INTRODUCTION
In our growing digital world, machine learning
is at the core of data science [1]. Machine learning
techniques and computing power play an essential role
in the analysis of collected data. They have focused
on representing input data and generalizing learned
predictive models to future data [2]. Data representation
has a dramatic effect on machine learner performance.
Proper data representation can lead to high performance
even with straightforward machine learning. In contrast,
poor representation of data with advanced complex
machine learning can lead to decreased performance [3].
Deep learning is an important branch of machine
learning that has emerged to achieve impressive
results in the field of artificial intelligence. Its strength
is in its ability to automatically create powerful data
representation through layers of learning without human
intervention, thus ensuring great precision of analysis
[4]. In comparison with shallow learning algorithms,
deep learning uses supervised and unsupervised
techniques and machine-learning approaches to
automatically learn the hierarchical representation
of multi-level data for feature classification [5, 6].
This deep learning composition is inspired by the
representation of human brain for processing natural
signals. It has attracted the academic community lately
due to its performance in different research fields, such
as agriculture.
More recently, a number of technologies common
in industry have been applied to agriculture, such as
remote sensing, the Internet of Things (IoT), and robotic
platforms, leading to the concept of “smart agriculture”
[7, 8]. Smart agriculture is important to face
agricultural production challenges in terms of
productivity, environmental impact, and food security.
To tackle these challenges, it is necessary to analyze
agricultural ecosystems, which involves constant
monitoring of different variables. These operations
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create data that can be used as input values and
processed with varying analysis techniques in deep
learning to identify weeds, diseases, etc.
The objects of our study were two neural networks,
namely the Convolutional Neural Network (CNN) and
the Recurrent Neural Network (RNN). A CNN is an
artificial neural network used for image recognition and
processing [9]. It is specially intended for handling pixel
information. A CNN is viewed as a powerful artificial
intelligence (AI) image processing system that employs
deep learning to perform generative and descriptive
tasks. It commonly uses machine vision, which includes
image and video recognition, as well as recommendation
systems and natural language processing (NPL) [10].
A RNN, in its turn, is an artificial neural network
essentially utilized in discourse identification and
programmed regular language treatment. RNNs
are intended to perceive successive attributes and
information utilization patterns needed to foresee likely
scenarios [11]. Therefore, the use of a RNN in image
classification requires optimization with the long shortterm
memory (LSTM) technique to reduce the risk of
gradient vanishing [12]. In this study, we compared these
two techniques of deep learning with other state of the
art techniques in order to create an optimized model and
train it to detect weeds. We aimed to create an intelligent
system that could detect weeds and spray them locally
to avoid wasting herbicides and protect the environment.
STUDY OBJECTS AND METHODS
In this study, we used various methods, devices,
techniques, and libraries to study deep learning in
crop planting and train the deep learning models on a
database that includes images for relevant and smart
weed detection. The following sections contain complete
descriptions of these methods.
Deep learning. This method came to expand
machine learning (ML) and added a lot of complexity
and depth to the model based on artificial neural
networks (ANNs). A neural network is a system
designed to resemble the neural organization of the
human brain. A more complex definition would be
that a neural network is a computational model made
up of artificial neurons connected to each other and
resulting in a network architecture. This architecture
has specific parameters called weights. Adjusting
them, we can enhance the accuracy of our model.
This type of networks contains many layers, each
with a specific mission. Their number determines
the complexity of the network. We can find three
layers in a small neural network: the input layer,
the hidden layer, and the output layer. Each of these
layers is comprised of hubs called “nodes” and has
a given assignment, as the name suggests. The input
layer is liable for recovering information and giving
it to the following layer. The hidden layer plays
out all the back-end assignments of the calculation
and change of information utilizing different
capacities that permit its portrayal in a progressive
manner through a few degrees of abstraction [13].
There can be multiple layers hidden in a neural network
as needed. Several parameters influence the laying
of various layers, and the goal is always to obtain a
high degree of accuracy. The output layer passes the
consequence of the hidden layer, as shown in Figure 1.
Deep learning has various applications ranging
from natural language to image processing. Its
important advantage is the learning of functionalities,
or automatic extraction of functionalities from raw data.
Functionalities of more significant levels of a progressive
system are framed by the arrangement of lower level
functionalities.
Deep learning can tackle more perplexing issues
well and rapidly by utilizing more complex layers,
which permits enormous parallelization. These complex
algorithms increase classification accuracy and reduce
errors, provided there are large, well-prepared and
sufficient data sets to describe the problem and the layers
are well constructed.
The profoundly progressive construction and great
learning capacity of deep learning algorithms permit
them to perform classification and expectation with
high accuracy. They are versatile and adaptable to a
wide range of exceptionally complex problems. Deep
learning has numerous applications in data management
(e.g. video, images), tending to be applied to any type
of information, like natural language, speech, and
continuous or point data [15].
The main drawbacks of deep learning could be long
learning time and a need for powerful hardware suitable
for parallel programming (Graphics Processing Unit,
Field-programmable Gate Array), while conventional
strategies like Scale Invariant Feature Transform (SIFT)
or Support Vector Machine (SVM) have less difficult
learning measures [16]. In any case, the testing time
is quicker in deep learning tools and most of them are
more accurate. The subsections below present the most
common deep learning techniques.
Figure 1 Artificial neural network [14]
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Convolutional Neural Network (CNN). In deep
learning, convolutional neural networks (CNNs) are
a class of profound feedforward ANNs that has been
effectively applied to computer vision. In contrast to
an ANN, whose tediously prepared prerequisites may
be unfeasible in some huge scope issues, a CNN can
learn complex issues quite rapidly because of weight
sharing and more complex layers utilized.Convolutional
neural networks can increase their likelihood of correct
classifications, provided there are sufficiently large
data sets (i.e. hundreds to thousands of measurements,
depending on the complexity of the problem under
investigation) available to describe the problem. They
are made up of different convolutional layers, grouped
and/or fully connected. Convolutional layers perform
operations to extract distinct features from the input
images whose dimensionality is reduced by grouping
the layers together, while fully connected layers
perform classification operations. They usually exploit
the learned high-level functionalities at the last layer
in order to classify the input images into predefined
classes. Many organizations have successfully applied
this technique in various fields, such as agriculture
where it accounts for 80% of all methods used [17]. An
example of CNN architecture is shown in Figure 2 [18].
Fig. 2 shows different representations of the training
dataset created by applying various convolutions to
certain layers of the network. Training always begins
as the most general at the level of the first layers, which
are larger, and becomes more specific at the level of the
deeper layers.
A combination of convolutional layers and dense
layers makes the production of good precision results
possible. There are various “successful” architectures
that researchers commonly use to start building
their models instead of starting from scratch. These
include AlexNet, the Visual Geometry Group (VGG)
(shown in Figure 2), GoogleNet, and Inception-
ResNet, which uses what we call ‘transfer learning.’
Besides, there are various tools and platforms that
allow researchers to experience deep learning. The
most popular are TensorFlow, Theano, Keras (an
application programming interface on top of TensorFlow
and Theano), PyTorch, Caffe, TFLearn, Pylearn2,
and Matlab. Some of these tools (e.g. Caffe, Theano)
integrate popular platforms such as those mentioned
above (e.g. AlexNet, VGG, GoogleNet) in the form of
libraries or classes [19].
Recurrent Neural Network (RNN). Recurrent
neural networks (RNNs) are another type of neural
networks that is used to solve difficult machine learning
problems involving sequences of inputs. Some RNN
architectures for sequence prediction issues are:
1. One-to-Many: sequence yield, for picture captioning;
2. Many-to-One: sequence in input contribution, for
sentiment investigation; and
3. Many-to-Many: sequence for synchronized input,
machine translation and output sequences, typically
processing operations for video classification.
RNNs have connections with loops, adding feedback
and memory to networks over time. This memory
has come to replace traditional learning that relies on
individual patterns. It allows this type of network to
learn and generalize through a sequence of inputs.
When an out is produced, it is copied and sent back
to the recurrent network [20]. To make a decision, it
considers the current entry and the exit it learned from
the previous entry. An example of RNN architecture is
shown in Figure 3.
Fig. 3 shows an RNN for the entire sequence.
For example, if a sentence consists of five words, the
network will unwind into a neural network of five layers,
one layer for each word. The formulas that govern
calculations in an RNN are as follows:
– xt entered at time t.
– U; V; W are the parameters that the network will learn
from the training data.
Figure 2 An example of CNN architecture (VGG)
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– St is the hidden state at time t. It is the ‘memory’ of the
network. St is calculated based on the previous hidden
state and the entry to the current step:
(1)
where f is a nonlinear function such as ReLu or
Hyperbolic tangent (TanH).
Ot (2) is the exit at time t. A well-known example
here is the prediction of a word in the sentence. When
we want to know the next word in the sentence, it will
show a vector of possibilities in the vocabulary.
(2)
Deep learning applications in agriculture.
Applications of deep learning in agriculture are
spread across several areas, the most popular being
weed identification, land cover classification, plant
recognition, fruit counting, and crop type classification.
According to Figure 4, which shows deep learning
models in crop planting, CNNs and RNNs account for
80% and only 5% of all methods, respectively.
The low ratio of RNNs in agriculture is due to the
fact that traditional RNNs have unstable behavior with
the vanishing gradient and therefore are not used in
image classification. For this reason, we will discuss
an advanced RNN in this article that uses the LSTM
technique for image classification in weed identification.
Weeds are plants that grow spontaneously on
agricultural soils where they are unwanted. The growth
of these plants causes competition with crops for space,
light, and water. Herbicides are the first tool used to
fight against weeds, but they present secondary risks for
man and nature. Therefore, we need to think about ways
to reduce their effects. In this study, we proposed an
intelligent system that automatically detects weeds and
contributes to localized spraying of infected areas only.
To identify weeds, we processed photos of crops and
classified them to apply specific herbicides.
Weeds can be classified according to the size of their
leaves into grass categories (dicot and monocot). This
division is adequate since grasses and broadleaf weeds
are differentiated in treatment due to the selectivity
of some herbicides to the specific group. Herbicide
application works best if treatment is targeted at the
specific class of weed. Several studies have shown
the success of CNNs in comparison with RNNs and
other deep learning techniques used for weed identification
[21–23].
Technical details. From a technical standpoint,
almost all of the research has used popular CNN
architectures such as AlexNet, VGG, and Inception-
ResNet, or combined CNNs with other procedures. All
the experiments that exploited a well-known system also
used a deep learning framework, with Caffe being the
most famous. Noteworthily, most studies that only had
small datasets to train their CNN models exploited the
power of data augmentation to artificially increase the
number of training images to enhance their accuracy.
They used translations, transposition, and reflections,
as well as modified the intensities of the RGB Channels,
and that is what we did to prepare our dataset.
Also, the majority of related works included image
preprocessing steps, where each image in the dataset
was scaled down to a smaller size before being used
as input into the model, such as 256×256, 128×128,
96×96, 60×60 pixels, or converted to CNN grayscale
architectures to take advantage of transfer learning.
Transfer learning exploits already existing knowledge of
certain related tasks in order to increase the efficiency
of learning the problem at hand, refining pre-trained
models when it is impossible to train the network
on the data from the beginning due to a small set of
training data or the resolution of a complex problem.
We can get significant results if we rely on weights
from other models that were previously trained on big
datasets [24]. In our case, these are preformed CNNs
that have already been trained on datasets related to
different class numbers. The authors of related work
mainly used large datasets to train their CNN models,
in some cases containing thousands of images. Some
of them came from well-known and publicly available
sources such as PlantVillage, MalayaKew, LifeCLEF,
and UC Merced. In contrast, some authors produced
their own datasets for their research needs, as we
can see in Table 1. The table also shows whether the
authors compared their CNN-based approach with
other techniques used to solve the problem under study,
as well as the precision of each model. Therefore,
conventional precision of the model’s response must be
exactly t Figure 4 Deep learning methods in crop planting he expected response.
Figure 3 An RNN (left) and its unrolled version (right)
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Application of an optimized RNN in weed
detection. All the studies referred to above have used
CNN architectures to create deep learning models that
detect weeds. Also, they have compared them with other
models in terms of accuracy and error. RNNs, however,
do not feature much in these works, which means they
are hardly used in this field of agriculture, especially for
image classification. That is why we aimed to create an
optimized RNN model with the long short-term memory
(LSTM) technique as an alternative to the traditional
RNN [25]. We trained this RNN-LSTM model on
our dataset in order to compare the results with those
obtained by the CNN above. First, we loaded the
dataset that was already used in a previous experiment.
This database contained a set of weeds known in our
region and spread over four classes. Then, we built
an RNN model and trained it on the database created
using the following parameters: inputs = 28, step = 28,
neuron = 150, output = 10, epoch = 20, Softmax
function. LSTMs were introduced in our model in order
to improve the RNN standards. The RNN model we
created is shown in Figure 5.
To design this deep learning model, we used the
python code, as shown in Figure 6.
Table 1 Application of deep learning in agriculture (weed detection)
Agricultural
area
Description
of the problem
Data DL Architecture
DL Model Accuracy Comparison with
other methods
References
Weed
detection
Detection and
classification
of weeds
in soybean crops
400 images of crops
captured by the
authors with a drone
CNN CaffeNet
(CAFFE FW)
98% SVM: 98%
AdaBoost: 98.2%
Random
Forest: 96%
[21]
Weed
detection
Weed detection
and classification by
spectral band analysis
200 hyperspectral
images with 61 bands
CNN MatConvnet 94.72 % HoG: 74.34% [22]
Weed
detection
Accelerate a DL with
FPGA approach
to classification
Weed with 8 classes
18000 weed images
from the DeepWeedX
dataset
CNN VGG-16,
DenseNet-128-10
90.08% ResNet:
95.7%
[23]
Figure 5 RNN model architecture
Figure 6 Python code used to create the RNN model
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The above code includes the initialization function
__init__, which defines some variables. The fully
connected layer follows the basic RNN by “self.FC”
that allows data to flow through the RNN layer and
then through the fully connected layer, also using the
function “init_hidden” that exploits hidden weights with
zero values.
The database used to train the proposed RNN and
CNN models comprised about 3000 images taken in
a wheat field with a digital camera (Sony 6000) under
different lighting conditions (from morning to afternoon
in sunny and cloudy weather). We combined these
images with those from the online Kaggle repository
dataset. The images featured four types of weeds
that propagate in our region. They corresponded to
four classes to be identified by our model (Fig. 7). A
well-prepared database is a very important factor in
deep learning. We applied preprocessing and dataaugmentation
techniques on the same data to generate
other learning examples through different manipulations
(flip, orientation, contrast, crop, exposure, noise,
brightness…). These techniques reduced the model’s
performance.
RESULTS AND DISCUSSION
Before training the model, we added all necessary
functions (Fig. 8). Firstly, we specified the device
runtime to use during training, determined in the
python code by torch.device(...). This function gives
commands to the program to use the GPU (Graphics
Processing Unit) if it is available. Otherwise the CPU
(Central Processing Unit) will be used as a default
device. The GPU acts as a specialized microprocessor.
It is swift and efficient for matrix multiplication and
convolution. Parallelism is often cited as an explanation.
The GPU is optimized for bandwidth, while the CPU
is optimized for latency. Therefore, the CPU has less
latency, but its capacity is lower than that of the GPU.
In other words, CPUs are not suited to handle massive
amounts of data, while GPUs can provide large amounts
Figure 7 The dataset samples
Figure 8 Addition of necessary functions
Figure 9 Training accuracy of the RNN model Figure 10 The error rate of the RNN model
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of memory. The CPU is responsible for performing all
kinds of calculations, whereas the GPU only handles
graphics calculations. Since our dataset was not large,
we used an i7 CPU with 2.80GHz and 8G of RAM.
Then, we create an instance of the model with the
ImageRNN(...) function, with its own configuration
and parameters, the criterion represents the function
we will use to get the loss of the designed model. To
do this process, it is sufficient to use the function:
nn.CrossEntropyLoss(), which is a softmax function
utilized as boundaries log probabilities and followed by
a negative log-likelihood loss activity over the output of
the model. The code shows how to provide this to the
criterion.
We add an optimization function that recalculates the
weights based on the current loss and updates it. This
is done using the Optim.adam function, which requires
setting the model parameters and learning rate. To
display the results and get the accuracy, we will use the
get_accuracy(...) function, which computes the accuracy
of the model given the log probabilities and target values
for each epoch. All these functions are shown in the
figure below.
After training the model on 20 epochs, we obtained
relevant results (Figs. 9 and 10).
There are different ways and measures to evaluate
the performance of a classification model. The
performance measures often used are precision, kappa,
recall, and others [26]. We were therefore interested
in the model’s accuracy and error rate. Accuracy is
a proportion of genuine expectations in relation to
the absolute number of input pictures. The error rate
measures the difference between the model’s predictions
and the real images in the training set [27]. Figs. 9
and 10 show the accuracy and error for each epoch. In
particular, Figure 10 shows how the neural network
gradually decreased the error to arrive at 0.9. According
to Figure 9, the training accuracy reached 97.58% due
to a set of factors, such as the dataset, optimization
function, and the adjustment of weights and biases.
Fig. 11 shows how the model performed on the test
images. The display of predictions on test images is a
technique to test the final solution in order to confirm
the real predictive power of the network. We computed
the accuracy on our dataset to test how well the model
performed on the test images. Fig. 11 shows a value of
96.40%, which means that the predictions on the test
images were well classified.
These results indicated a good performance of
the LSTM-RNN on our dataset. According to the
three figures above, this model updates with every
step, adjusting weights to reduce error and increasing
accuracy using a backpropagation algorithm and
gradient descent. In addition to the studies that were
based on CNNs, we also built a CNN-based model
(Fig. 12) and trained it on the same dataset that we used
in the RNN experiment.
Our results were close to those reported by the
authors referred to above.
The training was run on our local machine and after
a few times it reached 98% validation accuracy. The
model showed good results after 9 hours of training.
Fig. 13 shows accuracy taken from Tensboard.
To sum up, CNNs are preferred for interpreting
visual data, sparse data or data that does not come in
sequence. Recurrent neural networks, however, are
designed to recognize sequential or temporal data.
They make better predictions by considering the order
or sequence of data concerning previous or next data
nodes. Applications where CNNs are particularly useful
include face detection, medical analysis, drug discovery,
and image analysis. RNNs are useful for linguistic
translation, entity extraction, conversational intelligence,
sentiment analysis, and speech analysis. Our experiment
also showed that RNNs can be used to classify images if
we add the LSTM technique. Based on literature and our
Figure 11 Test accuracy of the RNN model
Figure 12 CNN basic configuration
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results, we compared the characteristics of RNNs and
CNNs and summarized them in Table 2.
Our experimentation clearly shows why CNNs are
so widely used in agriculture despite the abundance
of other deep learning techniques. In addition, we
proved that a RNN can also be used to detect weeds,
but with less efficiency and more effort. Therefore, we
recommend the CNN as the best suited deep learning
technique for more efficient weed detection as the basis
for smarter precision farming.
CONCLUSION
Precision agriculture encompasses several areas
of application, such as plant and leaf disease detection,
land cover classification, plant recognition, and weed
identification to name the most common uses. The
development of precision agriculture requires new
monitoring, control, and information technologies,
including deep learning. This paper presents an
overview and a comparative study of deep learning
tools in crop planting. First, we looked at agriculture
to describe its current problems, specifically weed
detection. Then, we listed the technical characteristics of
popular deep learning techniques. After that, we created
a CNN and a RNN and trained them on our dataset to
compare their overall accuracy. The results showed that
the optimized RNN model (RNN with LSTM) can also
be used to classify images with acceptable accuracy.
Hence, a RNN combined with the LSTM is suitable for
detecting weeds among other techniques, but a CNN
always comes first in terms of speed and accuracy. In
future work, we intend to use other metrics to compare
the results, such as recall and Kappa. We will also try
to develop a platform combining the RNN with the
CNN to achieve the best accuracy. These results will be
used to build an intelligent system based on Raspberry
Pi 4 that can detect weeds in real time and spray them
in their area.
CONTRIBUTION
The authors were equally involved in writing the
manuscript and are equally responsible for plagiarism.
CONFLICT OF INTEREST
The authors declare no conflict of interest regarding
the publication of this article.
ACKNOWLEDGMENTS
This research is part of the Digital Agriculture
doctorate project that involves a group of doctors from
the Limati Laboratory at Sultan Moulay Slimane
University in Morocco.

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