=Paper=
{{Paper
|id=Vol-2917/paper20
|storemode=property
|title=Usage of Artificial Intelligence Systems and Working with the Neural Network in Assessing the Condition of Plants in Smart Greenhouses
|pdfUrl=https://ceur-ws.org/Vol-2917/paper20.pdf
|volume=Vol-2917
|authors=Olha Danyltsiv,Andrii Khomiak,Oleg Nazarevych
|dblpUrl=https://dblp.org/rec/conf/momlet/DanyltsivKN21
}}
==Usage of Artificial Intelligence Systems and Working with the Neural Network in Assessing the Condition of Plants in Smart Greenhouses==
https://ceur-ws.org/Vol-2917/paper20.pdf
Usage of Artificial Intelligence Systems and Working with the
Neural Network in Assessing the Condition of Plants in Smart
Greenhouses
Olha Danyltsiv, Andrii Khomiak, Oleg Nazarevych
Ternopil Ivan Puluj National Technical University, Ruska Street, 56, Ternopil, Ternopil region, 46001, Ukraine
Abstract
The paper proposes an approach to solving the problem of recognizing the state of plants using
a neural network for use in information systems for monitoring and managing the microclimate
for smart growing boxes and smart greenhouses. The developed smart greenhouse prototype is
presented, which includes information technology for monitoring temperature, humidity, soil
and recording photos of plants and further analysis of plant condition using a neural network
(deeplearning). In addition, a telegram-bot interface is built for easier control light, temperature
and other factors that create a favourable microclimate for a particular type of plants and
manage through smartphone.
The main purpose of the work is to adapt the algorithm for training an artificial neural network
using a GPU and the ability to assess the condition of plants based on photos for use in
information technology control of smart greenhouse prototype.
Keywords 1
Artificial intelligence, smart greenhouse, deeplearning, plant physiology
1. Introduction
In recent decades, there has been an increased interest in neural networks in connection with the
implementation of the SMART concept in various spheres of human life. Neural networks started to be
explored and introduced into everyday life not only by specialists in technology, physiology and
psychology but also by ordinary users. This is logical, because the artificial neural network, in fact, is
a model of the natural nervous system of a living organism, so the creation and study of such networks
allows us to learn more about the functioning of natural systems [1].
An important factor and a unique feature of the process is that the neural network draws certain
generalized conclusions automatically. Due to its structure, it is able to analyze, compare, process
information without requiring any specific programs. Another property of neural networks is reliability.
Even when some elements of the network work incorrectly or simply fail, the network is still able to
give the correct results, albeit with less accuracy.
There are special types of neural networks, they are able to generate an abstract image, which is
based on input signals. As an example, a network is a sequence of distorted images of a symbol, letter,
or punctuation mark. After training, the network will be able to generate this input without distortions.
This indicates that the network is able to generate unique conclusions based on the input information
obtained during training using the finished dataset.
Thus, today the study of neural network training methods is a really relevant topic that can be applied
to the concept of SMART greenhouse management.
MoMLeT+DS 2021: 3rd International Workshop on Modern Machine Learning Technologies and Data Science, June 5, 2021, Lviv-Shatsk,
Ukraine
EMAIL: olhadanyltsiv@gmail.com (O. Danyltsiv); andrewhamster@hotmail.com (A. Khomiak); taltek.te@gmail.com (O. Nazarevych)
ORCID: 0000-0002-2014-0997 (O. Danyltsiv); 0000-0002-1763-868X (A. Khomiak); 0000-0002-8883-6157 (O. Nazarevych)
©️ 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
The main purpose of the work is to adapt the algorithm for training an artificial neural network using
a GPU and the ability to assess the condition of plants based on photos for use in information technology
control of smart greenhouse prototype.
In our research we distinguish the following tasks:
1. Choose the type of neural network for the classification and assessment of plants.
2. Form, organize, filter and systematize a dataset for network research (training) with the help of
smart greenhouse management information technology.
3. Carry out the marking of the condition of a plant on the basis of the formed dataset.
4. Implement neural network training using GPU.
5. Evaluate the results obtained.
2. The concept of artificial neural network
Since the 1980s, the interest in artificial neural networks grew rapidly. Specialists in various fields
of technical design, philosophy, physiology and psychology were interested in the opportunities
provided by this technology. They actively sought to apply it in their fields within development projects
and research.
In 2007, Jeffrey Hinton at the University of Toronto developed algorithms for deep learning of
multilayer neural networks. This event marked the beginning of the active use of artificial neural
networks in various spheres of human life. In turn, this gave an impetus to the development of artificial
intelligence technology [1].
Artificial neural networks (ARNs) are among the most common classification methods in data
mining. They are also a powerful method of forecasting. Such systems, as a rule, learn to solve
problems, considering examples in general, i.e. the research is perceived integrally, without any specific
programming or any prior knowledge about it.
Two strengths of artificial neural networks are the parallelization of information processing and the
ability to self-learn, i.e. to create abstractions.
What is an abstraction? Abstractions are the ability to output an acceptable result based on the data
used in the learning process. Such features allow neural networks to solve large-scale problems that are
still considered difficult to solve. Despite this, artificial neural networks cannot provide ready-made
solutions directly; they need to be integrated into more complex systems instead. For example, complex
problems can be divided into simpler ones, which will be solved by neural networks [2].
The main features of artificial neural networks are:
1. The obviousness of the answer. For example, if we consider the classification problem, the neural
network can be designed to provide information not only about the class of input data but also about
the reliability of the result. Such information will provide an opportunity not to take into account
questionable decisions of the neural network [1].
2. Nonlinearity. Because the activation function of each neuron is often chosen to be nonlinear, the
network as a whole can mimic much more complex nonlinear functions. This allows the ANN to
approximate functions of almost any complexity. The only limitation is the number of neurons and
the structure of the network connections.
3. Clear correspondence of the input information to the output.
4. Adaptability. Neural networks learn to flexibly solve problems. In particular, they can be retrained
quite easily if the fluctuations in environmental parameters are insignificant. Also, high adaptability
allows the reliable use of neural networks in non-stationary environments. However, it should be
noted that high adaptability can lead to unstable behaviour of ANN. If it quickly adapts to certain
stimuli when changing the coefficients, it can significantly affect further performance. That is, high
adaptability can lead to loss of stability and vice versa. This is usually called the problem of stability-
plasticity.
5. Fault tolerance. A well-trained neural network can function properly, even if several of its neurons
fail. This would allow for the implementation of neural networks on a physical level and confidence
in the reliability of the chosen architecture. Because the information needed for the network to
function is distributed in each neuron, the entire network can fail only if its structure is severely
damaged, while minor damage will never seriously affect performance. This is an obvious advantage
of ANN.
6. Ability to scale. The architecture of neural networks makes it very easy to perform a large
percentage of calculations simultaneously. This allows a minimal amount of effort to transfer or
scale systems based on artificial neural networks.
3. Types of neural networks
Neural networks are divided into single-layer and multilayer ones [3].
The set of neurons combined in one layer is called a single-layer neural network. A multilayer neural
network is formed when the combination of single-layer neural networks into a single entity takes
place [4].
Multilayer networks between input and output data have several layers of hidden neurons. These
layers add more nonlinear connections to the model. They provide greater accuracy in the process. For
example, a multilayer perceptron with sigmoid activation functions is able to approximate an arbitrary
functional dependence.
It should be noted that an important advantage of neural networks over classical means of
classification and prediction is their ability to learn [4].
The very phrase "teach the neural network" means to show it what exactly is expected of it in action.
This process can be compared to teaching numbers to a child. After showing a picture with the image
of the number "5" to a child, we later ask: "What is this number?". In that case, if the answer is incorrect,
we tell the child the correct answer, i.e.: "This is the number five". The child remembers this example
together with the correct answer, i.e. in their memory, there are certain changes in the right direction.
Then we will repeat the process of presenting the numbers again and again until everything is studied.
When teaching a neural network, similar actions are performed. That is, we have a database that
contains materials for training (in the case of this research the materials are graphical).
Presenting the image to the input system of the neural network, we get a certain answer from it.
It is important to note that after repeated presentation of the examples, the weights of the neural
network are stabilized and it gives the correct answers to all (or almost all) examples from the database.
In this case, it is said that "the neural network has studied all the examples", "the neural network has
been taught", or "the neural network has been trained". In software implementations, we can see that
during the learning process the magnitude of the error (the sum of the squares of errors for all outputs)
gradually decreases. When the margin of error reaches zero or an acceptable low level, the training is
stopped, and the resulting neural network is considered trained and ready for use on new data.
All the information that the neural network has about the task is contained in a set of examples.
Therefore, the quality of neural network learning directly depends, first of all, on the number of samples
in the training database, as well as on how fully and holistically these samples describe this task. That
is, for example, it makes absolutely no sense to use a neural network to predict the financial crisis if the
training sample for crisis prediction is not presented. It is believed that for a full-fledged training of the
neural network one would need at least a few dozen (preferably hundreds of) examples.
It is worth mentioning that teaching a neural network is a rather complex and knowledge-intensive
process. Learning algorithms for such systems have different parameters and settings, and to manage
them one has to have an understanding of their impact.
4. Types of training
As mentioned in the previous paragraph, in order for a network to solve the tasks set before it, it
must first be trained. This ability is considered the main property of the brain. Next, the relationship
between the type of training and its effectiveness has to be considered. The learning process for such
systems involves adjusting the structure of connections between individual neurons and synoptic
connections that affect the signals of the coefficients. Such complexes allow the neural networks to
solve the tasks effectively. Basically, neural network learning is based on a sample set (data set).
Scientists and researchers in this field have developed special algorithms for teaching neural networks.
Such algorithms allow to increase the efficiency of response to input signals and expand the scope of
their application.
Examples of these are the so-called Hebb training (Donald Gebb, who hypothesized training based
on the neuroplasticity), Rosenblatt (he is considered the founder of the perceptron), Alexei Ivakhnenko
and Valentin Lapa (researchers of the first working multilayer neural networks). Such algorithms allow
to increase the efficiency of response to input signals and expand the scope of their application.
A large number of different technologies are used for machine learning. In particular, discriminant
analysis, Bayesian classifiers and numerous other mathematical methods can be used. But at the end of
the 20th century, artificial neural networks (ANN) started receiving more and more attention. Now they
are not only used in various spheres of economic and social activities, but are increasingly used by
ordinary people. One of the largest bursts of interest in them began in 1986, after the significant
development of the so-called "method of backpropagation", which was successfully used in the training
of the neural network.
ANN is a system of connected and interacting artificial neurons, built on the basis of relatively
simple processors. Each ANN processor periodically receives signals from one processor (or from
sensors or from other signal sources) and periodically sends signals to other processors. These simple
processors, which are arranged into a network, are able to solve quite complex problems.
Most often, neurons are arranged in the network by levels (which are more often called layers). Let
us have a look at a multilevel neural network. First-level neurons are usually input neurons. They
receive data from the outside (for example, from the sensors of a particular recognition system) and
after processing transmit pulses through the synapses of neurons at the next level. Neurons at the second
level (this level is called hidden because it is not directly connected to either the input or output of the
ANN) process the received pulses and transmit them to the neurons at the output level. The simulation
of neurons is involved in the process, hence each input-level processor is associated with several hidden-
level processors, each of which, in turn, is associated with several output-level processors. This is the
architecture of the simplest ANN, which is capable of learning and can find simple connections in the
data.
Deep learning is applied only to more complex artificial neural networks containing several hidden
levels. At the same time levels of neurons can alternate with layers that carry out difficult logical
transformations or calculations. Each subsequent layer of the network looks for connections in the
previous one. An ANN arranged in this way is able to find not only simple connections but also links
between connections. Google is a fine example of employing a similar system. After the transition to a
neural network with the in-depth learning, the company has managed to dramatically improve the
quality of its popular product Google Translate.
To summarize, there are three types of artificial neural network training: with a teacher, without a
teacher, and mixed.
ANN training with a teacher is one of the most popular ways. It basically contains a change in weight
coefficients using training examples. For each input example, there is a corresponding output vector. In
the process of training, an example is selected randomly, and the neural network forms the original
vector based on the example. This vector is compared with the desired one and according to a special
algorithm, the process of modification of weighting factors takes place in such a way as to approach the
desired vector [4].
When learning without a teacher, the model has a data set without explicit instructions on what to
do with it. The neural network tries to find connections in the data on its own, extracting useful features
and analyzing them.
Depending on the task, the model organizes the data differently:
• Clustering. Even without special knowledge, you can look at images of animals and divide
them into groups by species, simply based on the shape, size or presence of fur, and so on.
• This is called clustering – the most common task for learning without a teacher. The algorithm
selects similar data, finds common features, and arranges them into groups.
• Detection of anomalies. Banks can detect fraudulent transactions by detecting unusual actions
in the purchasing behaviour of customers. For example, it is suspicious if one credit card is used in
two different countries or on different continents on the same day.
• Associations. Considering several key features of the object, the model can predict other cases
where a connection is present [1].
5. Description of the research prototype
Passing in the optimizer and loss function is optional, and in many situations fastai can automatically
select appropriate defaults.
Learner is also responsible (along with Optimizer) for handling fastai’s transfer learning
functionality. When creating a Learner the user can pass a splitter. This is a function that describes how
to split the layers of a model into PyTorch parameter groups, which can then be frozen, trained with
different learning rates, or more generally handled differently by an optimizer.
One area that we have found particularly sensitive in transfer learning is the handling of batch-
normalization layers [3]. We tried a wide variety of approaches to training and updating the moving
average statistics of those layers, and different configurations could often change the error rate by as
much as 300%. There was only one approach that consistently worked well across all datasets that we
tried, which is to never freeze batch-normalization layers, and never turn off the updating of their
moving average statistics. Therefore, by default, Learner will bypass batch-normalization layers when
a user asks to freeze some parameter groups. Users often report that this one minor tweak dramatically
improves their model accuracy and is not something that is found in any other libraries that we are
aware of.
DataLoaders and Learner also work together to ensure that model weights and input data are all on
the same device. This makes working with GPUs significantly more straightforward and makes it easy
to switch from CPU to GPU as needed.
Here the developed information part is described. It consists of many levels (such as physical,
network and software levels) and provides the collection, retrieval, processing and transmission of
information. Such systems allow solving a wide range of tasks of different nature, including
organizational tasks [5].
The advantages of "smart" greenhouses, in general, are quite obvious. This is not just a place where
plants are completely protected from external negative factors, but can also bear fruit all year round
with proper care.
Due to the automation of routine processes, many trivial tasks can be performed without human
input.
Of course, not just any "smart" greenhouses are able to grow plants without human intervention, but
their capabilities are quite wide.
Such greenhouses are able to maintain a comfortable temperature inside. The room is usually
ventilated automatically, so on hot days vegetables, fruits, berries, etc. do not wither in nature-dictated
conditions.
• Water the plants at the specified time. The irrigation system is usually automated and extremely
easy to operate. This is so that any user, including a novice gardener or a person unfamiliar with the
principles of the system, can easily figure the setup out.
The system is also capable of providing automated control of processes, which is of interest to
modern science and humanity as a whole. This process is the cultivation of plants that can have different
purposes (decorative or for cooking, main course or seasoning). Different species and different
environments require different approaches to this process [5].
Smart Growing Box (SGB) is a concept of a physical device for seed germination/plant cultivation,
which automatically maintains an ideal environment for a particular plant (lighting provided for the
specific part of the day, temperature and humidity or microclimate) [7].
Inside the SGB there is a lamp, a fan and a heater that switches on automatically to maintain a
microclimate set according to cloud service recommendations based on SGB location data, the type of
plant grown in it and analytical data collected from similar SGBs. Data is exchanged using the MQTT
protocol, which is based on the TCP transport layer protocol and implements the publish-subscribe
model. Its advantage is low redundancy, which is critical for embedded devices with low power and
limited network bandwidth. In this information system, the cloud is a set of services available via the
Internet that provide reception, storage and processing of data received under the MQTT protocol.
Cloud data is stored in two types of databases:
• SQL for storing static user data, SGB settings.
• Elasticsearchserver [6] for storage and further machine learning based on analytical data
obtained from sensors. The cloud will also have its own ApplicationProgramInterface (API) to
interact with the web application, which will duplicate the functionality of the telegram bot and
additionally have the visualization of analytical data in the form of real-time graphs within 1 minute.
An intelligent greenhouse that uses the principles of artificial intelligence as a key principle of
operation is a design in which the main processes are automated using a model of neural connections,
computer vision and appropriate plant growth sensors.
Such constructions are aimed at facilitating the cultivation of plants, reducing and optimizing the
time of their care, providing and maintaining a favourable microclimate with mandatory analysis of
humidity, temperature, ventilation and more.
So, lets describe standard example of how to fine-tune an ImageNet [17] model on the Oxford IIT
Pets dataset [18] and achieve close to state-of-the-art accuracy within a couple of minutes of training
on a single GPU.
At first we should imports all the necessary pieces from the library. fastai is designed to be usable
in a read–eval–print loop (REPL) environment as well as in a complex software system. Then we
connect with standard dataset.
This is an abstraction that represents a combination of training and validation data and will be
described more in a later section. The next step is fitting the model. In this case, it is using the 1cycle
policy [19], which is a recent best practice for training and is not widely available in most deep learning
libraries by default. It is annealing both the learning rates, and the momentums, printing metrics on the
validation set, displaying results for example in an HTML table (if run in a Jupyter Notebook, or a
console table otherwise), recording losses and metrics after every batch to allow plotting later, and so
forth. A GPU will be used if one is available.
In modern natural language processing (NLP), perhaps the most important approach to building
models is through fine-tuning pre-trained language models.
Projects like this typically use climate controllers, artificial irrigation systems, ventilation, air
irrigation and plant or emergency warning systems. Most smart greenhouse solutions focus on the
proper operation of automatic control systems, which allow you to program care actions according to
time or depending on the performance of the system sensors.
Modern technology makes it possible to develop smart greenhouses on the basis of a popular
platform for IoT (Internet of things) - ESP, which is usually controlled from a mobile device or via the
Internet.
Let us look at the principle of the Internet of things in more detail. The very idea that devices can
exchange information with each other without human intervention, appeared a long time ago.
The modern "Internet of things" involves network communication of objects that interact with each
other and the environment. You can control a variety of devices with your phone from anywhere.
However, having a stable uninterrupted internet connection is a requirement. In addition, interconnected
devices can perform certain actions without user intervention.
Among the important functions of the Internet of things are the facilitation of everyday life,
improving efficiency and quality of work and energy preservation. In general, we can state that there is
no clear list of devices for which this approach can be applied. That is, this idea can be implemented in
household appliances: a remote control washing machine, or a refrigerator capable of compiling
shopping lists and ordering home delivery.
Another option is well-known gadgets that can be worn. There is probably no person who has not
heard of fitness trackers and smart watches. IoT also includes cars or other vehicles with an autopilot
system (meaning those that can drive without a driver behind the wheel).
However, it is important to note that the Internet of Things is not limited to "smart" appliances. It
can be used in any industry where certain processes can be automated. This means it can be useful in
almost all spheres of human activity and life. IoT is especially active in the agricultural sector, logistics,
and the concept of Smart City. This is useful for cases where there is a need to monitor the condition of
objects or to collect large amounts of data for further analysis.
Based on the above, it is logical that the idea of IoT is used in this paper.
A prototype of a smart greenhouse prototype was developed to implement this information
technology of monitoring and control. 7.8 As a result of such realization, we have an opportunity to
receive data on air and soil temperature and relative humidity. Record the modes of operation of
actuators for air ventilation, drip irrigation and heating.
To enable the accumulation of current images of plants in this information technology, webcams
and a server for storing photos (Raspberry Pi3) were installed. [8]
The concept of monitoring and control of such a smart greenhouse prototype is shown in the figure
below.
Figure 1: The concept of monitoring and management of a smart greenhouse prototype
The general concept of the prototype of such a smart greenhouse prototype can be seen in the video
[9].
It is based on the concept of a device for growing different types of plants by supporting automatic
watering, light control, temperature and other factors that create a favourable microclimate for a
particular type of seed [8].
The prototype itself contains temperature sensors, automated ventilation and heating, based on pre-
collected data on a particular plant. An important element of a smart greenhouse is also watering, set
for certain hours or days. The collected indicators form four main graphs, which are presented below
in Figure 2.
Figure 2: Graphs of temperature, humidity, ventilation and lighting of the system
To display graphs of monitoring indicators of smart greenhouse prototype the Grafana system, which
is hosted on the management server of smart greenhouse prototype, was used [10].
The greenhouse is equipped with cameras that take pictures of plants at set time intervals in order to
further analyze their condition and make clear recommendations for care. These actions occur through
the use of artificial intelligence neural networks.
6. Description of the software algorithm
In any process of work, development or research stages are usually outlined. When the project is
divided into clearly defined tasks, the implementation is more convenient and issue-free.
6.1. Description of the dataset structure
Neural network learning begins with the preparation of materials. These materials are usually
marked images of objects, in our case – photos that will need to be recognized. It is with their help that
the neural network is trained. In fact, in this way we teach the NN to analyze what exactly is depicted
in the photo and the condition of the object depicted. However, in order to have this "training data" in
the form required, it is necessary to form such a database.
The so-called "raw" data is a large sample of photos taken at intervals using a webcam in the
developed box. What it looks like at the beginning is shown in Figure 3.
Input data encompasses the names of the photos, the photos themselves that contain some info about
the plant, the stage of its growth, as well as water and temperature levels. The data also includes
temperature conditions in the room where the experiment is performed, the temperature outdoors, the
temperature of the ground for plants and the temperature in the experimental box with plants.
Figure 3: Learning objects for database formation
Brief characteristics of the plant are the data given as to whether it is well lit, watered sufficiently,
whether the leaves stretch upwards or wither, etc. Some of them are given below:
• • Temp_outside.
• • Temp_room.
• • Temp_box.
• • Temp_ground.
• • Is_fito_lamp_on.
In our case, the authors do not follow strict terms to describe the condition of plants in terms of plant
physiology. The authors have formed a simplified list of markers of plant condition, as the goal is to
focus on the adaptation of techniques and training of the neural network.
6.2. Neural network training
The following tools are used to implement the training process:
• Anaconda is a common Python platform for data processing. The platform includes a collection
of about 200 open-source packages and Intel MKL optimization. The platform specializes in
"scientific computing": the science that deals with data, application of machine learning methods,
large-scale info processing, forecasting etc. The use of the platform provides for simplified
management of packages and their deployment [11];
• Anaconda Navigator is a desktop user interface that is part of the Anaconda distribution and
allows users to run related programs and manage packages, among others.
• Conda is a freely and openly distributed cross-platform and language-agnostic package
manager and Anaconda environment management system that installs and updates the packages and
their dependencies. Originally designed for Python programs, today it can package and distribute
software for a very wide range of languages.
• Python is one of the most powerful and widely used programming languages, which is quite
easy to master without special skills. It has efficient high-level data structures and a simple but
effective approach in terms of object-oriented programming [12].
It is also important to note that the list of existing Python libraries provides a wide range of tools for
solving problems related to the creation of artificial intelligence and data analysis. With their help,
engineers and scientists can cope with the tasks of image analysis, text and visualize the analyzed data
much easier.
Python is also successfully used to implement Geographic Information Systems (GIS): cartography,
geolocation, and GIS programming.
Typically, the advantage of Python over specialized languages created by scientists for scientists is
that it is much easier for programmers to do more scientific tasks without learning a separate language
each time. Also, using the Python language, the user will be able to more easily integrate the solution
of a scientific problem with the practical goals of the project itself. Therefore, if there is a need for
scientific calculations or data analysis one should consider this programming language.
During program development, the main packages used are:
• Pandas is one of the Python libraries that works with tabular data and has the technology to
optimally process it. The library introduces two basic concepts that describe the data structures in
it:DataFrame is a data object that is best represented in the form of a regular table because it is a
tabular data structure. Series is an object similar to a one-dimensional array (an example is a list in
Python), but the difference is the presence of associated labels, or so-called indexes, along with each
element in a list. This feature turns it into an associated Python array or dictionary.
• The Fast.AI library is a top-level software add-on to the Pytorch package for the Python
programming language. All these constructs are designed to facilitate the creation of software
models of deep neural networks. Fast library. AI uses the best practices and approaches. At the
moment, it has a wide range of opportunities [13]. FastAi is organized around two main design
goals: to be approachable and rapidly productive, [15]. We wanted to get the clarity and development
speed of Keras [16] and the customizability of PyTorch. This goal of getting the best of both make
us to design of a layered architecture.
• NumPy is a Python language library for working with multidimensional data arrays. Typically,
these arrays are indexed by positive integers. The data that the library works with is homogeneous.
So what does that mean? Homogeneity of data allows optimizing work in comparison with standard
lists of language [14].
• Scikit_Learn - Another library of machine learning software for the Python programming
language. It is closely related to the previous library, as it is Scikit_Learn that mainly takes NumPy
arrays as input.
There is a number of different work teams that were used in our study. For example, the
pd.read_excel command is used to read a markup file. Then file names are converted to the file path on
a disk.
Later, we filter the files, because only those that are on the disk are needed for work.
Let's move on to the method that trains the model for this target variable (label_name) based on the
pre-trained ResNet34 architecture (ResNet is an abbreviated name for Residual Network (see Figure
4).
Let us move on to ResNet, also known as a residual neural network. In short, it is an artificial neural
network (ANN), based on structures known from pyramidal cells of the cerebral cortex.
Residual neural networks do this through bandwidth connections or shortcuts to go through some
layers. Typical ResNet models are implemented with two-layer or three-layer passes. They contain
nonlinearities and batch normalization between them.
Figure 4: Syntax of the model training method
With the command train_model_for_label ("label_name") you can see the work of the training
algorithm to mark the state of the leaves of the plant. Such training is conducted for all available labels.
6.3. Results
After completing the process of training the model, we obtain the so-called table result, which is
shown in Figure 5.
Here you can find information about the stages of neural network learning, training losses, actual
losses, the accuracy of training and time spent on each stage.
Figure 5: Initial table of model training results
Confusion matrix is also presented as a source element. Its construction is provided by the Sklearn
library commands.
The main purpose of using the confusion matrix is to assess the quality of the classifier output on
the data set. That does, in fact, show how accurate our model is. Diagonal elements represent the number
of points for which the expected label is equal to the real label, and outside the diagonal elements –
those that are marked by the classifier incorrectly. The higher the value of the diagonal numbers of the
confusion matrix, the better (the values themselves range from 0 to 1), as this indicates a large number
of correct predictions.
Figure 6 shows the confusion matrix for training the lighting threshold.
Figure 6: Training quality matrix for the illumination column model
Analyzing the matrix in relation to the above, we see that since the diagonal elements are equal to
ones and the lateral elements to zeros, the quality of the neural network is satisfactory, and the training
has been conducted correctly.
We shall present the same matrices for some other characteristics. For example, the matrix for the
label "leaf position" is shown in Figure 7, and for the values of "humidity" – in Figure 8.
Figure 7: Training quality matrix model for the position of plant leaves
Figure 8: Training quality matrix model for humidity values
If the matrices on the diagonals are different from the unit value but quite close, this is similarly an
indicator of the high quality of the algorithm.
It should be noted that the algorithm itself shows the method of image classification. That is, if you
focus, for example, on the characteristics of lighting, it highlights the most contrasting images in this
regard and divides them into certain groups. This ensures better network performance in the future.
6. Conclusions
A result of the constructed prototype of the smart greenhouse prototype and using information
technology to monitor and manage the prototype, a set of observation data for the period of plant growth
(within 20 days) was formed.
The neural network was adapted for the studied dataset, machine learning was performed using the
deeplearning network and the corresponding reliable learning results were obtained, as evidenced by
the training quality matrices.
As a result of the training, the available information technology of monitoring and management of
a smart greenhouse prototype and an opportunity to receive a condition of plants on the basis of a photo
from webcams placed in it was improved.
Regarding the further development of this project, the need for more rigorous formalization of the
list of markers in accordance with the requirements of plant physiology should be noted. After making
such changes to the list of markers for the formation of a new dataset and retraining of the neural
network.
In this study, we have discussed the advancements of CNN in image classification tasks in general
too. We have experienced that combining inception module and residual blocks with conventional CNN
model, and ResNet gained better accuracy than stacking the same building blocks again and again.
In fact, the proposed prototype has two directions of further development: for smart growing box,
which can be placed in a house or apartment (for growing greenery or seedlings) and for smart
greenhouse which are usually placed on land. In both cases, it will be important to implement this
system for assessing the condition of the plant on the base of the proposed approach to the construction
of a neural network.
7. References
[1] X. B. Olabe, Redes Neuralna Artificiales y sus Applicaciones Format Impreso: Publicaciones de
la Escuela de Ingenieros, 1998.
[2] M. Minsky, S. Papert, Perceptrons, Mir, Moscow, 1971.
[3] D.V. Efimov, V.A. Terekhov, I.Yu. Tyukin, Neural network control systems: Textbook. Manual
for universities. Moscow, 2002.
[4] P.M. Hrytsiuk Application of artificial neural networks for long-term forecasting of grain yield
2018.
[5] O. Nazarevych, A. Volokha, O. Zymnytsky. Multilevel information system of ecomonitoring and
climate control Smart Growing box, Innovative technologies: Materials of XVI scientific and
technical. conf. students, graduate students, doctoral students and young scientists, INTL NAU,
Kyiv, 2019.
[6] R. Kuc, M. Rogozinsk, Mastering Elasticsearch, Second Edition, 2015.
[7] F. Xia, L. Yang, L. Wang, A. Vinel, Internet of Things, International Journal of Communication
Systems, no. 9, vol. 25, Wiley, 2012. doi.org/10.1002/dac.2417
[8] O.B. Nazarevych, V. Melnyk, Information technology of eco-monitoring of the farm for
germination of microgreens based on the Raspberry Pi3 platform using, Proceedings of the
scientific conference of Ternopil National Technical University named after Ivan Pulyuy, Ternopil,
2019.
[9] StrikhaAI DD SLIDESHOW, 2019. URL: https://youtu.be/NYeijedEHA4.
[10] Grafana: The open observability platform. Grafana Labs. URL: https://grafana.com/.
[11] A. Matthes, J. Tacke, Python Programming: 3 Menuscripts Crash Course Coding With Python
Data Science, 2020.
[12] J. Joshua Thomas, P. Karagoz,, B. Bazeer Ahamed, P Vasant, Deep Learning Techniques and
Optimization Strategies in Big Data Analytics, 2020.
[13] A. Anisimova, Y. Doroshenko, S. Pogoriliy, Y. Dorogy, Programming of numerical methods in
Python language, Kyiv, Kyiv University Publishing and Printing Center, 2014.
[14] L. Chin, T. Dutta, NumPy Essentials, 2016.
[15] J. Howard, S. Gugger, Deep Learning for Coders with fastai and PyTorch: AI Applications Without
a PhD, 1st ed., O’Reilly Media, Inc., 2020.
[16] F. Chollet, others, Keras, 2015. URL: https://keras.io.
[17] J. Deng, W. Dong, R. Socher, ImageNet: A Large-Scale Hierarchical Image Database, CVPR09,
2009. doi:10.1109/CVPR.2009.5206848
[18] O. Parkhi, A. Vedaldi, A. Zisserman, C. Jawahar, Cats and Dogs, IEEE Conference on Computer
Vision and Pattern Recognition, 2012. doi: 10.1109/CVPR.2012.6248092.
[19] L. Smith, A disciplined approach to neural network hyper-parameters: Part 1 - learning rate, batch
size, momentum, and weight decay, CoRR 2018. doi:abs/1803.09820.
[20] Ioffe, S.; Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing
Internal Covariate Shift. CoRR 2015. doi:abs/1502.03167.