Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification
Agriculture is vital for providing food and economic benefits, but crop diseases pose significant challenges, including coffee cultivation. Traditional methods for disease identification are labor-intensive and lack real-time capabilities. This study aims to address existing methods’ limitations and...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/7/1/13 |
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| author | Opeyemi Adelaja Bernardi Pranggono |
| author_facet | Opeyemi Adelaja Bernardi Pranggono |
| author_sort | Opeyemi Adelaja |
| collection | DOAJ |
| description | Agriculture is vital for providing food and economic benefits, but crop diseases pose significant challenges, including coffee cultivation. Traditional methods for disease identification are labor-intensive and lack real-time capabilities. This study aims to address existing methods’ limitations and provide a more efficient, reliable, and cost-effective solution for coffee leaf disease identification. It presents a novel approach to the real-time identification of coffee leaf diseases using deep learning. We implemented several transfer learning (TL) models, including ResNet101, Xception, CoffNet, and VGG16, to evaluate the feasibility and reliability of our solution. The experiment results show that the proposed models achieved high accuracy rates of 97.30%, 97.60%, 97.88%, and 99.89%, respectively. CoffNet, our proposed model, showed a notable processing speed of 125.93 frames per second (fps), making it suitable for real-time applications. Using a diverse dataset of mixed images from multiple devices, our approach reduces the workload of farmers and simplifies the disease detection process. The findings lay the groundwork for the development of practical and efficient systems that can assist coffee growers in disease management, promoting sustainable farming practices, and food security. |
| format | Article |
| id | doaj-art-854c8113c3de43ee9fcbdb053958eac3 |
| institution | Kabale University |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-854c8113c3de43ee9fcbdb053958eac32025-01-24T13:16:14ZengMDPI AGAgriEngineering2624-74022025-01-01711310.3390/agriengineering7010013Leveraging Deep Learning for Real-Time Coffee Leaf Disease IdentificationOpeyemi Adelaja0Bernardi Pranggono1School of Computing and Information Science, Anglia Ruskin University, Cambridge CB1 1PT, UKSchool of Computing and Information Science, Anglia Ruskin University, Cambridge CB1 1PT, UKAgriculture is vital for providing food and economic benefits, but crop diseases pose significant challenges, including coffee cultivation. Traditional methods for disease identification are labor-intensive and lack real-time capabilities. This study aims to address existing methods’ limitations and provide a more efficient, reliable, and cost-effective solution for coffee leaf disease identification. It presents a novel approach to the real-time identification of coffee leaf diseases using deep learning. We implemented several transfer learning (TL) models, including ResNet101, Xception, CoffNet, and VGG16, to evaluate the feasibility and reliability of our solution. The experiment results show that the proposed models achieved high accuracy rates of 97.30%, 97.60%, 97.88%, and 99.89%, respectively. CoffNet, our proposed model, showed a notable processing speed of 125.93 frames per second (fps), making it suitable for real-time applications. Using a diverse dataset of mixed images from multiple devices, our approach reduces the workload of farmers and simplifies the disease detection process. The findings lay the groundwork for the development of practical and efficient systems that can assist coffee growers in disease management, promoting sustainable farming practices, and food security.https://www.mdpi.com/2624-7402/7/1/13convolutional neural networkdeep learningdisease identificationtransfer learning |
| spellingShingle | Opeyemi Adelaja Bernardi Pranggono Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification AgriEngineering convolutional neural network deep learning disease identification transfer learning |
| title | Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification |
| title_full | Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification |
| title_fullStr | Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification |
| title_full_unstemmed | Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification |
| title_short | Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification |
| title_sort | leveraging deep learning for real time coffee leaf disease identification |
| topic | convolutional neural network deep learning disease identification transfer learning |
| url | https://www.mdpi.com/2624-7402/7/1/13 |
| work_keys_str_mv | AT opeyemiadelaja leveragingdeeplearningforrealtimecoffeeleafdiseaseidentification AT bernardipranggono leveragingdeeplearningforrealtimecoffeeleafdiseaseidentification |