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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 apply machine learning to build a multi-class classification model to predict the type of "crop", while using techniques to avoid multicollinearity, which is a concept where two or more features are highly correlated.

# All required libraries are imported here for you.
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import seaborn as sns
from sklearn.metrics import f1_score

# Load the dataset
crops = pd.read_csv("soil_measures.csv")

# Write your code here
crops.head()
# Viewing data set types
print(crops.info(), "\n")

# Viewing dataset description
print(crops.describe(), "\n")

# Viewing labels in crop column and recurrence
print(crops['crop'].value_counts(), "\n")

# Looking for missing data
print(crops.isna().sum(), "\n")

After checking the content of the data we can see that there is no missing data and each column is the right data type.

# Features
features = ["N", "P", "K", "ph"]

# Splitting data set into train and test subsets
X_train, X_test, y_train, y_test = train_test_split(crops[features], crops["crop"], test_size=0.2, random_state=42)
for feat in features:
    # Instantiating the LogisticRegression model
    # - max_iter: maximum number of iteration for the algorithm to find the coefficients that minimize the loss function
    log_reg = LogisticRegression(max_iter=2000, multi_class='multinomial')
    log_reg.fit(X_train[[feat]], y_train)
    y_pred = log_reg.predict(X_train[[feat]])
    print("Trainning feature: {}".format(feat))
    score = f1_score(y_train, y_pred, average='weighted')
    print("Score: {}\n".format(score))
# Identifying correlations between features
sns.heatmap(crops[features].corr(), annot=True)
plt.show()

The highest correlation features is P, Phosphorus. We will remove this feature from our model to avoid multicollinearity.

# New features with lowest correlation
final_features = ["N", "K", "ph"]

# Splitting data set into train and test subsets
X_train, X_test, y_train, y_test = train_test_split(crops[final_features], crops["crop"], test_size=0.2, random_state=42)

# Testing and training new model
log_reg = LogisticRegression(max_iter=2000, multi_class='multinomial')
log_reg.fit(X_train, y_train)
y_pred = log_reg.predict(X_test)
print("Prediction: ", prediction)
model_performance = f1_score(y_test, y_pred, average='weighted')
print("Score: {}\n".format(model_performance))