Real-Time Plant Health Detection Using Deep Convolutional Neural Networks Article Swipe
Mahnoor Khalid
,
Muhammad Shahzad Sarfraz
,
Uzair Iqbal
,
Muhammad Umar Aftab
,
Gniewko Niedbała
,
Hafiz Tayyab Rauf
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture13020510
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/agriculture13020510
In the twenty-first century, machine learning is a significant part of daily life for everyone. Today, it is adopted in many different applications, such as object recognition, object classification, and medical purposes. This research aimed to use deep convolutional neural networks for the real-time detection of diseases in plant leaves. Typically, farmers are unaware of diseases on plant leaves and adopt manual disease detection methods. Their production often decreases as the virus spreads. However, due to a lack of essential infrastructure, quick identification needs to be improved in many regions of the world. It is now feasible to diagnose diseases using mobile devices as a result of the increase in mobile phone usage globally and recent advancements in computer vision due to deep learning. To conduct this research, firstly, a dataset was created that contained images of money plant leaves that had been split into two primary categories, specifically (i) healthy and (ii) unhealthy. This research collected thousands of images in a controlled environment and used a public dataset with exact dimensions. The next step was to train a deep model to identify healthy and unhealthy leaves. Our trained YOLOv5 model was applied to determine the spots on the exclusive and public datasets. This research quickly and accurately identified even a small patch of disease with the help of YOLOv5. It captured the entire image in one shot and forecasted adjacent boxes and class certainty. A random dataset image served as the model’s input via a cell phone. This research is beneficial for farmers since it allows them to recognize diseased leaves as soon as they noted and take the necessary precautions to halt the disease’s spread. This research aimed to provide the best hyper-parameters for classifying and detecting the healthy and unhealthy parts of leaves in exclusive and public datasets. Our trained YOLOv5 model achieves 93 % accuracy on a test set.
Related Topics
Concepts
Convolutional neural network
Computer science
Artificial intelligence
Deep learning
Mobile phone
Identification (biology)
Machine learning
Phone
Plant disease
Object (grammar)
Random forest
Object detection
Class (philosophy)
Pattern recognition (psychology)
Telecommunications
Linguistics
Biology
Philosophy
Botany
Biotechnology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/agriculture13020510
- https://www.mdpi.com/2077-0472/13/2/510/pdf?version=1676964493
- OA Status
- gold
- Cited By
- 74
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4321457483
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4321457483Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/agriculture13020510Digital Object Identifier
- Title
-
Real-Time Plant Health Detection Using Deep Convolutional Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-20Full publication date if available
- Authors
-
Mahnoor Khalid, Muhammad Shahzad Sarfraz, Uzair Iqbal, Muhammad Umar Aftab, Gniewko Niedbała, Hafiz Tayyab RaufList of authors in order
- Landing page
-
https://doi.org/10.3390/agriculture13020510Publisher landing page
- PDF URL
-
https://www.mdpi.com/2077-0472/13/2/510/pdf?version=1676964493Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2077-0472/13/2/510/pdf?version=1676964493Direct OA link when available
- Concepts
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Convolutional neural network, Computer science, Artificial intelligence, Deep learning, Mobile phone, Identification (biology), Machine learning, Phone, Plant disease, Object (grammar), Random forest, Object detection, Class (philosophy), Pattern recognition (psychology), Telecommunications, Linguistics, Biology, Philosophy, Botany, BiotechnologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
74Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 24, 2024: 37, 2023: 13Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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