Deep Learning-Assisted SVMs for Efficacious Diagnosis of Tomato Leaf Diseases: A Comparative Study of GoogleNet, AlexNet, and ResNet-50
Bibliographic record
Abstract
Plant diseases contribute to substantial yield and quality deficits in agricultural production, thus necessitating rapid and precise identification techniques.Conventional plant protection efforts, reliant on the ocular inspection of diseases and pests affecting tomato plants, suffer from protracted durations and varying degrees of accuracy.As the demand for precision agriculture escalates, the development of efficient, rapid, and more importantly, computeraided disease recognition systems have emerged as a crucial requirement.In this study, feature extraction was performed utilizing three prominent pre-trained convolutional neural network (CNN) models, namely GoogleNet, AlexNet, and ResNet-50.A novel deep learning model, which amalgamates features derived from these distinct CNN architectures, was subsequently introduced.Training of a Support Vector Machine (SVM) classifier was accomplished using these deep features.The proposed model was employed for classifying images of tomato plant diseases, part of the publicly accessible PlantVillage dataset from Kaggle, comprising 18,835 labeled images of tomato plant leaves.The hold-out validation strategy was implemented for model evaluation, using metrics such as accuracy, precision, sensitivity, and F-Scores.The experimental results affirm the efficacy of combined deep features in detecting diseases in tomato plants, with a remarkable accuracy of 96.99%.These findings underscore the potential of our approach in transforming the landscape of precision agriculture by offering a more accurate and efficient means of disease detection.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".