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Predicting the Type of Disease in Grapevines using Python

In this Python script, we leverage data analysis and visualization libraries such as pandas, matplotlib, and seaborn to assess the health status of grapevines based on various factors. The code reads vineyard data, classifies plant health, defines management strategies, and calculates the vineyard's life expectancy. Utilizing conditional statements, the script categorizes plants into different health statuses and recommends corresponding management strategies. Additionally, visualizations, including pie charts and stacked bar charts, provide insights into the distribution of health statuses and age distribution of vineyards. This comprehensive approach facilitates the prediction and management of grapevine diseases, aiding vineyard practitioners in making informed decisions.

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Comprehensive Python Solution for Grapevine Disease Prediction and Management

Explore the capabilities of this Python script designed for predicting and managing grapevine diseases. Using powerful libraries such as pandas, matplotlib, and seaborn, the script conducts a detailed analysis of vineyard data. It efficiently classifies plant health, defines targeted management strategies, and calculates life expectancy, providing practitioners with crucial insights for decision-making. Employing conditional statements, the code categorizes plants based on health status, offering a nuanced understanding of disease severity. The accompanying visualizations, including informative pie charts and stacked bar charts, enhance comprehension of health status distributions and vineyard age demographics. Whether you are a practitioner seeking insights into grapevine health or a student needing assistance with your Python assignment, this script is a comprehensive solution.

Block 1: Import Necessary Libraries

# Import necessary libraries import pandas as pd import matplotlib.pyplot as plt import seaborn as sns

This block imports the required libraries for data analysis and visualization: pandas for handling data frames, matplotlib.pyplot for creating plots, and seaborn for enhancing the visual appeal of the plots.

Block 2: Reading the Data

# Reading the data df = pd.read_csv('Data_for_Assignment_Curated_BV.csv') print('The data looks like\n') df.head()

This block reads a CSV file named 'Data_for_Assignment_Curated_BV.csv' into a Pandas DataFrame (df) and displays the first few rows of the data.

Block 3: Classify the Plant Health Status

# Classify the plant health status def classify_plant_health(row): # ... (multiple if-elif conditions to classify plant health) return 'Unknown' df['Health_Status'] = df.apply(classify_plant_health, axis=1)

This block defines a function (classify_plant_health) to classify the health status of each plant based on various conditions, and then applies this function to create a new column 'Health_Status' in the DataFrame.

Block 4: Define a Management Strategy

# Define a management strategy def define_management_strategy(row): # ... (multiple if-elif conditions to define management strategy) return 'Unknown' df['Management_Strategy'] = df.apply(define_management_strategy, axis=1)

Similar to the previous block, this one defines a function (define_management_strategy) to determine a management strategy for each plant and creates a new column 'Management_Strategy' in the DataFrame.

Block 5: Displaying Updated DataFrame

pd.set_option('display.max_colwidth', None) df.head()

This block sets an option to display the full content of DataFrame columns and then displays the updated DataFrame with the new columns 'Health_Status' and 'Management_Strategy'.

Block 6: Calculate Life Expectancy

# Calculate life expectancy # ... (user input for total plants, diseased plants, vineyard age, and disease severity) life_expectancy = calculate_life_expectancy(total_plants, num_diseased_plants, vineyard_age, disease_severity) print("The life expectancy of the vineyard is ", life_expectancy)

This block takes user inputs (total plants, diseased plants, vineyard age, disease severity) and calculates the life expectancy of the vineyard based on certain conditions.

Block 7: Visualization

# Block 7.1: Health Status Distribution Pie Chart # ... (code for pie chart) # Block 7.2: Health Status Distribution by Vineyard Stacked Bar Chart # ... (code for stacked bar chart) # Block 7.3: Age Distribution of Vineyards Histogram with KDE # ... (code for histogram with KDE)

Each of these blocks serves a specific purpose in analyzing and visualizing the data, classifying plant health, defining management strategies, and providing insights into the vineyard's life expectancy.

Conclusion

In conclusion, the provided Python code performs a comprehensive analysis and management strategy formulation for a vineyard based on plant health data. Beginning with data import and exploration, it classifies plant health and defines corresponding management strategies. The code further calculates the vineyard's life expectancy, offering actionable insights based on disease severity and vineyard age. Visualizations, including pie charts and bar graphs, provide a clear overview of health status distributions. This holistic approach integrates data manipulation, classification, strategy definition, and visualization, empowering vineyard managers to make informed decisions for optimal plant health and longevity. The code demonstrates the power of Python in combining data science and practical insights for effective agricultural management.