Release KrishokChat backend ML suite: YOLO26 vision models and 5-fold soil moisture regression
12631d6 verified | license: apache-2.0 | |
| tags: | |
| - agriculture | |
| - plant-pathology | |
| - crop-disease | |
| - computer-vision | |
| - yolo26 | |
| - efficientnet | |
| - soil-moisture | |
| - bangladesh | |
| - multimodal | |
| datasets: | |
| - agricultural-vision | |
| metrics: | |
| - accuracy | |
| - rmse | |
| library_name: ultralytics | |
| pipeline_tag: image-classification | |
| # KrishokChat: Agricultural Vision & Soil Intelligence Model Suite | |
| This repository contains the complete, production-ready suite of machine learning models developed for the **KrishokChat Advisory System**. These models are packaged into self-contained, folderized directories to enable academic reviewers, agronomists, and developers to reproduce and run our full diagnostic pipeline locally without requiring expensive cloud GPU servers. | |
| --- | |
| ## 1. Architectural Overview | |
| The KrishokChat perception and diagnostic stack uses a multi-tier decoupled architecture: | |
| 1. **Tier 1: Crop Species Identification** — A `YOLO26-cls` model identifying 10 major crop families in Bangladesh to eliminate cross-crop pesticide misbinding hazard. | |
| 2. **Tier 2: Specialized Crop Pathology Diagnosis** — Six per-crop dedicated `YOLO26-cls` models diagnosing foliar fungal, bacterial, viral, and physiological diseases. | |
| 3. **Tier 3: Soil Moisture Matric Tension Estimation** — A 5-fold cross-validated `EfficientNet-B0` regression model predicting continuous soil suction pressure ($\text{kPa}$) directly from field images for precision irrigation guidance. | |
| ``` | |
| [ Farmer Field Leaf Image ] | |
| │ | |
| ▼ | |
| [ 01_crop_classifier ] (YOLO26-cls) | |
| ┌─────────────────┼─────────────────┐ | |
| │ │ │ | |
| ▼ ▼ ▼ | |
| [ 02_potato_disease ] [ 03_rice_disease ] [ 04_wheat_disease ] ... | |
| │ │ │ | |
| └─────────────────┼─────────────────┘ | |
| ▼ | |
| [ Diagnosis + Bangla Treatment Advice ] | |
| ``` | |
| --- | |
| ## 2. Model Catalog & Checkpoint Manifest | |
| Every PyTorch weight (`.pt`) is named distinctively with zero ambiguity. Corresponding optimized `ONNX` weights are included for cross-platform edge deployment. | |
| | # | Directory | PyTorch Weight (`.pt`) | Edge ONNX (`.onnx`) | Architecture | Classes / Output | Target Crop / Task | | |
| |:---:|:---|:---|:---|:---:|:---:|:---| | |
| | **1** | `01_crop_classifier` | `crop_classifier.pt` (10.5 MB) | `crop_classifier.onnx` (20.8 MB) | YOLO26-cls | 10 classes | Multi-crop species classification | | |
| | **2** | `02_potato_disease` | `potato_disease.pt` (10.5 MB) | `potato_disease.onnx` (20.8 MB) | YOLO26-cls | 3 classes | Potato (*Solanum tuberosum*) | | |
| | **3** | `03_rice_disease` | `rice_disease.pt` (19.9 MB) | `rice_disease.onnx` (39.6 MB) | YOLO26-cls | 10 classes | Rice (*Oryza sativa*) | | |
| | **4** | `04_wheat_disease` | `wheat_disease.pt` (9.0 MB) | `wheat_disease.onnx` (5.9 MB) | YOLO26-cls | 11 classes | Wheat (*Triticum aestivum*) | | |
| | **5** | `05_corn_disease` | `corn_disease.pt` (10.5 MB) | `corn_disease.onnx` (20.8 MB) | YOLO26-cls | 4 classes | Corn / Maize (*Zea mays*) | | |
| | **6** | `06_chilli_disease` | `chilli_disease.pt` (3.1 MB) | `chilli_disease.onnx` (5.9 MB) | YOLO26-cls | 8 classes | Chilli (*Capsicum annuum*) | | |
| | **7** | `07_brassica_disease` | `brassica_disease.pt` (10.5 MB) | `brassica_disease.onnx` (20.8 MB) | YOLO26-cls | 11 classes | Cabbage & Cauliflower (*Brassica spp.*) | | |
| | **8** | `08_soil_moisture` | `effnetb0_fold[0-4].pt` (81 MB) | N/A (PyTorch state_dict) | EfficientNet-B0 | 1 continuous (kPa) | Soil matric tension estimation | | |
| --- | |
| ## 3. Directory Layout | |
| ```text | |
| backend_models/ | |
| ├── README.md # Main Model Card & Reviewer Documentation | |
| ├── requirements.txt # Minimal pip dependencies | |
| ├── verify_all_models.py # Single-click automated diagnostic verification suite | |
| ├── run_inference.py # Multi-model CLI inference runner | |
| ├── upload_to_huggingface.py # Hugging Face Hub upload automation script | |
| │ | |
| ├── 01_crop_classifier/ # Intake Crop Classifier (10 classes) | |
| │ ├── crop_classifier.pt | |
| │ ├── crop_classifier.onnx | |
| │ ├── class_names.json | |
| │ └── README.md | |
| │ | |
| ├── 02_potato_disease/ # Potato Pathology (Early Blight, Late Blight, Healthy) | |
| │ ├── potato_disease.pt | |
| │ ├── potato_disease.onnx | |
| │ ├── class_names.json | |
| │ ├── disease_details.json | |
| │ └── README.md | |
| │ | |
| ├── 03_rice_disease/ # Rice Pathology (Blast, BLB, Sheath Blight, etc.) | |
| │ ├── rice_disease.pt | |
| │ ├── rice_disease.onnx | |
| │ ├── class_names.json | |
| │ ├── disease_details.json | |
| │ └── README.md | |
| │ | |
| ├── 04_wheat_disease/ # Wheat Pathology (Rusts, Blast, Tan Spot, etc.) | |
| │ ├── wheat_disease.pt | |
| │ ├── wheat_disease.onnx | |
| │ ├── class_names.json | |
| │ ├── disease_details.json | |
| │ ├── metadata.json | |
| │ └── README.md | |
| │ | |
| ├── 05_corn_disease/ # Corn Pathology (Rust, Leaf Spot, Blight) | |
| │ ├── corn_disease.pt | |
| │ ├── corn_disease.onnx | |
| │ ├── class_names.json | |
| │ ├── disease_details.json | |
| │ └── README.md | |
| │ | |
| ├── 06_chilli_disease/ # Chilli Pathology (Bacterial Spot, Curl Virus, etc.) | |
| │ ├── chilli_disease.pt | |
| │ ├── chilli_disease.onnx | |
| │ ├── class_names.json | |
| │ ├── disease_details.json | |
| │ └── README.md | |
| │ | |
| ├── 07_brassica_disease/ # Crucifers (Cabbage / Cauliflower diseases) | |
| │ ├── brassica_disease.pt | |
| │ ├── brassica_disease.onnx | |
| │ ├── class_names.json | |
| │ ├── disease_details.json | |
| │ └── README.md | |
| │ | |
| ├── 08_soil_moisture/ # Soil Moisture Tensiometer Regression (5-fold ensemble) | |
| │ ├── effnetb0_fold0.pt ... fold4.pt | |
| │ ├── oof_predictions.csv | |
| │ ├── pred_vs_actual.png | |
| │ ├── model_def.py | |
| │ └── README.md | |
| │ | |
| └── sample_images/ # Representative leaf images for zero-setup verification | |
| ├── crop_sample.jpg | |
| ├── potato_sample.jpg | |
| ├── rice_sample.jpg | |
| ├── wheat_sample.jpg | |
| ├── corn_sample.jpg | |
| ├── chilli_sample.jpg | |
| └── brassica_sample.jpg | |
| ``` | |
| --- | |
| ## 4. Quickstart Guide for Academic Reviewers | |
| ### Step 1: Install Dependencies | |
| ```bash | |
| cd backend_models | |
| pip install -r requirements.txt | |
| ``` | |
| ### Step 2: Automated Verification of All Models | |
| Run the automated test runner to verify that every single model checkpoint loads into memory, performs forward inference, and verifies against class specifications: | |
| ```bash | |
| python verify_all_models.py | |
| ``` | |
| ### Step 3: Run Standalone Inferences | |
| **Test Potato Disease Diagnosis:** | |
| ```bash | |
| python run_inference.py --crop potato --image sample_images/potato_sample.jpg | |
| ``` | |
| **Test Rice Disease Diagnosis:** | |
| ```bash | |
| python run_inference.py --crop rice --image sample_images/rice_sample.jpg | |
| ``` | |
| **Test Wheat Disease Diagnosis:** | |
| ```bash | |
| python run_inference.py --crop wheat --image sample_images/wheat_sample.jpg | |
| ``` | |
| **Test Soil Moisture Regression:** | |
| ```bash | |
| python run_inference.py --task soil --image sample_images/crop_sample.jpg | |
| ``` | |
| --- | |
| ## 5. Python API Usage | |
| ### Vision Models (YOLO26) | |
| ```python | |
| from ultralytics import YOLO | |
| # Load any specialized YOLO26 pathology classifier | |
| model = YOLO("02_potato_disease/potato_disease.pt") | |
| # Predict on an RGB image | |
| results = model.predict("sample_images/potato_sample.jpg", imgsz=224, verbose=False)[0] | |
| top_class = model.names[results.probs.top1] | |
| confidence = float(results.probs.top1conf) | |
| print(f"Result: {top_class} ({confidence:.2%})") | |
| ``` | |
| ### Soil Moisture Regression (5-Fold Ensemble) | |
| ```python | |
| import sys | |
| sys.path.insert(0, "08_soil_moisture") | |
| from model_def import load_soil_model | |
| import torch | |
| import torchvision.transforms as T | |
| from PIL import Image | |
| transform = T.Compose([ | |
| T.Resize((224, 224)), | |
| T.ToTensor(), | |
| T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| ]) | |
| img = transform(Image.open("sample_images/crop_sample.jpg").convert("RGB")).unsqueeze(0) | |
| # Ensemble average across 5 folds | |
| predictions = [] | |
| for fold in range(5): | |
| m = load_soil_model(f"08_soil_moisture/effnetb0_fold{fold}.pt") | |
| with torch.no_grad(): | |
| predictions.append(m(img).item()) | |
| mean_kpa = sum(predictions) / len(predictions) | |
| print(f"Estimated Soil Matric Potential: {mean_kpa:.2f} kPa") | |
| ``` | |
| --- | |
| ## 6. Uploading to Hugging Face Hub | |
| To publish this suite to the official Hugging Face repository: | |
| ```bash | |
| python upload_to_huggingface.py --token <YOUR_HF_TOKEN> | |
| ``` | |
| Target repository: `https://huggingface.co/RaiyanKhaan/KrishokTech-Models` | |
| --- | |
| ## 7. Citation | |
| If you use these model weights or the KrishokChat pipeline in your research, please cite our paper: | |
| ```bibtex | |
| @inproceedings{krishokchat2026, | |
| title={KrishokChat: A Grounded Multi-Modal Agricultural Advisory and Pathology Diagnostic System for Smallholder Farming}, | |
| author={Reza, Raiyaan and Khaan, Raiyan and Collaborators}, | |
| booktitle={Proceedings of the European Chapter of the Association for Computational Linguistics (EACL)}, | |
| year={2026} | |
| } | |
| ``` | |