PLANT DISEASE CLASSIFICATION USING AI
- SPL DEEP LEARNING AND MACHINE
LEARNING
Leena Gupta*
Banasthali Vidyapith, Vidyapith, Rajasthan (India)
Institute of Information Technology and Management, Guru Gobind Singh
Indraprastha University, New Delhi (India)
email2leena@gmail.com
Vaibhav Vyas
Banasthali Vidyapith, Vidyapith, Rajasthan (India)
Reception: 27/02/2023 Acceptance:21/04/2023 Publication: 10/07/2023
Suggested citation:
Gupta, L. and Vyas, V. (2023). Plant Disease Classification Using AI - Spl
Deep Learning and Machine Learning. 3C Tecnología. Glosas de innovación
aplicada a la pyme, 12(2), 6 5 - 7 6 . https://doi.org/
10.17993/3ctecno.2023.v12n2e44.65-76
https://doi.org/10.17993/3ctecno.2023.v12n2e44.65-76
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254-4143
Ed.44 | Iss.12 | N.2 April - June 2023
65
ABSTRACT
The field of plant disease classification has recently been seen to be a vast area of
research. Recent years have witnessed a growing interest in the application of
artificial intelligence (AI) and machine learning (ML) techniques for various tasks.
Among these techniques, deep learning (DL) algorithms have received significant
attention and demonstrated remarkable results. This review article aims to give a
comprehensive overview of the current advancements in the field of plant disease
classification using AI and ML, with a focus on DL approaches. The paper will cover
key literature in the field, including recent advances and challenges, and will discuss
the most commonly used algorithms, such as Convolutional Neural Networks (CNNs),
Recurrent Neural Networks (RNNs), and Transfer Learning (TL). Additionally, the
review will highlight the various applications of AI in plant disease classification,
including the use of images, genomic data, and environmental data. The paper will
also provide insights into the limitations and opportunities of AI-based plant disease
classification, as well as future directions for research in this field. The goal of this
paper is to provide a comprehensive overview of the field and to serve as a useful
resource for researchers and practitioners in the area of plant disease classification
using AI and ML.
KEYWORDS
Plant disease classification, artificial intelligence, machine learning, deep learning,
convolutional neural networks, recurrent neural networks, transfer learning
INDEX
ABSTRACT
KEYWORDS
1. INTRODUCTION
2. LITERATURE REVIEW
3. RESEARCH METHODOLOGY
4. DISCUSSION
5. RECOMMENDATIONS
6. FUTURE DIRECTIONS
7. CONCLUSION:
REFERENCES
https://doi.org/10.17993/3ctecno.2023.v12n2e44.65-76
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254-4143
Ed.44 | Iss.12 | N.2 April - June 2023
66
ABSTRACT
The field of plant disease classification has recently been seen to be a vast area of
research. Recent years have witnessed a growing interest in the application of
artificial intelligence (AI) and machine learning (ML) techniques for various tasks.
Among these techniques, deep learning (DL) algorithms have received significant
attention and demonstrated remarkable results. This review article aims to give a
comprehensive overview of the current advancements in the field of plant disease
classification using AI and ML, with a focus on DL approaches. The paper will cover
key literature in the field, including recent advances and challenges, and will discuss
the most commonly used algorithms, such as Convolutional Neural Networks (CNNs),
Recurrent Neural Networks (RNNs), and Transfer Learning (TL). Additionally, the
review will highlight the various applications of AI in plant disease classification,
including the use of images, genomic data, and environmental data. The paper will
also provide insights into the limitations and opportunities of AI-based plant disease
classification, as well as future directions for research in this field. The goal of this
paper is to provide a comprehensive overview of the field and to serve as a useful
resource for researchers and practitioners in the area of plant disease classification
using AI and ML.
KEYWORDS
Plant disease classification, artificial intelligence, machine learning, deep learning,
convolutional neural networks, recurrent neural networks, transfer learning
INDEX
ABSTRACT
KEYWORDS
1. INTRODUCTION
2. LITERATURE REVIEW
3. RESEARCH METHODOLOGY
4. DISCUSSION
5. RECOMMENDATIONS
6. FUTURE DIRECTIONS
7. CONCLUSION:
REFERENCES
https://doi.org/10.17993/3ctecno.2023.v12n2e44.65-76
1. INTRODUCTION
Plant diseases caused by pathogens, environmental factors, or genetic issues have
a major impact on global food security, crop yield, and economic stability. Accurate
and timely diagnosis of plant diseases is crucial for the implementation of effective
control measures. Conventional methods of plant disease diagnosis rely on expert
knowledge, visual inspections, and laboratory tests, which can be time-consuming,
labor-intensive, and subjective. To overcome these limitations, the field of plant
disease classification has increasingly turned to the use of artificial intelligence (AI)
and machine learning (ML) techniques [1].
In recent years, deep learning (DL) algorithms have shown significant promise in
plant disease classification, particularly in the analysis of image data. Convolutional
Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transfer Learning
(TL) are among the most commonly used DL algorithms in this field. In addition to
images, AI has also been applied to the analysis of genomic and environmental data
for plant disease classification [2]. The purpose of this literature review is to examine
the advancements made in the field of plant disease diagnosis through the use of
artificial intelligence and machine learning techniques, particularly highlighting the
applications of deep learning methods. The paper will cover key literature in the field,
including recent advances and challenges, and will discuss the most commonly used
algorithms. The review will also highlight the various applications of AI in plant disease
classification and provide insights into the limitations and opportunities of AI-based
plant disease classification. The goal of this paper is to serve as a comprehensive
resource for researchers and practitioners in the area of plant disease classification
using AI and ML
Figure 1. Source from Ashworth, J. Et al. [17].
This literature review aims to comprehensively examine the current research in the
field of plant disease classification using AI and ML techniques, including a thorough
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analysis of prior studies. By examining the current state-of-the-art in this field, the
review will identify gaps in current knowledge and areas that need further
investigation. The review will also provide an overview of the different types of deep
learning algorithms used in plant disease classification, including Convolutional Neural
Networks (CNNs), Recurrent Neural Networks (RNNs), and Transfer Learning (TL),
and will discuss the strengths and limitations of these algorithms. The paper will also
compare the accuracy of these algorithms with traditional methods of plant disease
classification and discuss the challenges faced in applying AI and ML in this field.
Furthermore, the review will provide insights into the future directions for research and
development in the area of plant disease classification using AI and ML. This literature
review will serve as a comprehensive resource for researchers and practitioners in the
area of plant disease classification using AI and ML. By providing an overview of the
current state-of-the-art in this field and highlighting the opportunities and challenges,
the review will provide valuable insights for future research and development in this
area, helping to improve the accuracy and timeliness of plant disease diagnosis and
control.
2. LITERATURE REVIEW
Plant diseases caused by pathogens, environmental factors, or genetic issues have
a major impact on global food security, crop yield, and economic stability. Accurate
and timely diagnosis of plant diseases is crucial for the implementation of effective
control measures. Conventional methods of plant disease diagnosis rely on expert
knowledge, visual inspections, and laboratory tests, which can be time-consuming,
labor-intensive, and subjective. To overcome these limitations, the field of plant
disease classification has increasingly turned to the use of artificial intelligence (AI)
and machine learning (ML) techniques [4].
Figure 2. Source from Ngo, T. H. [18].
https://doi.org/10.17993/3ctecno.2023.v12n2e44.65-76
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68
analysis of prior studies. By examining the current state-of-the-art in this field, the
review will identify gaps in current knowledge and areas that need further
investigation. The review will also provide an overview of the different types of deep
learning algorithms used in plant disease classification, including Convolutional Neural
Networks (CNNs), Recurrent Neural Networks (RNNs), and Transfer Learning (TL),
and will discuss the strengths and limitations of these algorithms. The paper will also
compare the accuracy of these algorithms with traditional methods of plant disease
classification and discuss the challenges faced in applying AI and ML in this field.
Furthermore, the review will provide insights into the future directions for research and
development in the area of plant disease classification using AI and ML. This literature
review will serve as a comprehensive resource for researchers and practitioners in the
area of plant disease classification using AI and ML. By providing an overview of the
current state-of-the-art in this field and highlighting the opportunities and challenges,
the review will provide valuable insights for future research and development in this
area, helping to improve the accuracy and timeliness of plant disease diagnosis and
control.
2. LITERATURE REVIEW
Plant diseases caused by pathogens, environmental factors, or genetic issues have
a major impact on global food security, crop yield, and economic stability. Accurate
and timely diagnosis of plant diseases is crucial for the implementation of effective
control measures. Conventional methods of plant disease diagnosis rely on expert
knowledge, visual inspections, and laboratory tests, which can be time-consuming,
labor-intensive, and subjective. To overcome these limitations, the field of plant
disease classification has increasingly turned to the use of artificial intelligence (AI)
and machine learning (ML) techniques [4].
Figure 2. Source from Ngo, T. H. [18].
https://doi.org/10.17993/3ctecno.2023.v12n2e44.65-76
In recent years, deep learning (DL) algorithms have shown significant promise in
plant disease classification, particularly in the analysis of image data [5]
.
Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and
Transfer Learning (TL) are among the most commonly used DL algorithms in this field
[6]
. In addition to images, AI has also been applied to the analysis of genomic and
environmental data for plant disease classification [7].
The objective of this review paper is to furnish a comprehensive examination of the
cutting-edge advancements in the field of classifying plant diseases using artificial
intelligence and machine learning techniques, with a focus on DL approaches. The
paper will cover key literature in the field, including recent advances and challenges,
and will discuss the most commonly used algorithms. The review will also highlight the
various applications of AI in plant disease classification and provide insights into the
limitations and opportunities of AI-based plant disease classification. The goal of this
paper is to serve as a comprehensive resource for researchers and practitioners in
the area of plant disease classification using AI and ML.
AI and ML-based plant disease classification has revolutionized the way plant
diseases are diagnosed, providing a fast and accurate alternative to traditional
methods. The use of deep learning algorithms, specifically Convolutional Neural
Networks (CNNs), Recurrent Neural Networks (RNNs), and Transfer Learning (TL)
has shown exceptional results in the analysis of image data for plant disease
classification [5].
In recent years, researchers have applied AI to various types of data, including
image, genomic, and environmental data, for plant disease classification [6]
. For
instance, a study by Liu [8]
applied deep learning algorithms to analyze leaf images
for the classification of potato late blight, a prevalent plant disease caused by the
Phytophthora infest and fungus. The study demonstrated the effectiveness of DL in
accurately identifying infected and healthy potato leaves, with a high accuracy rate of
96.7% [7].
However, despite the many advances and benefits, AI-based plant disease
classification is not without its limitations. One major challenge is the need for large
amounts of high-quality and diverse data to train the models effectively. This is
particularly true in the case of rare or newly emerging diseases, which may have
limited data available [8]
In addition, the performance of AI-based plant disease
classification is dependent on the quality and type of data used, and may not always
be transferable to other datasets or regions.
AI and ML-based plant disease classification have shown significant promise in
improving the accuracy and speed of plant disease diagnosis. The use of deep
learning algorithms, specifically CNNs, RNNs, and TL, has been particularly effective
in the analysis of image data. However, the field is still evolving and more research is
needed to overcome the limitations and challenges, such as the need for large
amounts of diverse data and the dependence on the quality and type of data used.
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Artificial intelligence (AI) and machine learning (ML) are increasingly being used for
plant disease classification, with deep learning (DL) algorithms showing great promise
in the analysis of image data. Convolutional Neural Networks (CNNs), Recurrent
Neural Networks (RNNs), and Transfer Learning (TL) are among the most commonly
used DL algorithms in the field [5]
. However, the use of AI and ML for plant disease
classification is not without limitations and challenges. The need for large amounts of
high-quality and diverse data is one significant challenge, and the performance of AI-
based methods is dependent on the quality and type of data used [10]
. Over-reliance
on AI technology can also lead to a loss of expert knowledge and skills [13]
. It is
important to be guided by ethical considerations, such as data privacy, data bias, and
responsible use of technology [14]
. Practitioners need to understand the limitations
and strengths of AI-based methods and use them in conjunction with traditional
methods for a comprehensive diagnosis. Further research is needed to address these
limitations and challenges [10],[11],[13],[14]
The use of AI and ML in plant disease classification has gained much attention in
recent years as a way to overcome the limitations of conventional methods such as
subjectivity and dependence on expert knowledge. Despite the promising results of
AI-based methods, it is important to consider the limitations and ethical considerations
associated with the use of these technologies. One of the main limitations of AI-based
methods is the need for large amounts of high-quality data, which can be difficult to
obtain promptly. Additionally, the performance of AI-based methods is highly
dependent on the quality and diversity of the data used, which can lead to issues such
as data bias [10]
. It is also important to consider the ethical implications of using AI
technology, such as data privacy, responsible use, and data bias, as well as the
potential loss of expert knowledge and skills [14].
Another limitation of AI-based methods is the difficulty in interpreting the results, as
the decision-making process of these models can be complex and not transparent. To
address this issue, researchers are developing interpretable AI models that provide
explanations for their predictions, allowing practitioners to make more informed
decisions [15]
. Furthermore, it is important to understand the strengths and limitations
of AI-based methods and use them in conjunction with traditional methods to provide
a comprehensive diagnosis [13].
The applications of AI in plant disease classification are wide-ranging and include
the analysis of image, genomic, and environmental data. The use of DL algorithms,
particularly CNNs, has shown great promise in the analysis of image data, as these
algorithms can learn complex patterns and relationships within the data [5]. The use of
AI in the analysis of genomic and environmental data provides additional information
for disease classification, allowing for a more comprehensive diagnosis [6].
In conclusion, the field of plant disease classification using AI and ML is rapidly
evolving, with many exciting advancements and opportunities. Despite the limitations
and challenges, the use of AI-based methods has the potential to revolutionize plant
disease classification, leading to more accurate and timely diagnosis, and improved
https://doi.org/10.17993/3ctecno.2023.v12n2e44.65-76
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Ed.44 | Iss.12 | N.2 April - June 2023
70
Artificial intelligence (AI) and machine learning (ML) are increasingly being used for
plant disease classification, with deep learning (DL) algorithms showing great promise
in the analysis of image data. Convolutional Neural Networks (CNNs), Recurrent
Neural Networks (RNNs), and Transfer Learning (TL) are among the most commonly
used DL algorithms in the field [5]. However, the use of AI and ML for plant disease
classification is not without limitations and challenges. The need for large amounts of
high-quality and diverse data is one significant challenge, and the performance of AI-
based methods is dependent on the quality and type of data used [10]. Over-reliance
on AI technology can also lead to a loss of expert knowledge and skills [13]. It is
important to be guided by ethical considerations, such as data privacy, data bias, and
responsible use of technology [14]. Practitioners need to understand the limitations
and strengths of AI-based methods and use them in conjunction with traditional
methods for a comprehensive diagnosis. Further research is needed to address these
limitations and challenges [10],[11],[13],[14]
The use of AI and ML in plant disease classification has gained much attention in
recent years as a way to overcome the limitations of conventional methods such as
subjectivity and dependence on expert knowledge. Despite the promising results of
AI-based methods, it is important to consider the limitations and ethical considerations
associated with the use of these technologies. One of the main limitations of AI-based
methods is the need for large amounts of high-quality data, which can be difficult to
obtain promptly. Additionally, the performance of AI-based methods is highly
dependent on the quality and diversity of the data used, which can lead to issues such
as data bias [10]. It is also important to consider the ethical implications of using AI
technology, such as data privacy, responsible use, and data bias, as well as the
potential loss of expert knowledge and skills [14].
Another limitation of AI-based methods is the difficulty in interpreting the results, as
the decision-making process of these models can be complex and not transparent. To
address this issue, researchers are developing interpretable AI models that provide
explanations for their predictions, allowing practitioners to make more informed
decisions [15]. Furthermore, it is important to understand the strengths and limitations
of AI-based methods and use them in conjunction with traditional methods to provide
a comprehensive diagnosis [13].
The applications of AI in plant disease classification are wide-ranging and include
the analysis of image, genomic, and environmental data. The use of DL algorithms,
particularly CNNs, has shown great promise in the analysis of image data, as these
algorithms can learn complex patterns and relationships within the data [5]. The use of
AI in the analysis of genomic and environmental data provides additional information
for disease classification, allowing for a more comprehensive diagnosis [6].
In conclusion, the field of plant disease classification using AI and ML is rapidly
evolving, with many exciting advancements and opportunities. Despite the limitations
and challenges, the use of AI-based methods has the potential to revolutionize plant
disease classification, leading to more accurate and timely diagnosis, and improved
https://doi.org/10.17993/3ctecno.2023.v12n2e44.65-76
crop yields. Further research is needed to address the limitations and challenges of
AI-based methods and to ensure that they are used ethically and responsibly.
3. RESEARCH METHODOLOGY
The methodology for this literature review paper aims to provide a comprehensive
overview of the current state-of-the-art in plant disease classification using AI and ML,
specifically deep learning algorithms. A systematic approach will be followed to
identify and evaluate relevant studies in this field. The search strategy will involve
conducting a comprehensive search using academic databases such as PubMed,
Web of Science, and Google Scholar, using relevant keywords. The articles will then
be screened based on their relevance to the topic, and only those that employ deep-
learning algorithms for plant disease classification will be considered. The relevant
information from each article will be extracted, including the authors, publication year,
study design, data source, algorithm used, accuracy, and conclusions. The quality of
the studies will be assessed based on their methodology, results, and generalizability
of findings, and only high-quality studies with robust results will be included in the
review. The extracted data will then be analyzed and synthesized to provide a
comprehensive overview of the field, discussing the most commonly used algorithms,
applications, and limitations. This methodology will ensure that the review is
systematic, rigorous, and comprehensive, providing a valuable resource for
researchers and practitioners in the field of plant disease classification using AI and
ML
In addition to the systematic search and selection of studies, the review will also
include a discussion of the current trends and challenges in the field. The strengths
and weaknesses of the various deep learning algorithms used for plant disease
classification will be compared and contrasted, and their suitability for different types
of plant diseases and data sources will be evaluated. The role of transfer learning,
ensemble learning, and other advanced deep learning techniques in plant disease
classification will also be discussed.
The review will also consider the ethical and legal implications of using AI and ML
in plant disease classification, including data privacy and security, intellectual property
rights, and the potential impact on agriculture and food security. The potential benefits
and risks of using AI and ML in this field will be evaluated, and recommendations for
future research and development will be provided.
The results of the literature review will be presented clearly and concisely, with
tables and figures used to summarize key findings and trends. The review will
conclude with a discussion of the implications of the findings for researchers,
practitioners, and policymakers, and will highlight areas for future research and
development in the field of plant disease classification using AI and ML. The review
will be written in a clear and accessible style, suitable for a broad audience of
researchers, practitioners, and students with an interest in the field.
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This review paper endeavors to offer a comprehensive examination of the most
recent advancements in the field of plant disease classification through the utilization
of AI and ML techniques, specifically deep learning algorithms. By synthesizing the
available research in this field, the review will provide valuable insights into the most
promising approaches and opportunities for future research and development in this
important area.
4. DISCUSSION
The utilization of AI and ML technologies in the field of plant disease classification
has gained significant attention in recent times, with DL methods being a prominent
contributor. This literature review aims to provide a comprehensive examination of the
current advancements and state-of-the-art in plant disease classification utilizing AI
and ML techniques, with a focus on DL techniques. This paper will provide a
discussion of the key literature in the field, including recent advancements,
challenges, and commonly used algorithms, as well as highlight the various
applications of AI in plant disease classification.
DL algorithms, such as Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), and Transfer Learning (TL), have shown significant promise in the
analysis of image data for plant disease classification. These algorithms are capable
of learning complex patterns and relationships within the data, leading to more
accurate diagnoses [5].
In addition to image data, AI has also been applied to the
analysis of genomic and environmental data for plant disease classification, providing
additional information for a more comprehensive diagnosis [6].
Despite the promising results of AI-based methods, some limitations and
challenges need to be considered. One of the main limitations is the need for large
amounts of high-quality data, which can be difficult to obtain promptly [10]
. The
performance of AI-based methods is also highly dependent on the quality and
diversity of the data used, which can lead to issues such as data bias [10]. There is
also a need to consider the ethical implications of using AI technology, including data
privacy, responsible use, and data bias [14].
Another limitation of AI-based methods is the difficulty in interpreting the results, as
the decision-making process of these models can be complex and not transparent. To
address this issue, researchers are developing interpretable AI models that provide
explanations for their predictions, allowing practitioners to make more informed
decisions [15]. Furthermore, it is important to understand the strengths and limitations
of AI-based methods and use them in conjunction with traditional methods to provide
a comprehensive diagnosis [13].
The use of AI and ML in plant disease classification has shown significant promise
in recent years, with DL algorithms being particularly effective in the analysis of image
data. Despite the limitations and challenges, the use of AI-based methods has the
https://doi.org/10.17993/3ctecno.2023.v12n2e44.65-76
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254-4143
Ed.44 | Iss.12 | N.2 April - June 2023
72
This review paper endeavors to offer a comprehensive examination of the most
recent advancements in the field of plant disease classification through the utilization
of AI and ML techniques, specifically deep learning algorithms. By synthesizing the
available research in this field, the review will provide valuable insights into the most
promising approaches and opportunities for future research and development in this
important area.
4. DISCUSSION
The utilization of AI and ML technologies in the field of plant disease classification
has gained significant attention in recent times, with DL methods being a prominent
contributor. This literature review aims to provide a comprehensive examination of the
current advancements and state-of-the-art in plant disease classification utilizing AI
and ML techniques, with a focus on DL techniques. This paper will provide a
discussion of the key literature in the field, including recent advancements,
challenges, and commonly used algorithms, as well as highlight the various
applications of AI in plant disease classification.
DL algorithms, such as Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), and Transfer Learning (TL), have shown significant promise in the
analysis of image data for plant disease classification. These algorithms are capable
of learning complex patterns and relationships within the data, leading to more
accurate diagnoses [5]. In addition to image data, AI has also been applied to the
analysis of genomic and environmental data for plant disease classification, providing
additional information for a more comprehensive diagnosis [6].
Despite the promising results of AI-based methods, some limitations and
challenges need to be considered. One of the main limitations is the need for large
amounts of high-quality data, which can be difficult to obtain promptly [10]. The
performance of AI-based methods is also highly dependent on the quality and
diversity of the data used, which can lead to issues such as data bias [10]. There is
also a need to consider the ethical implications of using AI technology, including data
privacy, responsible use, and data bias [14].
Another limitation of AI-based methods is the difficulty in interpreting the results, as
the decision-making process of these models can be complex and not transparent. To
address this issue, researchers are developing interpretable AI models that provide
explanations for their predictions, allowing practitioners to make more informed
decisions [15]. Furthermore, it is important to understand the strengths and limitations
of AI-based methods and use them in conjunction with traditional methods to provide
a comprehensive diagnosis [13].
The use of AI and ML in plant disease classification has shown significant promise
in recent years, with DL algorithms being particularly effective in the analysis of image
data. Despite the limitations and challenges, the use of AI-based methods has the
https://doi.org/10.17993/3ctecno.2023.v12n2e44.65-76
potential to revolutionize plant disease classification, leading to more accurate and
timely diagnosis, and improved crop yields. Further research is needed to address the
limitations and challenges of AI-based methods and to ensure that they are used
ethically and responsibly.
5. RECOMMENDATIONS
Based on the current state-of-the-art in plant disease classification using AI and
ML, with a focus on DL approaches, the following recommendations can be made for
future research:
•Data Collection:
There is a need for more standardized and larger datasets for
plant disease classification, to train and evaluate the performance of AI algorithms.
•Algorithm Development:
Further development of deep learning algorithms to
improve their accuracy, robustness, and generalizability in plant disease
classification.
•Integration of Multiple Data Sources:
To improve the accuracy of plant disease
classification, the integration of multiple data sources, such as images, genomic
data, and environmental data, needs to be explored.
•Evaluation of Algorithms in Real-World Scenarios:
The evaluation of AI
algorithms in real-world scenarios, such as in field conditions, needs to be
performed to validate their performance and generalizability.
•Interdisciplinary Collaboration: Collaboration between researchers from different
disciplines, such as computer science, biology, and agriculture, is needed to
address the complex and interdisciplinary challenges in plant disease classification
using AI and ML.
6. FUTURE DIRECTIONS
In the future, there are several directions that the field of plant disease classification
using AI and ML could take. One possibility is the integration of multiple sources of
data, such as images, genomic, and environmental data, to improve the accuracy and
reliability of plant disease classification. This could be achieved through the use of
multi-modal deep learning algorithms, such as those that combine CNNs and RNNs.
Another direction could be the development of more sophisticated and interpretable
deep learning models, such as those that utilize attention mechanisms and
reinforcement learning, to better understand the decision-making process of the
model.
Another area of future development could be the deployment of AI-based plant
disease classification systems in resource-limited settings, such as developing
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countries, where access to expert knowledge and laboratory facilities is limited. This
would require the development of low-cost and easy-to-use AI systems that can be
used by non-experts in the field.
Finally, there is a growing need to address ethical and privacy concerns related to
the use of AI in plant disease classification. This includes ensuring that data collected
from crops and other sources is protected and used only for disease classification and
that the use of AI does not have any negative impact on the privacy of farmers or
other stakeholders.
7. CONCLUSION:
In conclusion, the future of plant disease classification using AI and ML holds great
promise, with the potential to revolutionize the way that plant diseases are diagnosed
and controlled. The field must continue to evolve and address current limitations and
impact.
challenges associated with AI-based plant disease classification. These include issues
potential for overfitting and bias.
Figure 3. Source from Albattah, W. Et al. [19].
To continue to advance the field and overcome these limitations, it is recommended
that further research be conducted in areas such as large-scale data collection, the
development of novel deep learning algorithms, and the integration of AI with other
improve the interpretability and transparency of AI models, as well as to ensure their
ethical and responsible use.
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countries, where access to expert knowledge and laboratory facilities is limited. This
would require the development of low-cost and easy-to-use AI systems that can be
used by non-experts in the field.
Finally, there is a growing need to address ethical and privacy concerns related to
the use of AI in plant disease classification. This includes ensuring that data collected
from crops and other sources is protected and used only for disease classification and
that the use of AI does not have any negative impact on the privacy of farmers or
other stakeholders.
7. CONCLUSION:
In conclusion, the future of plant disease classification using AI and ML holds great
promise, with the potential to revolutionize the way that plant diseases are diagnosed
and controlled. The field must continue to evolve and address current limitations and
challenges while exploring new directions, to ensure its continued success and
impact. However, despite the potential benefits, there are also limitations and
challenges associated with AI-based plant disease classification. These include issues
such as limited training data, the need for large computing resources, and the
potential for overfitting and bias.
Figure 3. Source from Albattah, W. Et al. [19].
To continue to advance the field and overcome these limitations, it is recommended
that further research be conducted in areas such as large-scale data collection, the
development of novel deep learning algorithms, and the integration of AI with other
conventional diagnostic methods. Additionally, further efforts should be made to
improve the interpretability and transparency of AI models, as well as to ensure their
ethical and responsible use.
https://doi.org/10.17993/3ctecno.2023.v12n2e44.65-76
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