described in
Battilani et al. (2003) for the FBs and in Battilani et al. (2013) for
AFB1. Finally, the results were compared using the classification
obtained by the two DNNs.
RESULTS
A total of 378 and 225 samples were included in the A. flavus-
maize and F. verticilloides-maize data sets, respectively (Table 2).
No data were retrieved for FBs before 2009 or during 2012 and
2013. Concerning the AFB1, data for it were not retrieved for
the years 2012 and 2013. The sample sizes per year were slightly
different for the two data sets because of this missing data.
Fields with AFB1contamination above 5.0 µg/kg were found,
whose incidence and mean values differed across the considered
years. The highest amount of AFB1was found in 2009, at 494.3
µg/kg. In all years AFB1was greater than LOQ, but lower than
LOD, except in 2009, 2014, and 2016. Regarding the incidence of
fields found positive for AFB1, this was highest at 50% in 2017
and the lowest (12.9%) in 2011 (Table 2). In the whole maize data
set, for AFB1, the mean incidence of positive samples was 32.9%.
Fields with FBs’ contamination above 4,000 µg/kg were found
in all the years considered, but this incidence differed across
all years. The only year when the FBs was below the LOD was
in 2017. The highest amount of FBs (106,053.5 µg/Kg) and
the highest incidence of positive samples (89.8%) were both
detected in 2014. The lowest incidence of FBs was 6.7%, scored
in 2011 (Table 2). Considering the FER-maize data set as a whole,
the overall mean incidence of positive samples, above the legal
limit, was 32.2%.
Means and standard deviations computed for AFI and FK, the
kernel moisture, and the growing days, are reported in Table 3.
Categorical data were grouped into three or four categories
(Table 1). The FAO class was represented by four categories:
200–300, 400, 500, and 600–700. The preceding crop was
represented by three categories: arable crops, small grain, and
maize. The sowing and harvest weeks both accounted for
four categories. Considering the sowing week, category #1 was
assigned to weeks 10–12 of the year, #2 to weeks 13–14,
#3 to weeks 15–16 and #4 to week ≥17. For the harvest
week, those weeks of the year from 32 to 35 were designated
category #1, and likewise 36–37 to # 2, 38–39 to #3, with all
weeks >40 assigned to #4. The damage caused by ECB was
divided into three categories: no damage and small damage
were grouped into category #1, medium damage was assigned
to category #2, and severe damage was assigned to category #3
(Camardo Leggieri et al., 2015).
Standardized continuous data were joined to categorical data,
to form the neural network’s input vector; thus, the final input
array was formed by five encoded and three continuous variables
(Tables 1 and 3), respectively.
The generation of the “sub-datasets” included a total 283
(75%) and 95 (25%) samples in the CV and blind set respectively
TABLE 3 | Basic statistics of the continuous data included as the input into the
model for the two pathosystems, A. flavus-maize and F. verticillioides-maize.
Model Variable Mean StDev Maximum Minimum
A. flavus-maize AFI index 2,906.17 2,226.792 8,944.5 11.6
Kernel moisture
(%)
20.66 3.541 31.5 11.9
Growing days 158.0 16.68 234 66
F. verticilloides-
maize
FK index 246,407.4 626,994.91 537,8545.3 2,102.3
Kernel moisture
(%)
19.99 3.712 31.5 11.8
Growing days 156.6 16.30 207 115
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for AFB1, and 169 and 56 samples in the CV and blind set
respectively for FBs.
Deep Neural Network (DNN) Training
The two DNNs were trained to be able to predict the content of
AFB1and FBs, respectively. The NN-A. flavus-maize model was
implemented with one hidden layer consisting of 80 neurons,
a ReLU activation function, and an L2 regularization term of
0.0001. The parameters of that model were updated using the
Adam algorithm (Kingma and Ba, 2014). By contrast, the NN-
F. verticilloides-maize model’s sole hidden layer had 50 neurons, a
ReLU activation function, and an L2 regularization term of 0.1. Its
parameters were updated using the LBFGS algorithm (Byrd et al.,
1995;Table 4). The two developed NN models were validated
using both the 5-fold cross-validation and a blind data set.
DNN Validation
Cross-validation ROC curves and their relative AUCs were
computed for the two NN models, to assess the quality of
the two classifiers (Figures 1A,B). The 5-fold-cross-validation
for NN-A. flavus-maize achieved an accuracy of 66.56 ±3.381
(mean ±SE), and even higher at 78.94 for the blind data set.
An MCC of 0.10 ±0.157 and 0.49 were achieved by the cross-
validation and blind data set, respectively. The AUC and the TPR
averaged 0.58 ±0.063 and 0.08 ±0.073 during the model’s cross-
validation. The model scored an AUC of 0.64 and a TPR of 0.42
when tested against the blind data set (Table 5).
The NN-F. verticilloides-maize model’s 5-fold cross-validation
attained an accuracy of 69.63 ±10.892; the accuracy was 79.31%
using the independent data set. A TPR of 0.53 ±0.118 and
0.65 were, respectively, achieved by cross-validation and blind
data set, respectively. Moreover, the model had an AUC of
TABLE 4 | Hyperparameter values used to implement the neural networks
A. flavus-maize (NN-A. flavus-maize) and F. verticilloides-maize
(NN-F. verticilloides-maize) models.
Hyperparameter NN-A. flavus-maize NN-F. verticilloides-maize
Number of input
neurons
7 7
Number of hidden
layers
1 1
Number of neurons
per hidden layer
80 50
Activation function
input—hidden
ReLU (Eq. 1) ReLU (Eq. 1)
Activation function
hidden—output
Logistic (Eq. 2) Logistic (Eq. 2)
L2 regularization
term
0.0001 1.024
Parameters update
algorithm
Adam LBFGS
The number of input neurons is defined as the sum of the categorical and
continuous data. The hyperparameter values were selected by following a grid
searching procedure.
0.72 ±0.103 and an MCC of 0.35 ±0.229 during the cross-
validation phase, with corresponding values of 0.75 and 0.56
when tested against the unseen data set (Table 4).
Finally, to check whether the new approach represented a
major step forward in the prediction of mycotoxin contamination
in maize, our two DNN-models were compared with two
counterpart mechanistic models, both run with the whole
available data set. The resulting confusion matrix (Table 6)
shows the performances of the NN-A. flavus-maize and NN-
F. verticilloides-maize models and those of the two mechanistic
models vis-à-vis the blind data set. The NN-A. flavus-
maize model correctly estimated about 78% of samples (14%
true positives, 64% true negatives). The wrong classification
accounted for 19% of them being underestimated and 3%
overestimated. The NN-FER-maize model correctly classified
approximately 75% of the data set (25% true positives, 50%
true negatives), with underestimations and overestimations
amounting to 15 and 11%, respectively. In stark contrast,
the AFLA-maize model correctly predicted just ∼53% of
samples (11% true positives, 42% true negatives). Further,
a wrong classification accounted for more underestimations
(22%) and overestimations (25%). Similarly, the FER-maize
model correctly classified only ∼52% of samples (31% true
positives, 20% true negatives), but its wrong classifications
included fewer (7%) underestimated cases being more prone to
overestimations (41%).
DISCUSSION
Maize is exposed to mycotoxins, which threaten human and
animal health, and represent the major non-tariff trade barrier for
agricultural products, negatively affecting the income of small-
holder farmers and disrupting regional and international trade
(Palumbo et al., 2020;Logrieco et al., 2021). Timely identification
of contaminated lots is not a trivial challenge since mycotoxin
contamination relies on several factors, including meteorology
and how farmers manage the crop during the season and in
the postharvest stages of storage and distribution (Munkvold,
2014;Logrieco et al., 2021). Different methodologies for the rapid
detection of mycotoxin contamination are currently available
(Öner et al., 2019;Camardo Leggieri et al., 2020b), but since
they are applied at harvest or postharvest stages, they offer no
support for taking preventive action and for optimizing lot use
and management. On the contrary, farmers can benefit from
model predictions of the risk of mycotoxin occurrence above the
legal limit, when delivered before or during the cropping season,
in the form of risk maps or risk indexes. Therefore, predictive
modeling has garnered mounting interest over the last two
decades (Battilani, 2016). Predictions refer to maize at harvest
and it is assumed that the postharvest management guarantee a
rapid grain drying to humidity ≤14%, kept stable during storage,
to avoid fungal activity and further mycotoxin production.
Meteorological factors jointly determine whether fungi can
grow and produce toxins, while the site’s cropping system
modulates the amount of contamination that ensues (Battilani
and Camardo Leggieri, 2015). The former are the driving
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Camardo Leggieri et al. Maize Mycotoxins and Machine Learning
FIGURE 1 | Receiver operating characteristics (ROC) curves for the independent data set for the (A) aflatoxin B1 and (B) FBs models. The solid blue lines represent
the ROCs for the two models. The goodness-of-fit of the models is conveyed as the area under the curve (AUC): the higher it is, the better the model performed. The
dotted red line represents the random prediction.
TABLE 5 | Classification results summary for the prediction of aflatoxin B1(AFB1)
and fumonisins (intended as the sum of FB1+FB2, FBs) in the maize samples.
CV Blind data set
(DNN-models)
Blind data set
(mechanistic
models)
AFB1
ACC 66.56 ±3.381 78.94 52.63
TPR 0.08 ±0.073 0.42 0.32
TNR 0.94 ±0.053 0.96 0.63
PPV 0.59 ±0.424 0.90 0.29
MCC 0.10 ±0.157 0.49 –0.051
FBs
ACC 69.63 ±10.892 79.31 52.63
TPR 0.53 ±0.118 0.65 0.81
TNR 0.80 ±0.122 0.88 0.34
PPV 0.63 ±0.175 0.78 0.44
AUC 0.72 ±0.103 0.75 n.c.
MCC 0.35 ±0.229 0.56 0.17
The deep neural networks (DNNs) considered were evaluated using a 5-fold cross-
validation and a blind data set. Cross-validation (CV) results were reported as an
average value and related standard deviation (Avg ±Stdev).
ACC, accuracy; MCC, Matthew’s correlation coefficient; AUC, area under the
curve; TPR, true positive rate; TNR, true negative rate; PPV, positive predictive
value; n.c., not computed.
variables for predictive modeling, whereas the latter are rarely
included, especially in mechanistic models. This omission is
starting to gravely limit the reliability of predictions; in fact,
during the last two decades, the typical cropping system
has changed significantly due to the knowledge transfer
from scientists to farmers; farmers are now following the
guidelines to optimize crop management and mitigate mycotoxin
contamination, with good results so far in term of a reduced
mycotoxin occurrence. Both the meteorological data and
the cropping system data have been used before as model
inputs, to predict the content of mycotoxin in maize at the
time of its harvest (Battilani et al., 2003, 2008a;Bertuzzi
et al., 2014;Camardo Leggieri et al., 2015), yet they were
used independently and only supported by basic statistical
approaches. The aim of this study was to evaluate how
combining cropping system information with mechanistic
predictive models could support the sought-after improvement
in prediction performance.
Here an ML approach was developed using the AFLA-maize
and FER-maize outputs (mycotoxin risk indexes) combined with
cropping system information—this being known to significantly
influence mycotoxin contamination in maize according to
other studies (Palumbo et al., 2020;Logrieco et al., 2021)—as
input variables. Other crop-related variables should have been
included, like fertilization, irrigation, and pest control (Mazzoni
et al., 2011;Munkvold, 2014); however, we excluded them
because this data was largely unavailable to us. Moreover, the
geolocation of maize fields was excluded as an input variable in
our modeling; actually, even when the field location is known to
be relevant (Torelli et al., 2012;Camardo Leggieri et al., 2015), the
idea of this work was to obtain models applicable at a global level,
without geographical constraints. When combining weather data
and cropping system no information is lost, even when the maize
fields’ geolocation is excluded.
The predictions of NN-A. flavus-maize and NN-
F. verticilloides-maize, the two neural network models developed
in this study, were capable of an accuracy approaching ∼78%
for AFB1and ∼79% for FBs, with a good correlation between
TABLE 6 | Confusion matrix computed from the blind data set results for the
predicted and observed values of aflatoxin B1 (AFB1) and fumonisins (intended as
the sum of FB1+FB2, FBs). The predicted vs. observed results are
reported as percentages.
DNNs Predicted
Observed Negative Positive
AFB1Negative 65 2
Positive 19 14
FBs Negative 53 7
Positive 14 26
Mechanistic models
AFLA-maize Negative 42 25
Positive 22 11
FER-maize Negative 21 41
Positive 7 31
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Camardo Leggieri et al. Maize Mycotoxins and Machine Learning
predicted and observed data. This result is supported by the MCC
results, which reached values of 0.49 and 0.56 when computed
for the NN-A. flavus-maize and NN-F. verticilloides-maize, and
by their corresponding AUC of 0.64 and 0.75 for NN-A. flavus-
maize and NN-F. verticilloides-maize. Both AFLA-maize and
FER-maize, the mechanistic models which served as the starting
point of our investigation, achieved accuracies one-third lower,
of about 50%, and their respective MCC was very close to 0; this
indicates that their predictions were comparable to random one,
when based on the same data set (i.e., the blind data set) used for
NN model evaluation. It is, therefore, evident the proposed ML
approach significantly improved the prediction of mycotoxins’
content across the studied maize fields, making the successful
use of this tool to detect maize grain not compliant with the
current legal limit in Europe now more realistic and feasible to
implement. Nevertheless, the NN-F. verticillodes-maize model
performed better than NN-A. flavus-maize; apparently, it is
easier to predict levels of FBs than AFB1as contaminants. The
reason for this is not entirely clear, but the very low limit fixed
by the legislation for AFB1could surely play a role. Further,
irrigation is known to be very relevant for AFB1contamination,
and grain humidity at harvest too, but they were excluded as
input variables because of many missing data and this has surely
a considerable impact on the predictive capacity of the model.
The NN-models were developed based on a large data
set, one that included data collected from over 12 different
years, with 378 samples in the AFB1data set and 225
samples in the FBs data set. The large data sets used,
and the range of years considered, including the period of
significant changes in the cropping system, strongly support
the robustness of NN-A. flavus-maize and NN-F. verticilloides-
maize models and their promising utility as a tool to support
farmers in their decision-making. Future applications in other
pathosystems is also foreseeable, as previously done for
AFLA-maize (Kaminiaris et al., 2020).
CONCLUSION
To conclude, despite omitting some relevant cropping system
variables, a substantial improvement at correctly predicting
maize fields contaminated with mycotoxins above their legal
limits was gained. Further improvements should be obtained
by optimizing the data collection. Solving the missing data
problem might be an easy task in the future, once scientists
succeed in convincing farmers of the crucial role they can play
in such data collection and also of the added value of predictive
models in mycotoxin management. This will be a matter of
building with the maize chain stakeholders a knowledge exchange
approach and make them more involved compared to what
actually happened in the past. Another aspect of improvement
could be gleaned through our modeling approach, that of
emerging issues related to mycotoxins. The main example of
this concerns the recent report of co-occurring A. flavus and
F. verticilloides in maize ears due to climate change effects,
resulting in their complex interaction, with their dominance
alternating during the growing season (Camardo Leggieri et al.,
2019, 2020a;Giorni et al., 2019). Looking ahead, we anticipate
that elucidating the impact of these interactions between co-
existing fungi upon mycotoxin production in maize will become
crucial. Data collection to develop a joint model for the prediction
of AFB1and FBs, including the impact of fungi co-occurrence,
is ongoing and in the next future it is expected to contribute
to a step up in mycotoxin prediction, possibly joining also NN-
A. flavus-maize and NN-F. verticilloides-maize in a NN-mycotox-
maize predictive model.
The current work represents a notable step forward in
modeling and predicting mycotoxins in crops. We retrieved
evidence that ML can effectively combine cropping system data
and meteorological data, thereby improving the accuracy and
robustness of predictions. Big data is a relatively new concept
for agriculture and plant disease research and management,
but massive volumes of data with several components that
interact within the pathosystem can also be captured in this
context, and their elaboration can enhance the decision-making
process (Wolfert et al., 2017). Applying machine learning to farm
management systems is quickly evolving into a real artificial
intelligence (AI) system, providing richer recommendations and
insight for subsequent decisions and timely actions (Liakos
et al., 2018). Further research that aims to integrate automated
data recording, mycotoxin analysis, ML implementation, and
decision support systems will provide practical tools in line with
so-called “knowledge-based agriculture.” This should move us
closer toward sustainable agriculture and smart farming that also
improves food safety and quality.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation, to any
qualified researcher.
AUTHOR CONTRIBUTIONS
PB, MC, and MM contributed to the conception and design of
the study and wrote sections of the manuscript. MC organized
the database. MM performed the statistical analysis. MC and
MM wrote the first draft of the manuscript. All authors
contributed to manuscript revision, read it, and approved the
final submitted version.
FUNDING
This study was partially supported by the regional
government, Emilia Romagna region, “SERVICE – SistEmi
infoRmatiVi rIschio miCotossinE” (IT systems for mycotoxin
risk) No. 5149128.
ACKNOWLEDGMENTS
We are grateful to the Emilia Romagna meteorological web
service for providing meteorological data, to CRPV for the
project coordination and to all the farmers and technicians who
contributed to maize samples collection.
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Camardo Leggieri et al. Maize Mycotoxins and Machine Learning
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2021 Camardo Leggieri, Mazzoni and Battilani. This is an open-access
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Frontiers in Microbiology | www.frontiersin.org 10 April 2021 | Volume 12 | Article 661132
