| --- |
| 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. |
|
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| 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. |
|
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| 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. |
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| For deployment, document the model version, training date, dataset version, evaluation results, and any changes to preprocessing or class mappings. |
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|
