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Sowing Success: How Machine Learning Helps Farmers Select the Best Crops

Measuring essential soil metrics such as nitrogen, phosphorous, potassium levels, and pH value is an important aspect of assessing soil condition. However, it can be an expensive and time-consuming process, which can cause farmers to prioritize which metrics to measure based on their budget constraints.

Farmers have various options when it comes to deciding which crop to plant each season. Their primary objective is to maximize the yield of their crops, taking into account different factors. One crucial factor that affects crop growth is the condition of the soil in the field, which can be assessed by measuring basic elements such as nitrogen and potassium levels. Each crop has an ideal soil condition that ensures optimal growth and maximum yield.

A farmer reached out to you as a machine learning expert for assistance in selecting the best crop for his field. They've provided you with a dataset called soil_measures.csv, which contains:

  • "N": Nitrogen content ratio in the soil
  • "P": Phosphorous content ratio in the soil
  • "K": Potassium content ratio in the soil
  • "pH" value of the soil
  • "crop": categorical values that contain various crops (target variable).

Each row in this dataset represents various measures of the soil in a particular field. Based on these measurements, the crop specified in the "crop" column is the optimal choice for that field.

In this project, you will build multi-class classification models to predict the type of "crop" and identify the single most importance feature for predictive performance.

# All required libraries are imported here for you.
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn import metrics

## STEP 1: # Load the dataset
# Read the data into a pandas DataFrame
crops = pd.read_csv("soil_measures.csv")

## STEP 2: Read the data into a pandas DataFrame and perform exploratory data analysis
# Get a glimpse of the dataset
print(crops.head())
print(crops.info())
print(crops.shape)
print(crops.describe())

# Check for missing values
crops.isna().sum()

# check to see types of crop types
crops['crop'].unique()

## STEP 3: Split the data
# Build X dataset
X = crops.drop("crop", axis=1).values

# Build Y dataset
y = crops["crop"].values 

# Split data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)

## STEP 4: Evaluate Feature performance 
features_dict = {}

# Loop through each feature
for feature in crops.drop("crop", axis=1).columns:
    # Reshape the feature to be 2D
    X_train_feature = X_train[:, crops.columns.get_loc(feature)].reshape(-1, 1)
    X_test_feature = X_test[:, crops.columns.get_loc(feature)].reshape(-1, 1)
    
    # Initialize and train the logistic regression model
    model = LogisticRegression()
    model.fit(X_train_feature, y_train)
    
    # Make predictions
    y_pred = model.predict(X_test_feature)
    
    # Evaluate the model
    accuracy = metrics.accuracy_score(y_test, y_pred)
    
    # Store the feature performance
    features_dict[feature] = accuracy

# Check out the features and their respective accuracies
features_dict

## STEP 5: Identify the best predictive feature
best_feature = max(features_dict, key=features_dict.get)
best_accuracy = features_dict[best_feature]

# Create the best_predictive_feature variable
best_predictive_feature = {best_feature: best_accuracy}

# View Result
print(best_predictive_feature)