Deep Learning-Based Classification of Tomato Leaf Diseases for Precision Agriculture
Bibliographic record
Abstract
Early detection of plant diseases is essential for improving crop yield and ensuring sustainable agriculture. Studies have shown that traditional disease diagnosis based on visual inspection is often time-consuming and prone to errors, especially under field conditions. This study applies a convolutional neural network (CNN) to classify tomato leaf diseases using a dataset of 8000 images across 10 categories, including diseases like Tomato Mosaic Virus, Bacterial Spot, and Late Blight, along with healthy leaves. The model consists of three convolutional layers with max-pooling, followed by a dense layer and a softmax classifier. Using data augmentation and rescaling techniques, the model achieved 95.74% training accuracy and 89.50% validation accuracy. These results demonstrate the effectiveness of deep learning in distinguishing visually similar plant diseases. This research highlights the potential of CNN-based disease classification as a valuable tool for precision agriculture, supporting farmers with timely and accurate diagnosis. Future work will explore model optimization for mobile deployment, enabling real-time disease detection in the field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".