Skip to content

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

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

# Write your code here

missing_val = crops.isna().sum().sum
missing_val
crop_type = crops["crop"].unique()
crop_type

create trining and test data using all features in crops

Let's split the crops dataset into training and test sets using all available features (N, P, K, ph) to predict the crop type.

# Define features and target
X = crops.drop('crop', axis=1)
y = crops['crop']

# Split the data into training and test sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Display the shape of the resulting datasets
print('Training features shape:', X_train.shape)
print('Test features shape:', X_test.shape)
print('Training labels shape:', y_train.shape)
print('Test labels shape:', y_test.shape)

Lets create 4 models for the features , and then lets store them in a dictionary called feature_dict

from sklearn.metrics import f1_score

feature_performance = {}

for feature in ["N", "P", "K", "ph"]:
    log_reg = LogisticRegression(multi_class='multinomial')
    log_reg.fit(X_train[[feature]], y_train)
    y_pred = log_reg.predict(X_test[[feature]])
    f1 = f1_score(y_test, y_pred, average='weighted')
    feature_performance[feature] = f1

feature_performance

Create a variable called best_predictive_feature. It should contain a single key-value pair. The key should be a string representing the name of the feature that produced the best model performance. The value should be the model's evaluation metric score.

# Find the feature with the best performance
best_feature = max(feature_performance, key=feature_performance.get)
best_predictive_feature = {best_feature: feature_performance[best_feature]}
best_predictive_feature