Creating a crop recommendation system using machine learning can greatly help farmers in making informed decisions about what crops to plant based on various factors. Here’s a high-level overview of how you can design and implement such a system:

1. Collect Data: Gather data related to soil quality, weather conditions, geographical location, crop types, historical yield, etc.
2. Preprocess Data: Clean and preprocess the data by handling missing values, normalizing variables, and encoding categorical features.
3. Feature Selection: Choose the relevant features that are most likely to influence the crop recommendation, such as soil pH, temperature, humidity, rainfall, etc.
4. Data Splitting: Divide the data into training and testing sets to evaluate the model’s performance.
5. Model Selection: Choose a machine learning algorithm suitable for this problem, such as Decision Trees, Random Forests, or Gradient Boosting Machines.
6. Model Training: Train the chosen model on the training set using the selected features.
7. Model Evaluation: Evaluate the model’s performance using the testing set and appropriate metrics like accuracy, precision, recall, or F1-score.
8. Prediction and Recommendation: Use the trained model to make predictions for new data and recommend crops accordingly.
9. User Interface: If needed, create a user-friendly interface where farmers can input the necessary data and receive recommendations.
10. Regular Updates: Regularly update the model with new data to ensure that the recommendations are aligned with the current conditions and trends.
11. Compliance: Make sure to follow all legal and ethical guidelines related to data collection and usage, especially if personal or sensitive information is involved.
12. Avoiding Spam Filters: If you are planning to communicate these recommendations through email, ensure that the emails are constructed properly, including relevant subject lines and content. Avoid using all caps or excessive exclamation marks as these might trigger spam filters. It may also be beneficial to provide an unsubscribe option if this communication is sent in bulk.

Building a crop recommendation system with machine learning can be a complex task, and this overview just scratches the surface. Depending on the specific requirements, you might need to dive deeper into each step, possibly engaging specialists in agronomy, data science, or software development.