Real-Time Plant Health Detection Using Deep Convolutional Neural Networks
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-tim...
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MDPI AG
2023-02-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/2/510 |
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| author | Mahnoor Khalid Muhammad Shahzad Sarfraz Uzair Iqbal Muhammad Umar Aftab Gniewko Niedbała Hafiz Tayyab Rauf |
| author_facet | Mahnoor Khalid Muhammad Shahzad Sarfraz Uzair Iqbal Muhammad Umar Aftab Gniewko Niedbała Hafiz Tayyab Rauf |
| author_sort | Mahnoor Khalid |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-64d51dd86bcd4f2891f448656b770827 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-64d51dd86bcd4f2891f448656b7708272025-01-23T14:03:29ZengMDPI AGAgriculture2077-04722023-02-0113251010.3390/agriculture13020510Real-Time Plant Health Detection Using Deep Convolutional Neural NetworksMahnoor Khalid0Muhammad Shahzad Sarfraz1Uzair Iqbal2Muhammad Umar Aftab3Gniewko Niedbała4Hafiz Tayyab Rauf5Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, PakistanDepartment of Artificial Intelligence and Data Science, National University of Computer and Emerging Sciences (NUCES), Islamabad 35400, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, PakistanDepartment of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, PolandIndependent Researcher, Bradford BD8 0HS, UKIn 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.https://www.mdpi.com/2077-0472/13/2/510plant health detectionprecision agriculturedeep learningobject detectionYOLOv5 |
| spellingShingle | Mahnoor Khalid Muhammad Shahzad Sarfraz Uzair Iqbal Muhammad Umar Aftab Gniewko Niedbała Hafiz Tayyab Rauf Real-Time Plant Health Detection Using Deep Convolutional Neural Networks Agriculture plant health detection precision agriculture deep learning object detection YOLOv5 |
| title | Real-Time Plant Health Detection Using Deep Convolutional Neural Networks |
| title_full | Real-Time Plant Health Detection Using Deep Convolutional Neural Networks |
| title_fullStr | Real-Time Plant Health Detection Using Deep Convolutional Neural Networks |
| title_full_unstemmed | Real-Time Plant Health Detection Using Deep Convolutional Neural Networks |
| title_short | Real-Time Plant Health Detection Using Deep Convolutional Neural Networks |
| title_sort | real time plant health detection using deep convolutional neural networks |
| topic | plant health detection precision agriculture deep learning object detection YOLOv5 |
| url | https://www.mdpi.com/2077-0472/13/2/510 |
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