AventIQ-AI/Crop_Recommendation

DistilBERT Model for Crop Recommendation Based on Environmental Parameters

This repository contains a fine-tuned DistilBERT model trained for crop recommendation using structured agricultural data. By converting numerical environmental features into text format, the model leverages transformer-based NLP techniques to classify the most suitable crop type.

🌾 Problem Statement

The goal is to recommend the best crop to cultivate based on parameters such as soil nutrients and weather conditions. Traditional ML models handle this as a tabular classification problem. Here, we explore the innovative approach of using NLP models (DistilBERT) on serialized tabular data.

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📊 Dataset

• Source: Crop Recommendation Dataset

• Features:

  • N: Nitrogen content in soil
  • P: Phosphorus content in soil
  • K: Potassium content in soil
  • Temperature: in Celsius
  • Humidity: %
  • pH: Acidity of soil
  • Rainfall: mm

• Target: Crop label (22 crop types)

The dataset is preprocessed by concatenating all numeric features into a single space-separated string, making it suitable for transformer-based tokenization.

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🧠 Model Details

• Architecture: DistilBERT
• Tokenizer: DistilBertTokenizerFast
• Model: DistilBertForSequenceClassification
• Task Type: Multi-Class Classification (22 classes)

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🔧 Installation

pip install transformers datasets pandas scikit-learn torch

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Loading the Model

from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
import torch

# Load model and tokenizer
model_path = "model_fp32_dir"
tokenizer = DistilBertTokenizerFast.from_pretrained(model_path)
model = DistilBertForSequenceClassification.from_pretrained(model_path)

# Sample input
sample_text = "90 42 43 20.879744 82.002744 6.502985 202.935536"
inputs = tokenizer(sample_text, return_tensors="pt")

# Predict
with torch.no_grad():
    outputs = model(**inputs)
predicted_class = torch.argmax(outputs.logits, dim=1).item()
print("Predicted class index:", predicted_class)

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📈 Performance Metrics

• Accuracy: 0.7636
• Precision: 0.7738
• Recall: 0.7636
• F1 Score: 0.7343

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🏋️ Fine-Tuning Details

📚 Dataset

The dataset is sourced from the publicly available Crop Recommendation Dataset. It consists of structured features such as:

• Nitrogen (N)
• Phosphorus (P)
• Potassium (K)
• Temperature (°C)
• Humidity (%)
• pH
• Rainfall (mm)

All numerical features were converted into a single textual input string to be used with the DistilBERT tokenizer. Labels were factorized into class indices for training.

The dataset was split using an 80/20 ratio for training and testing.

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🔧 Training Configuration

• Epochs: 3
• Batch size: 8
• Learning rate: 2e-5
• Evaluation strategy: epoch
• Model Base: DistilBERT (distilbert-base-uncased)
• Framework: Hugging Face Transformers + PyTorch

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🔄 Quantization

Post-training quantization was applied using PyTorch’s half() precision (FP16).
This reduces the model size and speeds up inference with minimal impact on performance.

The quantized model can be loaded with:

model = DistilBertForSequenceClassification.from_pretrained("quantized_model_fp16", torch_dtype=torch.float16)

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Repository Structure

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├── quantized-model/               # Contains the quantized model files
│   ├── config.json
│   ├── model.safetensors
│   ├── tokenizer_config.json
│   ├── vocab.txt
│   └── special_tokens_map.json
├── README.md                      # Model documentation

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Limitations

• Uses text conversion of tabular data, which may miss deeper feature interactions.
• Trained on a specific dataset; may not generalize to different regions or conditions.
• FP16 quantization may slightly reduce accuracy in rare cases.

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Contributing

Feel free to open issues or submit pull requests to improve the model or documentation.