Tea leaf disease detection using segment anything model and deep convolutional neural networks
Tea is an important beverage across many cultures. Diseases affecting tea leaves can adversely impact the integrity, production and cause substantial economic losses. Hence, detecting these diseases efficiently and accurately at an early stage is extremely crucial. The dataset used in this work cons...
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024020279 |
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| author | Ananthakrishnan Balasundaram Prem Sundaresan Aryan Bhavsar Mishti Mattu Muthu Subash Kavitha Ayesha Shaik |
| author_facet | Ananthakrishnan Balasundaram Prem Sundaresan Aryan Bhavsar Mishti Mattu Muthu Subash Kavitha Ayesha Shaik |
| author_sort | Ananthakrishnan Balasundaram |
| collection | DOAJ |
| description | Tea is an important beverage across many cultures. Diseases affecting tea leaves can adversely impact the integrity, production and cause substantial economic losses. Hence, detecting these diseases efficiently and accurately at an early stage is extremely crucial. The dataset used in this work consists of 6 categories to be trained, namely: Algal Spot, Brown Blight, Gray Blight, Healthy, Helopeltis and Red Spot. In our proposed method, a convolutional neural network is used in conjunction with advanced image preprocessing techniques for detecting and segmenting the infected tea leaf region. OpenCV was employed to extract the Region of Interest (ROI) and image cropping was performed to focus only on the leaf. In the process of cropping, the leaf was identified in the image, a bounding box was drawn around it and then it was finally cropped to maximize the leaf in the image. Further, the Segment Anything Model's (SAM) zero-shot segmentation capabilities were tested to segment and extract the diseased regions of the leaf. Also, the images were fed into a custom Convolutional Neural Network (CNN) model to extract the relevant features. These features were subsequently assigned to various classifiers like MLP, SVM, and Decision Tree classifiers to classify the diseases. The performance of each model was analyzed and compared. An accuracy of 95.06 % was achieved demonstrating that the proposed model has relatively higher accuracy in identifying the tea leaf diseases than many of the existing models. |
| format | Article |
| id | doaj-art-296b64c61ced4e0181d3d5f9191c67b4 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-296b64c61ced4e0181d3d5f9191c67b42024-12-22T05:29:40ZengElsevierResults in Engineering2590-12302025-03-0125103784Tea leaf disease detection using segment anything model and deep convolutional neural networksAnanthakrishnan Balasundaram0Prem Sundaresan1Aryan Bhavsar2Mishti Mattu3Muthu Subash Kavitha4Ayesha Shaik5Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, 600127, India; Corresponding author.School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, IndiaSchool of Information and Data Science, Nagasaki University, Nagasaki, 8528521, JapanCentre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, 600127, IndiaTea is an important beverage across many cultures. Diseases affecting tea leaves can adversely impact the integrity, production and cause substantial economic losses. Hence, detecting these diseases efficiently and accurately at an early stage is extremely crucial. The dataset used in this work consists of 6 categories to be trained, namely: Algal Spot, Brown Blight, Gray Blight, Healthy, Helopeltis and Red Spot. In our proposed method, a convolutional neural network is used in conjunction with advanced image preprocessing techniques for detecting and segmenting the infected tea leaf region. OpenCV was employed to extract the Region of Interest (ROI) and image cropping was performed to focus only on the leaf. In the process of cropping, the leaf was identified in the image, a bounding box was drawn around it and then it was finally cropped to maximize the leaf in the image. Further, the Segment Anything Model's (SAM) zero-shot segmentation capabilities were tested to segment and extract the diseased regions of the leaf. Also, the images were fed into a custom Convolutional Neural Network (CNN) model to extract the relevant features. These features were subsequently assigned to various classifiers like MLP, SVM, and Decision Tree classifiers to classify the diseases. The performance of each model was analyzed and compared. An accuracy of 95.06 % was achieved demonstrating that the proposed model has relatively higher accuracy in identifying the tea leaf diseases than many of the existing models.http://www.sciencedirect.com/science/article/pii/S2590123024020279Tea leaf diseaseZero-shot segmentationSegment anything modelCNNMLP |
| spellingShingle | Ananthakrishnan Balasundaram Prem Sundaresan Aryan Bhavsar Mishti Mattu Muthu Subash Kavitha Ayesha Shaik Tea leaf disease detection using segment anything model and deep convolutional neural networks Results in Engineering Tea leaf disease Zero-shot segmentation Segment anything model CNN MLP |
| title | Tea leaf disease detection using segment anything model and deep convolutional neural networks |
| title_full | Tea leaf disease detection using segment anything model and deep convolutional neural networks |
| title_fullStr | Tea leaf disease detection using segment anything model and deep convolutional neural networks |
| title_full_unstemmed | Tea leaf disease detection using segment anything model and deep convolutional neural networks |
| title_short | Tea leaf disease detection using segment anything model and deep convolutional neural networks |
| title_sort | tea leaf disease detection using segment anything model and deep convolutional neural networks |
| topic | Tea leaf disease Zero-shot segmentation Segment anything model CNN MLP |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024020279 |
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