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AgriScan_EfficientNetB0_Two_Classifier_Training

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----
-language:
-  - en
-license: other
-library_name: keras
-pipeline_tag: image-classification
-tags:
-  - agriscan
-  - agriculture
-  - plant-disease
-  - crop-classification
-  - image-classification
-  - efficientnet
-  - efficientnetb0
-  - tensorflow
-  - keras
-  - plantvillage
-datasets:
-  - mohanty/PlantVillage
----
-# AgriScan EfficientNetB0 — Crop and Plant Disease Classifiers
-AgriScan is an agricultural intelligence project developing computer-vision tools to support crop identification and plant-health screening.
-This repository is intended to contain two separately trained EfficientNetB0 models:
-1. **Crop classifier** — predicts whether an image belongs to maize, potato, or tomato.
-2. **Disease classifier** — predicts one of 17 crop-condition classes across those crops.
-> **Status:** Research and pilot prototype. Add measured evaluation results after the training notebook has completed successfully.
-## Repository contents
-Expected files:
-```text
-.
-├── README.md
-├── agriscan_efficientnet_crop.keras
-├── agriscan_efficientnet_disease.keras
-├── crop_names.json
-├── class_names.json
-├── evaluation_metrics.json
-├── crop_classification_report.csv
-├── disease_classification_report.csv
-├── crop_confusion_matrix.csv
-└── disease_confusion_matrix.csv
-```
-Upload only the files that have actually been generated. Evaluation reports and metrics should be added after the training and evaluation cells finish.
-## Model details
-| Property | Crop classifier | Disease classifier |
-|---|---|---|
-| Architecture | EfficientNetB0 | EfficientNetB0 |
-| Task | Crop classification | Crop-condition classification |
-| Number of output classes | 3 | 17 |
-| Input | RGB image | RGB image |
-| Input size | 224 × 224 pixels | 224 × 224 pixels |
-| Model format | Keras `.keras` | Keras `.keras` |
-The training workflow uses transfer learning with an ImageNet-pretrained EfficientNetB0 backbone, first training the classification head and then fine-tuning upper backbone layers.
-### Crop classes
-- Maize
-- Potato
-- Tomato
-### Disease classes
-The 17 output labels are defined in `class_names.json`. **The JSON order is significant** because it maps each output index to its label. Use the exact file produced by the training notebook; do not manually reorder the labels.
-## Dataset and evaluation
-The training workflow uses the `mohanty/PlantVillage` dataset and its `splits/color_train.txt` and `splits/color_test.txt` manifests, filtered to maize, potato, and tomato classes.
-The training partition is divided into training and validation subsets. The source test partition is reserved for final evaluation.
-No performance metrics are stated here because the EfficientNetB0 training run must complete before results can be reported. After evaluation, update this section using the generated `evaluation_metrics.json` and classification reports. Report the test-set size and metrics exactly as measured; do not use training or validation scores as test scores.
-## Input preprocessing
-- Convert the image to RGB.
-- Resize to 224 × 224 pixels.
-- Supply pixel values in the range expected by the saved model.
-- The Keras EfficientNetB0 backbone includes input rescaling. Do not apply MobileNetV2-specific `Rescaling(1/127.5, offset=-1)` preprocessing unless the model architecture is deliberately changed and retrained.
-Use the model's actual training/inference pipeline when deploying it.
-## Recommended inference flow
-1. Run the crop classifier.
-2. Run the disease classifier.
-3. Read the predicted labels using `crop_names.json` and `class_names.json`.
-4. Compare the crop predicted by the crop classifier with the crop implied by the disease-class label.
-5. If the two classifiers disagree, or confidence is low, return an uncertain/review result instead of presenting a definitive diagnosis.
-The two classifiers are independent models. A downstream application must implement the routing, confidence policy, and disagreement handling.
-## Intended use
-This model is intended for research, prototyping, and controlled pilot evaluation of crop classification and plant-disease image classification for maize, potato, and tomato.
-## Limitations and responsible use
-- This is a closed-set classifier. It is not a general plant detector and may assign an unsupported label to non-plant images.
-- It predicts only among the classes represented in the training labels; unknown diseases and other crops are not supported.
-- PlantVillage images may differ from farmer field photographs in lighting, backgrounds, camera quality, geography, crop varieties, and disease severity.
-- A high softmax score is not a guarantee of correctness and should not be treated as calibrated probability without further calibration work.
-- Predictions are not a substitute for expert agricultural advice or laboratory diagnosis.
-- Validate on representative real-world field images before operational deployment.
-## Citation and attribution
-The dataset used by the training workflow is available as [`mohanty/PlantVillage`](https://huggingface.co/datasets/mohanty/PlantVillage). Review the dataset card and its terms before redistributing derived assets.
-## Contact and project
-Project: **AgriScan**, an agricultural intelligence initiative associated with CDSA.
-For deployment, document the model version, training date, dataset version, evaluation results, and any changes to preprocessing or class mappings.