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.
# Import necessary libraries
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")
# Define features and target variable
features = ["N", "P", "K", "ph"] # Use "ph" instead of "pH"
target = "crop"
# Dictionary to store feature performance scores
feature_scores = {}
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(crops[features], crops[target], test_size=0.2, random_state=42)
# Loop through each feature and train a model
for feature in features:
X_train_feature = X_train[[feature]] # Use only the current feature
X_test_feature = X_test[[feature]] # Use only the current feature
# Train a logistic regression model
model = LogisticRegression(max_iter=200)
model.fit(X_train_feature, y_train)
# Predict on test set
y_pred = model.predict(X_test_feature)
# Evaluate model using accuracy score
accuracy = metrics.accuracy_score(y_test, y_pred)
# Store the feature and its performance score
feature_scores[feature] = accuracy
# Identify the best predictive feature
best_predictive_feature = {max(feature_scores, key=feature_scores.get): max(feature_scores.values())}
# Print the best feature and its score
print("Best Predictive Feature:", best_predictive_feature)
Executive Summary: Identifying the Best Predictive Feature for Crop Classification In this analysis, we aimed to determine which soil metric—Nitrogen (N), Phosphorous (P), Potassium (K), or pH—has the strongest predictive performance in classifying crop types. Using a machine learning approach, we trained individual models for each feature and evaluated their predictive accuracy.
Key Findings: Among the four soil metrics, Potassium (K) emerged as the most significant predictor of crop type. The predictive performance score for K was 0.295, indicating its stronger influence compared to the other features. Implications: Farmers and agricultural decision-makers should prioritize measuring Potassium (K) levels when selecting optimal crops for planting, as it has the highest impact on crop classification. While other soil metrics like Nitrogen (N), Phosphorous (P), and pH are important, they may have a lesser direct influence on crop type selection compared to K. Future research could explore multi-feature models to improve classification accuracy further. This insight can help optimize soil testing strategies, reducing costs and improving yield predictions for better agricultural planning.