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Crop Harvest Forecast via Agronomy-Informed Process Modelling and Predictive Monitoring

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Abstract

Reliable and timely forecasts on crop harvest bring significant benefits to agri-food industries by providing valuable input to complex decisions on production planning. Useful predictions on crop harvest require continual effort by seasoned field agronomists. However, they are often scarce resources in the real-world. A feasible way to facilitate crop harvest forecast is through developing predictive models that can exploit data relevant to crop growth and automatically generate consistent predictions. To this end, this paper presents our design of a systematic and data-driven approach to supporting online forecasts on crop harvest. Underpinned by process modelling and predictive monitoring techniques, our approach can utilise crop-growth-related information from multiple data sources and progressively generate crop harvest predictions within the crop growing season. The approach has a flexible design informed by agronomic knowledge applicable to crop growth in general, and may be tailored to different crops and production scenarios. A case study with a local farming company using its real-life production data demonstrates the feasibility and efficacy of our approach.
Crop Harvest Forecast via Agronomy-informed
Process Modelling and Predictive Monitoring
Jing Yang1,2[0000000192186954] , Chun Ouyang1,2[0000000170985480],
uven¸c Dik1,2[0000000208598540], Paul Corry1,2[0000000333135967] , and
Arthur H.M. ter Hofstede1[0000000227300201]
1Queensland University of Technology, Brisbane QLD 4000, Australia
{roy.j.yang, c.ouyang, g.dik, p.corry, a.terhofstede}@qut.edu.au
2Food Agility Cooperative Research Centre, Sydney NSW 2000, Australia
Abstract. Reliable and timely forecasts on crop harvest bring signifi-
cant benefits to agri-food industries by providing valuable input to com-
plex decisions on production planning. Useful predictions on crop harvest
require continual effort by seasoned field agronomists. However, they are
often scarce resources in the real-world. A feasible way to facilitate crop
harvest forecast is through developing predictive models that can ex-
ploit data relevant to crop growth and automatically generate consistent
predictions. To this end, this paper presents our design of a systematic
and data-driven approach to supporting online forecasts on crop harvest.
Underpinned by process modelling and predictive monitoring techniques,
our approach can utilise crop-growth-related information from multiple
data sources and progressively generate crop harvest predictions within
the crop growing season. The approach has a flexible design informed by
agronomic knowledge applicable to crop growth in general, and may be
tailored to different crops and production scenarios. A case study with a
local farming company using its real-life production data demonstrates
the feasibility and efficacy of our approach.
Keywords: Crop forecast ·predictive process monitoring ·event logs ·
process modelling ·knowledge discovery
1 Introduction
The resilience of agriculture and food production is one of the key challenges in
today’s world. Growers of fresh produce often have to make fast and complex
decisions about production planning based on outcomes of crop harvest. Reliable
and timely forecasts on crop harvest can bring significant benefits to agri-food
industries, as they provide valuable input to support growers’ decision-making,
e.g., schedule for appropriate harvest dates to ensure the quality of yield [10].
However, forecasting crop harvest is complicated by the fact that (i) crop growth
is affected by many factors, such as climate variability, geographical location, soil
and water quality, and (ii) the impact of relevant factors on crop production is
often dynamic, i.e., it changes along the crop growing process.
2 J. Yang et al.
Effective forecasts on crop harvest concern various aspects and factors re-
lated to crop growth, and demand efforts by experienced agronomists working
in the fields [2]. However, they are often scarce resources in the real-world. Re-
cent advancement in digitalisation and Internet of Things (IoT) technologies
enables the recording of various data relevant to crop production by information
systems deployed in the agri-food sector [10] (e.g., farm management systems).
It becomes an interesting yet challenging problem how to exploit such relevant
and multi-sourced data to provide accurate and timely crop forecasts within the
crop growing season automatically.
To this end, we propose a systematic and data-driven approach to supporting
online forecasts on crop harvest. By modelling the process nature of crop growth,
our approach can integrate static and dynamic crop-growth information from
various data sources into one consistent data input and utilise it for reliable crop
harvest predictions. Built upon process modelling and predictive monitoring [16]
capabilities, the approach enables predictions to be generated automatically and
progressively during the crop growing season. Furthermore, the approach has a
flexible design informed by agronomic knowledge applicable to crop growth in
general, and may be tailored to different crops and production scenarios. A case
study with a local farming company using historical real-life crop production
data demonstrates the feasibility and efficacy of the approach.
Our research contributes an effective approach to forecasting crop harvest
based on process science and machine learning. The contribution is three-fold.
Firstly, it presents a novel attempt to adapt a predictive process monitoring
framework in the domain of agri-food production. Secondly, the proposal of
using process modelling to integrate multi-sourced crop-growth data into one
standardised, consistent data input in the form of event logs, is a potential
contribution to the field of Information Systems Engineering. Last but not least,
for the farming company engaged in the case study, our work lays the technical
foundation for the company’s capability building in data-driven crop forecast
and contributes to improved production planning and resource deployment.
2 Background and Related Work
Forecasting crop harvest within crop growing seasons plays a vital role in crop
production planning and decision-making. There are three types of approaches
to crop forecasting [2]. Field survey is a traditional yet expensive way. Farm
managers and farmers collect and assess information such as the number of
pods and pod weight close to the harvest period and give estimation of the
final yield [12]. Field surveys usually require trained operators to carry out data
collection across multiple locations. A typical example concerns the Objective
Yield surveys conducted by the US Department of Agriculture. Field survey data
can be used to make forecasts, which often depend on agronomists’ opinions
about growing-season conditions (like weather events) and their expectations on
the final yield. As a result, field surveys may risk uncertainty and inconsistency
due to their reliance on agronomists’ expertise [2].
Agronomy-informed Process Modelling and Predictive Monitoring 3
Crop simulation modelling is an effective way to deriving crop forecasts.
Simulation models consist of mathematical equations to characterise plant de-
velopment and growth processes, considering factors of genotypes, environment,
management, and their interactions [13]. Historical data, such as actual observed
weather and averaged weather data, as well as climate model outputs, can be
used to establish parameters of a simulation model. Predictions on the end-of-
season harvest outcomes are then generated by running the instantiated model.
Since simulation models do not predict based on real-time observed data, they
are prone to risks of unknown climate situations between forecast dates and har-
vest dates [2]. To overcome this issue, some research (e.g., [5]) studies how to
calibrate simulation models at runtime within crop growing seasons.
Another promising way to approach crop harvest forecasting is through the
use of machine learning techniques, which has received growing interest in re-
cent years. Machine learning models, e.g., linear regression or neural network,
are trained to fit crop data from historical seasons, and can then be applied to
new crop data and make predictions for coming seasons. Crop data may cover
various types of information, including weather (e.g., temperature and solar ra-
diation), soil (e.g., soil type and nutrients), water (e.g., rainfall and humidity),
and the crop itself (e.g., crop variety and plant weight) [15]. Recent advancement
in sensor technologies has enabled possibilities to use data collected by dedicated
proximal sensors (e.g., Internet of Things devices deployed on fields) and remote
sensing platforms (e.g., satellites and Unmanned Aerial Vehicles or UAVs) [17]
as inputs to machine learning techniques. These data are usually images, from
which various vegetation indices and biophysical parameters can be derived and
utilised for prediction and change analysis [8]. For example, remotely-sensed
images acquired by UAVs are used to calculate the Normalized Difference Vege-
tation Index, contributing an important feature alongside weather variables for
predicting pasture biomass development [4]. Notably, a recent systematic liter-
ature review on application of machine learning to crop yield prediction [15]
highlights the need and challenge for future work to utilise data from different
data sources to improve predictions.
To automatically generate reliable forecasts on crop harvest within crop grow-
ing seasons, it is vital to integrate multi-sourced data relevant to crop growth
data reflecting aspects of the plant nature, growing environment, and produc-
tion management and to be able to utilise such data and synchronise with
dynamic changes during growing seasons. Event logs provide a flexible view that
aggregates multi-dimensional, time-series data relevant to the same process and
potentially from multiple data sources [1]. Crop growing seasons adhere to the
plant’s phenological nature [2] and can be captured using a process notation.
This makes event logs a suitable choice for integrating multi-sourced data rele-
vant to crop growth.
Predictive process monitoring techniques can be used to exploit event log
data and make predictions on running processes with regard to performance,
outcomes, risks, or future states [11]. These techniques use machine learning
4 J. Yang et al.
algorithms with a process focus [16] and strengthen organisations’ capabilities
of making timely decisions about processes at runtime.
In this paper, we propose an approach to integrating multi-sourced, crop
growth-related data into the form of event logs and using predictive process
monitoring techniques for online predictions of crop harvest. In the context of
research on process analytics [1] and predictive process monitoring [11], our
approach represents an application of these techniques to address key problems
in the agri-food domain.
3 Approach
In this section, we present our approach to supporting online predictions of
crop harvest. Fig. 1 depicts an overview of the approach, which takes as input
multiple data sources related to crop growing and generates predictions related
to crop growth and harvest (e.g., days to harvest and yield ). The design of the
approach is informed by the relevant agronomic knowledge and is built upon a
benchmark predictive process monitoring workflow [16]. As such, it is capable
of addressing the need of making timely crop predictions automatically and
progressively during the crop growing season. There are two key components
data fusion guided by a crop growing process model, and an agronomy-informed
process-aware (AIPA) predictive model for crop forecast.
Fig. 1. An overview of the approach supporting online forecasts on crop harvest
3.1 Data Fusion
We consider three main data sources which provide useful information related
to crop growing. Crop production data records information of crop production
relevant to farm and supply chain management, such as production order, crop
plant variety, growing location, sowing date, and harvest date and yield (only
available in historical data). Crop development data contains information about
Agronomy-informed Process Modelling and Predictive Monitoring 5
crop growth, including important events like sowing and flowering; and physical
characteristics of growing plants like size of the plant, tiller number, etc. Crop
environment data records information concerning the crop growing environment
like soil and water quality and, in particular, weather information of the crop
growing locations such as temperature, radiation, and rainfall.
We use rice production as an example to explain how data fusion is carried
out, as illustrated in Fig. 2. While the three data sources are inherently different
from each other, the purpose of data fusion is to integrate them into one crop
growing dataset as the input for prediction.
Fig. 2. Illustration of data fusion using a simple hypothetical example, where (a) rice
production data, (b) rice plant development data and (c) weather data of rice growing
locations are integrated into (d) rice growing event log data, by referring to (e) a rice
growing process model (in a simplified view) of which the design is informed by (f) three
main phenological growth stages and milestone events of rice crop (adapted from [7])
The key to enabling data fusion in our approach is the design of a crop
growing process model which captures the phenological process of a given crop.
Consider the example of rice crop in Fig. 2 (f). The growth of a rice crop consists
of three main phenological stages Vegetation, Reproductive and Ripening, and
two intermediate milestone events tillering which indicates the transition from
Vegetation to Reproductive growth, and flowering which marks the transition
from Reproductive to Ripening growth [7]. Such agronomic knowledge can be
used to inform the design of a rice growing process model. Fig. 2 (e) depicts
a simplified view of such a model capturing rice growing activities in terms of
three main phenological growth stages and two milestone events between sow
(the start event of rice growth) and harvest (the end event of rice growth).
Once a crop growing process model is established, it can be deployed to re-
play or monitor the process of crop growth by incorporating the input data from
6 J. Yang et al.
available data sources. As a result, a log of events recording the occurrences of
crop growing activities can be generated, where data from the various sources is
systematically and consistently integrated into relevant data attributes associ-
ated with individual events. For example, Fig. 2 (d) shows a fragment of a rice
growing event log generated by deploying the rice growing process model in (e)
with the input data of rice production in (a), rice plant development in (b) and
relevant weather information in (c).
Concepts and notations We define several key concepts and notations for
describing crop growing event log data.
Acrop growing event (denoted e) is an instance of a crop growing activ-
ity (including a start, end or intermediate milestone event), and has attributes
carrying crop production, development, and environment data associated with
the activity. Each crop growing event ehas three mandatory attributes pro-
duction order (denoted o), crop growing activity (a) and timestamp (t), and is
uniquely identified by a combination of the values carried by these attributes.
Hence, ecan be represented as a tuple (o,a,t). For example, in Fig. 2 (d) each
row corresponds to a rice growing event and each column to an attribute, and the
rice growing event on the first row can be written as (200155, sow, 5/09/2019).
Acase-level attribute is an attribute of which values remain identical across
all crop growing events that belong to the same production order. By definition,
production order is always a case-level attribute of a crop growing event. Re-
visiting the example in Fig. 2 (d), each rice growing event has three case-level
attributes crop production order, variety, and location.
Acrop growing trace (denoted σ) is a non-empty finite sequence of crop
growing events that belong to the same production order. For each crop growing
trace σ, the order of crop growing events in σis determined by event times-
tamps. Let σorepresent the crop growing trace of production order o,σocan be
written as [(o,a1,t1),. . . ,(o,an,tn)] (where t1<. . . <tn). Revisiting the example
in Fig. 2 (d), the following two rice growing traces can be observed:
σ200155: [(200155, sow, 5/09/2019), (200155, Vegetation, 6/09/2019), (200155,
Vegetation, 7/09/2019), . . . , (200155, harvest, 3/12/2019)]
σ208715: [(208715, sow, 2/11/2020), . . . , (208715, tillering, 28/11/2020)]
Acrop growing event log is a set of crop growing traces. There are: completed
crop growing traces, which begin with the start event (e.g., sow) of a crop growing
process and finish with the end event (e.g., harvest) of the process; and ongoing
crop growing traces, which begin with the start event but finish with an event
other than the end event. In the above example, σ200155 is a completed rice
growing trace, and σ208715 is an ongoing rice growing trace.
3.2 AIPA Predictive Model
The availability of crop growing event log data (as output of data fusion) makes
it possible to develop a predictive model to forecast crop harvest by exploit-
ing predictive process monitoring capabilities. We adopt a benchmark predictive
Agronomy-informed Process Modelling and Predictive Monitoring 7
process monitoring workflow [16]. The main idea is to train a predictive model
using historical data of completed crop growing traces, and then use the trained
model to make predictions for ongoing crop growing traces (see Fig. 1). In par-
ticular, we focus on applying agronomic knowledge and domain expertise and
support model explainability in the design of our approach.
Bucket prefixes The first step is to extract prefixes from completed traces and
group them into buckets (or bins) according to certain criteria. Given a completed
trace σ, a prefix of σis defined as a sequence of the first l(1 l |σ|) events
of σ. Hence, a completed trace σcan be used to extract |σ|prefixes. These
prefixes capture the history of crop growth related to the trace progressively,
and are the input for feature encoding and model training. A bucketing strategy
is used to specify the criteria for grouping the extracted prefixes into buckets.
A typical example known as prefix-length-based bucketing is to group prefixes
of the same length into the same bucket. For crop prediction, we propose to
apply the relevant agronomic knowledge to the design of a bucketing strategy.
For example, the growth of a crop plant consists of different phenological stages.
Each stage is associated with a specific set of factors affecting crop harvest
outcome. According to this, prefixes of crop growing traces can be grouped into
buckets depending on which phenological stage each prefix belongs to.
Encode prefixes In the second step, prefixes in each bucket are encoded as
feature vectors using an encoding mechanism. Our focus is on how to encode
event-specific attributes, which change from event to event and are considered
dynamic attributes of an event log. Although there exist various feature encod-
ing techniques in data mining research, they are not necessarily suitable for crop
prediction. For example, weather data attributes are typical event-specific at-
tributes of a crop growing event log. Since weather plays an important role in
crop growth, it has been studied in the field of agrometeorology, where specific
measures and algorithms for aggregation of weather attributes are established
with an emphasis on their impact on crop production. Hence, we propose that
relevant agronomic knowledge such as agrometeorology principles should be used
to guide the design of encoding mechanisms for crop prediction.
Train models In this step, feature vectors in each bucket are used to train a
predictive model. While there are various machine learning techniques that one
can choose from, we propose two key rationales for making a design decision.
Firstly, since complex machine learning models developed to build advanced pre-
dictive capabilities are often used as a ‘black-box’, the recent body of literature
in machine learning has emphasised the importance to apply models that are
transparent and explainable [14]. Secondly, among the models that are explain-
able by design, statistical models are a good choice as they have already been
used to make crop predictions (see Sect. 2). A typical example is the use of
statistical regressions for predicting crop yield, where the applicability of such a
model is often driven by its simplicity and transparency [2]. Hence, we consider
the use of statistical models for crop prediction in our approach.
At the end of this step, a predictive model that is agronomy-informed and
process-aware (i.e., an AIPA predictive model) is generated. It comprises a set of
8 J. Yang et al.
buckets of prefixes, and encoded feature vectors and trained models associated
with each of the buckets. The performance of an AIPA predictive model can be
assessed using appropriate evaluation measures for the given predictive target.
Online prediction During the online phase, a trained AIPA predictive model
is used to make predictions for ongoing crop growing traces. Given an ongoing
trace, the correct bucket for the trace is firstly determined, then the feature
encoder for the bucket is used to encode the trace data into a feature vector,
and finally the trained model for the bucket is deployed to obtain a prediction.
4 Case Study
This section reports on a case study using real-life data provided by a farming
company X in Australia to predict the harvest of a crop Y.1
4.1 Context
Crop Y is a common type of crop grown by company X. To increase market
value of crop Y, the company wishes to develop a scientific solution that provides
timely predictions about the crop’s harvest date and yield, using historical crop
production and meteorological data. While its current predictions rely heavily
on the manual labour of field agronomists, company X expects the solution to
automatically produce predictions on a weekly basis during the crop growing
season. We applied our approach to address this need.
For this case study, company X provided us with historical crop production
data recording the actual production orders (orders for short) of Y over the past
five years. Each order in the dataset can be uniquely identified and records in-
formation on the growth period of a certain amount of crop Y. More specifically,
an order record has four types of information, including (i) location and area of
the production unit, (ii) variety of crop Y, (iii) key dates during the crop growth
period, e.g., harvest dates, (iv) quantities of the order, i.e., the ordered quantity
(corresponded to sales orders) and the delivered quantity (i.e., yield).
For crop environment data, we collected and used the public weather data
released by the state government. For each local area in the state, the weather
dataset records the daily maximum and minimum temperature, radiation, rain-
fall, evaporation, and vapour pressure.
4.2 Application of the Approach
Data fusion and preprocessing We discussed with agronomists from com-
pany X and built a process model capturing the phenological growth process of
crop Y. As shown in Fig. 3, the growing season of crop Y is divided into two
stages marked by two milestone events, namely “PF” and “HA”.
1For confidentiality reasons, we cannot disclose the company’s name and the specific
crop considered in this case study.
Agronomy-informed Process Modelling and Predictive Monitoring 9
LEGEND
timer:
start event timer:
intermediate event end event loop task
sow
Stage 1
milestone
PF
Stage 2
milestone
HA crop
harvested
sow
Stage 1
milestone
PF
Stage 2
milestone
HA crop
harvested
Fig. 3. Process model capturing the growth of crop Y using BPMN
We utilised the created crop growing process model to integrate the collected
order data and weather data, and generated the crop growing event log for the
given orders of crop Y. By matching the production unit locations in the order
data against the geographical locations in the weather data, we were able to
extract the daily weather observations during the crop growing season for every
order. Then, the created process model was deployed to generate for each order a
crop growing trace containing daily crop growing events. As a result, we obtained
a crop growing event log with 348 completed traces consisting of 22,115 events.
Table 1 shows a fragment of the event log.
In a real-life scenario, historical crop growing traces are used to train an
AIPA predictive model offline, which is then used for making predictions about
ongoing crop growing traces. In our evaluation, we simulated such a scenario
by splitting the event log dataset into two subsets. We ordered all traces by
their sowing dates. The first 75% was used for training the AIPA model (as
“historical” traces), while the more recent 25% was used for testing the derived
model (as “ongoing” traces).
Table 1. A fragment of the anonymised event log used in the case study
order event/ timestamp days to days to case: case: case: case: case: radn max t min t rain evap vp
subprocess PF harvest unit lo c unit area var order qty deliv qty
12763 sow 2016-08-10 74 56 M1 4.2 VG 32320 38350 17.4 24 5.4 0 2.4 11.6
12763 Stage 1 2016-08-11 73 55 M1 4.2 VG 32320 38350 8.1 22.9 7.2 0 2.9 13
12763 Stage 1 2016-08-12 72 54 M1 4.2 VG 32320 38350 16 21.9 8.5 2.9 3 12.4
. . .
12763 Stage 1 2016-10-05 0 18 M1 4.2 VG 32320 38350 25.8 26 7.1 0 6 9.7
12763 Stage 2 2016-10-06 n/a 17 M1 4.2 VG 32320 38350 25.5 28.3 6.6 0 5.4 7.2
12763 Stage 2 2016-10-07 n/a 16 M1 4.2 VG 32320 38350 26.3 29.8 5.8 0 5.2 10.5
. . .
13687 sow 2019-10-14 60 45 M3 2 W 3900 5700 20.1 29.6 15.4 0.8 5.3 17.8
13687 Stage 1 2019-10-15 59 44 M3 2 W 3900 5700 22 34.3 16.3 0 7.5 20.4
. . .
(i) Attributes derived from the order data: “days to PF”, days from the recorded event to PF;
“days to harvest”, days from the recorded event to the harvest date; “case:unit loc”, location of the
production unit; “case:unit area”, area of the production unit (hectare); “case:var”, crop variety;
“case:order qty”, ordered quantity (kg); “case:deliv qty”, delivered quantity (kg). (ii) Attributes
derived from the weather data: “radn”, solar radiation (MJ/m2); “max t”, maximum temperature
(); “min t”, minimum temperature (); “rain”, rainfall (mm); “evap”, evaporation (mm); “vp”,
vapour pressure (hP a). Attributes that start with “case” are case-level attributes.
Bucketing We applied a bucketing strategy based on the crop’s phenologi-
cal growth stages. As mentioned, the growing season of crop Y is divided into
10 J. Yang et al.
two stages. Therefore, we used two buckets to group encoded prefixes based on
whether milestone PF was reached.
Encoding We consulted agronomists from company X to identify and model
factors impacting the growth of crop Y. It was suggested that temperature and
radiation are the most important weather attributes. Specifically, temperature
can be characterised by two measures in agronomy, namely, growing degree days
(GDD) [9] and heat stress days [6]. These measures can be derived2based on
the daily maximum and minimum temperature in our dataset. For radiation, we
applied numerical aggregation functions including sum, mean, max, min and std
to encode features [16].
Furthermore, in terms of predicting harvest date, the agronomists advised
that the length of stage 1 (from sowing to milestone PF) is a good indicator for
estimating final harvest dates in practice. Therefore, for prefixes in the second
bucket (at stage 2), we included the duration from sowing to milestone PF as
an encoded feature. We set the field “days to harvest” as the prediction target.
For predicting yield, the agronomists suggested that the number of heat
stress days occurred during the first five days after milestone PF may be a
useful predictor besides the foregoing features. We included this for prefixes in
the second bucket. We set the delivered quantity per unit area as the prediction
target to eliminate the area difference across production units. This variable can
be derived from dividing “case:delivered qty” by “case:unit area”.
Statistical model In this case study, we employed ordinary linear regression
models with least squares. The reasons are two-fold. First, applying a linear re-
gression model requires minimal configuration, which helps avoid hyperparame-
ter tuning often required by other more complex regression techniques. Second,
the simplicity and transparency of a linear model helps us better communicate
our prediction results with company X when explaining how predictions are
obtained and identifying factors that have high impact on the predictions.
Model evaluation When selecting the evaluation metrics, we refer to the goals
set by company X: (i) predicted harvest dates are expected to be within 2 days
on either side of the actual harvest dates, and (ii) predicted yield (as delivered
quantity per unit) values are expected to deviate no more than 35% (either
side) from the actual delivered yield. Given these goals, we employed Mean
Absolute Error (MAE) for evaluating harvest date prediction and Mean Absolute
Percentage Error (MAPE) for yield prediction, respectively.
MAE = 1
n
n
X
i=1
|yiˆyi|,MAPE = 1
n
n
X
i=1
yiˆyi
yi
,
where nis the number of samples, yiand ˆyiare the observed and predicted
target values of the i-th sample [16]. Furthermore, we also included the adjusted
2Calculation of those meteorological measures was done based on an R-package
cropgrowdays (https://gitlab.com/petebaker/cropgrowdays).
Agronomy-informed Process Modelling and Predictive Monitoring 11
R2score (i.e., coefficient of determination) considering the regression nature of
the tasks.
We also considered earliness [16] in our evaluation, which refers to the earliest
point of time when the prediction accuracy (in terms of a selected measure)
satisfies a given goal. To enable earliness evaluation, we applied two filters on
prefixes in both the training and testing data set: (i) we excluded the final event
for all prefixes (i.e., event recording harvests), since predictions obtained on the
day of harvest will be trivial, and (ii) we excluded prefixes with excessive length3
to avoid unreliable results due to an unbalanced number of samples [16].
In our experiments, we conducted model evaluation in two ways. First, we
performed an overall model evaluation by calculating prediction scores and errors
for prefixes in the two buckets separately, i.e., stage 1 (pre-PF) and stage 2 (post-
PF). In this way, we were able to obtain an overview on the efficacy of linear
regression models using different features. Second, we performed a weekly-based
evaluation to assess how our approach generates predictions within the crop
growing season. We took all prefixes from both buckets and re-grouped them
by prefix length based on weeks, e.g., prefixes with lengths ranged from 1 to 7
comprise the first group and those from 8 to 14 comprise the second group, etc.
We then calculated the prediction errors for each weekly group and compared
the results against the prediction goal.
4.3 Results Analysis and Discussion
Overall model evaluation Table 2 show the results of the overall model eval-
uation by prediction errors and scores. In predicting days to harvest, the average
adjusted R2scores show that regression models built for both buckets have de-
cent performance. Meanwhile, the MAE values are 2.77 and 1.85. The decrease
in errors signifies that including the duration from sowing to milestone PF as a
feature contributes to making better predictions, which aligns with the advice
given by the agronomists of company X.
Table 2. Results of overall model evaluation
Prediction task Metric Bucket 1: pre-PF Bucket 2: post-PF
Days to harvest Adj.R20.93 0.85
MAE 2.77 1.85
Yield Adj.R20.62 0.60
MAPE 0.37 0.31
In predicting yield, we obtained adjusted R2scores of 0.62 and 0.60, respec-
tively. The scores are lower compared to those in the previous task, but this is
expected in predicting yield, all prefixes of the same trace have an identi-
cal target value (i.e., the final unit quantity delivered), due to the lack of data
3Note that this does not reduce the size of the training or the testing set, in terms
of the number of traces (orders).
12 J. Yang et al.
tracking crop development in terms of its final yield. This limitation of data
impeded the yield predictive model in capturing how weather conditions within
the growing season may have impacted the final yield.
1 2 3 4 5 6 7 8 9 10 11
Week
0
1
2
3
4
5
6
7
8
MAE
Prediction goal
1 2 3 4 5 6 7 8 9 10 11
Week
0
1
2
3
4
5
6
7
8
MAE
Days in total growing season
50-59 days
60-69 days
70-79 days
1.77
(a) all traces
1 2 3 4 5 6 7 8 9 10 11
Week
0
1
2
3
4
5
6
7
8
MAE
Prediction goal
1 2 3 4 5 6 7 8 9 10 11
Week
0
1
2
3
4
5
6
7
8
MAE
Days in total growing season
50-59 days
60-69 days
70-79 days
(b) per trace length cluster
Fig. 4. Weekly evaluation by Mean Absolute Error on predicting days to harvest
Weekly-based model evaluation In terms of predicting days to harvest,
Fig. 4a shows the results of weekly evaluation by MAE over prefixes of all traces.
It is clear that the prediction error decreases weekly as the crop growing season
proceeds weekly. Also, the developed models show good performance in terms of
earliness, since the prediction errors satisfy the goal set by company X as early
as week 6 (when MAE = 1.77 <2).
Furthermore, we unfolded the evaluation results to investigate how our ap-
proach performed on traces related to crop growing seasons of different lengths.
We did this by classifying traces into three clusters based on the number of days
from crop sowing to harvest: 50 59 days, 60 69 days, and 70 79 days,
respectively. Fig. 4b shows the results. We can observe that the predictive mod-
els performed well on the 50-day and 60-day clusters, with a similar trend of
progressively decreasing error as seen before. However, the longer traces (i.e.,
those in the 70-day cluster) seem to be the outliers. Even though the prediction
error decreases in general, it has an unexpected increase after week 6, which is
approximately the point that splits the two buckets by milestone PF. Also, the
prediction error never reaches a satisfactory level until the last week (week 11),
which would then have very little value in terms of earliness. A possible reason is
that it is less common that the total growing season spans over 70 days. We con-
firmed this by examining the distribution of traces in our dataset. It was found
that the longer traces comprise a small percentage of all traces, only around 15%
Agronomy-informed Process Modelling and Predictive Monitoring 13
in both the overall dataset and the subset used for model training. Therefore,
the predictive models may likely have under-fitted data of the longer traces.
1 2 3 4 5 6 7 8 9 10 11
Week
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
MAPE
Prediction goal
1 2 3 4 5 6 7 8 9 10 11
Week
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
MAPE
Amount of yield delivered
under-delivered
normal
over-delivered
0.338
(a) all traces
1234567891011
Week
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
MAPE
Prediction goal
1234567891011
Week
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
MAPE
Amount of yield delivered
under-delivered
normal
over-delivered
(b) per delivered quantity cluster
Fig. 5. Weekly evaluation by Mean Absolute Percentage Error on predicting yield
In terms of predicting yield, Fig. 5a show the results of weekly evaluation
by MAPE over prefixes of all traces. At first, the prediction error decreases in
general, albeit at a small scale, and meets the prediction goal in week 6 (MAPE =
33.8% <35%). During week 8 to week 9, there is an unexpected increase, but
the prediction error stays close to the accepted level. From week 10 onwards,
the prediction error becomes much lower to around 20%. These observations
provide further evidence to our previous conclusion that predicting yield with
the current dataset is challenging due to the limitation of the data yield
predictions become much more accurate in terms of the actual final yield only
when the crop growing season enters its later stages.
We further examined the yield prediction results. Again, traces were clus-
tered but, in this case, by calculating the ratio of delivered quantity to ordered
quantity (see data attributes in Table 1), which then represents the percentage
of ordered quantity delivered. We considered three clusters: (i) “normal”, if the
ratio is between 90% and 110%; (ii) “over-delivered”, if the ratio is higher than
110%; and (iii) “under-delivered”, if the ratio is lower than 90%. Fig. 5b shows
the unfolded results per cluster. Clearly, the predictive models performed well
on the “normal” cluster, showing an expected pattern of both low and progres-
sively decreasing error. Performance on the “over-delivered” cluster is acceptable
and remains stable over time. The most interesting finding concerns the “under-
delivered” cluster, where prediction error is consistently high in the early weeks
and merely reaches the prediction goal from week 10 onwards. Consider that
“under-delivered” traces accounted for 42% of the training set, the underperfor-
mance of the models is unlikely to be caused by insufficient data. We surmised
that crop growth relevant to those “under-delivered” traces was impacted by
14 J. Yang et al.
unseen factors not captured by information in the current dataset. This specula-
tion was later confirmed through our discussion with the agronomists reported
below. The relatively high proportion of “under-delivered” traces also explains
the increase of prediction error in week 7 as previously shown in Fig. 5a.
Summary Experiment results show that our approach can provide reasonably
accurate predictions about the harvest date and yield of crop Y during its grow-
ing season. Predictions generated from our approach become progressively more
accurate, and can already provide insights usable by the company as early as
week 6 from the date of sowing. We communicated our findings with company X
and received positive feedback. Agronomists from the company expressed high
interest in our solution in terms of how it integrates up-to-date weather ob-
servations and enables them to obtain prediction outcomes on a daily basis.
Specifically, regarding our speculation of predicting yield, we learned from the
discussion that the “delivered quantity” recorded in the dataset (which we spec-
ified as the target of yield prediction) does not fully represent the amount of
fresh yield. Instead, the delivered quantity records the result from a quality con-
trol process which took place after crop harvest. Not surprisingly, data in the
“under-delivered” clusters are related to more human intervention in quality
control, compared to other clusters. Since the dataset we used does not cover
any information about that post-processing step, it makes sense that the derived
predictive models did not perform well on the “under-delivered” clusters.
5 Discussion
Our work reported in this paper paves new avenues to some interesting future
work. For one, our approach has a flexible design which enables it to be gener-
alised to potentially any field crop for which phenological events can be identified
and captured by process modelling. Also, the use of a general predictive process
monitoring workflow allows many machine learning techniques to be plugged
in. For example: (i) when given data that embeds complex, non-linear patterns,
advanced regression algorithms, e.g., Support Vector Machine (SVM), can be
used to potentially improve the prediction accuracy [15]; and (ii) when encoding
prefixes, feature selection methods for sequence prediction [18] can be applied
to complement the use of expert knowledge from agronomists.
With this flexibility, it is worthwhile to investigate guidelines on how to
configure different steps of the approach according to factors like actual crops,
ecosystems (soil, growing season, etc.), and input data characteristics [8].
The use of process modelling in our approach can transcend the purpose of
integrating data from multiple, various sources to feed the predictive process
monitoring workflow. Process models formally capturing the phenology of crop
growth enable many existing process analytics tools to be applied “off-the-shelf”.
For example, historical crop growth can be visually analysed by replaying crop
growing event logs [3], so that agronomists can leverage the collated data to
examine how crop harvests were impacted by geographical locations, date-times,
Agronomy-informed Process Modelling and Predictive Monitoring 15
weather conditions, etc. We consider the use of process modelling an enabler
of future deployment and application of process analytics over data integrated
from various non-standard data sources this can potentially be extended to
domains other than forecasting crop harvest, making a contribution to the field
of Information Systems Engineering.
Last but not least, an interesting direction concerns how to integrate this
approach into the decision workflow of production planning for growers, specif-
ically how it can synergise with the management of other business processes in
crop production, e.g., delivery after crop harvest.
A limitation of our work is that the case study is subject to one crop grown
in the local area, and the collected data contains only crop production data and
crop environment data recording weather conditions. Therefore, an immediate
next step is to extend the case study to other crops and include more relevant
data. In particular, we are interested in exploring the use of crop development
data, for example, as collected by agronomists through field visits or by dedicated
sensors and remote sensing platforms [17]. Such ancillary data will provide pre-
cise information on crop growth monitoring and thus the opportunity to improve
the efficacy of our approach. We also seek to improve the input data quality, e.g.,
to use production data that captures the fresh yield rather than the processed
one.
6 Conclusion
Reliable and timely forecasts on crop harvest benefits decision-making in the
agri-food industries and contribute to the resilience of agriculture and food pro-
duction. In this paper, we present an approach that systematically utilises crop-
growth data from multiple sources and generates online predictions on crop
harvest automatically and progressively. Our approach offers a flexible solution
for crop harvest predictions and can be tailored according to different crops (by
redesigning the crop growing process model) and relevant agronomic knowledge
(by altering the strategies for bucketing, encoding, and the statistical models).
Meanwhile, our research findings contribute to the field of process science by
making a novel and successful attempt to use process modelling and predictive
monitoring techniques to address a key problem in the agri-food domain.
Acknowledgments. This work was supported by Food Agility CRC Ltd, funded
under the Commonwealth Government CRC Program. We also received highly
valuable input from A/Prof Miranda Mortlock, a specialist in agronomy and
statistics, and Dr David Carey, a senior horticulturist from Queensland Depart-
ment of Agriculture and Fisheries.
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Book
This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics. After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges. Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.