Enhanced Hybrid Neural Networks (CoAtNet) for Paddy Crops Disease Detection and Classification
Bibliographic record
Abstract
In the Asian continent rice cultivation process provide staple food for livelihood. A current research work in the agriculture area involves recognizing and classifying plants diseases based on live images. Farmer can traditionally do the cultivation process, hence here the identification of the disease was by manual (visual appearance) or send the sample data set to the nearest laboratory. In our proposed method we will provide accurate and early detection of various diseases in Oryza sativa (rice) plants, that can help the farmers in applying suitable treatment on the rice plants and improve productivity. We are using optimized deep learning models such as the ResNet-152, CoAtNet for classification and identify the diseases. We have captured healthy and unhealthy images from Villupuram district, Tamil Nadu, India. The total amount of captured images was 3071 from our farmer's field with proper sunlight. It was highly efficient and detects the diseases or recognizes the diseases from the captured image with different categories (Bacterial Leaf Blight, Leaf Blast, Brown Spot, and Tungro / Leaf smut). The experimental results show according to the proposed method CoAtNet, was achieved for overall achieved accuracy of 96.56%.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".