TeaNet8: A real time Android application-based Tea Leaf Disease detection using fine-tuned transfer learning and Gradient-Weighted Class Activation Mapping visualization
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Bibliographic record
Abstract
Tea is one of the most popular drinks in the world, and Bangladesh is a producer and user of it. However, diseases that impact the quality and productivity of crops can greatly impede the production of tea, impacting the final product’s quantity and quality. To prevent and control tea leaf diseases, a reliable and precise diagnosis and identification system is needed. Tea leaf infections are discovered manually, which takes time and affects crop quality and production. Detecting tea leaf disease early can lead to decreased damage to overall tea production. Advanced deep learning methods are simplifying the identification and categorization of specific illnesses in tea plants. The aim of this study is to introduce a new approach for identifying and categorizing illnesses found in tea plants by employing advanced deep learning methods. This study employs 2824 images of eight different types of leaf diseases. Preprocessing techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE), brightness adjustment, and unsharp masking were applied to enhance the dataset. Additionally, data augmentation techniques were used to increase its diversity. The proposed model shows that it can identify the type of persistent tea leaf disease with 97% accuracy.Gradient-Weighted Class Activation Mapping (Grad-CAM) visualization was employed to interpret and understand model predictions. The model demonstrated perfect accuracy for Algal Spot, Anthracnose, Gray Blight, and White Spot, with accuracy rates of 97.14% for Brown Blight, 94.59% for Healthy leaves, 94.12% for Red Spot, and 92.31% for Bird Eye Spot. Furthermore, the proposed model’s performance was compared against three pre-trained fine-tuning models. Various performance measurement indicators were used to evaluate the performance of the models utilized in the research. The results showed that the proposed model is effective in categorizing diseases in tea leaves.Finally, An Android-based system was developed employing the most effective model to aid farmers for detecting tea leaf diseases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it