Advanced Analytics of Agricultural Datasets

1. Chapter One: Precision Farming and Crop Management

1.1. Maximizing Yield through Soil Health Monitoring

In a region where soil health has been deteriorating due to overuse and erosion, farmers are struggling to maintain crop yield. How can data science help to develop an AI-powered soil monitoring system that uses data from IoT sensors to analyze real-time soil health metrics such as moisture, pH, and nutrient levels? How can this data be used to predict future crop yields and recommend optimal soil management practices? Explore the role of machine learning models in forecasting crop performance based on soil health trends, historical data, and climatic conditions, and identify patterns that lead to sustainable soil usage.

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Project Title: Maximizing Yield through Soil Health Monitoring

Introduction:

In modern agriculture, optimizing crop yields is becoming increasingly critical as soil health deteriorates due to overuse and erosion. The over-exploitation of agricultural lands leads to soil degradation, which in turn negatively impacts farmers' ability to maintain crop productivity. Soil health plays a vital role in sustaining crop performance, and monitoring its condition can provide key insights into managing soil usage more sustainably. With the advent of Internet of Things (IoT) technology, farmers now have the ability to collect real-time data on various soil health metrics, such as moisture levels, pH, and nutrient content. By using data science, artificial intelligence (AI), and machine learning (ML), an AI-powered soil health monitoring system can be developed to analyze these real-time metrics. The system would predict future crop yields and recommend optimal soil management practices. This project focuses on using advanced data analytics techniques to create a soil monitoring system capable of forecasting crop performance, thereby promoting sustainable farming practices through data-driven insights.

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Statement of the Problem:

Farmers are struggling to maintain optimal crop yields due to soil degradation caused by overuse and erosion. The absence of timely interventions based on real-time soil health monitoring has led to suboptimal soil management practices, exacerbating the problem. Developing a data-driven AI-powered soil monitoring system that leverages real-time data from IoT sensors to predict crop yields and recommend management practices is crucial for sustainable agriculture.

Business Objectives:

Develop an AI-powered soil health monitoring system to predict crop yields.

Analyze real-time soil health metrics such as moisture, pH, and nutrient levels using advanced data analytics.

Provide data-driven recommendations for optimal soil management practices.

Ensure sustainable soil use by identifying patterns that promote long-term agricultural productivity.

Stakeholders:

Farmers and Agricultural Managers

Agronomists and Soil Scientists

IoT Sensor Manufacturers

Data Scientists and AI Engineers

Government Agricultural Agencies

Environmental Conservation Organizations

Research Questions:

How can real-time data from IoT sensors be analyzed to monitor soil health effectively?

Can AI models predict future crop yields based on soil health metrics and climatic conditions?

What soil health trends are associated with higher crop performance?

How can data analytics identify optimal soil management practices that promote sustainable farming?

Advanced Analytics to Conduct:

For Question 1:

Use Time-Series Analysis to track soil health metrics (moisture, pH, nutrient levels) collected via IoT sensors. Implement advanced models like ARIMA, LSTMs, or Prophet for forecasting changes in soil conditions.

For Question 2:

Predictive Modeling using machine learning algorithms like Random Forest, Gradient Boosting Machines (GBMs), or neural networks can be used to predict crop yields based on the soil health metrics combined with climatic data. These models should learn the complex, non-linear relationships between soil health indicators and crop performance.

For Question 3:

Trend Detection using clustering techniques (e.g., K-means, DBSCAN) or anomaly detection to find patterns in soil health data that correspond to high crop yields. Combine these with regression models to estimate the impact of specific soil health trends on yield.

For Question 4:

Optimization Techniques using Reinforcement Learning or Genetic Algorithms to identify optimal soil management practices based on historical data and simulation of different soil treatment scenarios. This will help in decision-making for fertilizer use, irrigation, and other management actions.

KPIs and Metrics:

Crop Yield Prediction Accuracy: Metrics such as R^2, MAE, and RMSE can be used to evaluate the model’s accuracy in predicting crop yields.

Soil Health Score: A composite index of soil metrics (moisture, pH, nutrients) that serves as a health score.

Timeliness of Recommendations: Time taken by the system to provide actionable insights to farmers.

Clustering Quality: Measured using silhouette score to assess the separation between soil health trends and crop yields.

Yield Optimization Gain: The percentage increase in yield due to the recommended management practices.

Required Dependent and Independent Variables:

Dependent Variable: Crop Yield (numeric, in tons/hectare)

Independent Variables:

Soil Moisture (numeric, in percentage)

Soil pH (numeric)

Nitrogen Level (numeric, in mg/kg)

Phosphorus Level (numeric, in mg/kg)

Potassium Level (numeric, in mg/kg)

Climatic Data (temperature, humidity, rainfall)

Open Data Sources:

Kaggle: Crop and soil data (e.g., Crop Yield Prediction Dataset)

Open Weather API: Provides real-time weather data for agricultural analysis.

FAO Data: Global soil and crop data from the Food and Agriculture Organization (FAO).

World Bank Open Data: Agricultural indicators and environmental data.

Example Arbitrary Dataset:

Date Soil_Moisture (%) Soil_pH Nitrogen (mg/kg) Phosphorus (mg/kg)  Potassium (mg/kg)  Crop_Yield (tons/hectare)

2023-10-01 30 6.5 50 40 150 5.5

2023-10-02 28 6.3 48 38 148 5.4

2023-10-03 32 6.8 52 45 155 5.8

2023-10-04 29 6.2 47 42 145 5.3

2023-10-05 31 6.4 49 41 149 5.6

Elaboration of Dataset:

Dependent Variable: Crop_Yield (numeric) – This represents the target variable, indicating crop production per hectare.

Independent Variables:

Soil_Moisture (numeric) – The percentage of water content in the soil.

Soil_pH (numeric) – The acidity or alkalinity of the soil.

Nitrogen, Phosphorus, Potassium (numeric) – Key soil nutrients necessary for plant growth.

Date (datetime) – The timestamp for when the soil metrics were collected.

These variables offer a comprehensive overview of soil health and its impact on crop yield, with each data type suited to specific analysis techniques (e.g., numeric for predictive models, datetime for time-series analysis).

Assumed Results with Quantified Values:

Crop Yield Prediction Accuracy: The Random Forest model predicted crop yields with an R² of 0.89, indicating a strong correlation between soil health metrics and crop yield.

Soil Moisture Trends: An increase in soil moisture from 28% to 32% was associated with a 7% rise in crop yield (from 5.3 to 5.8 tons/hectare).

Nutrient Impact: A nitrogen level above 50 mg/kg was linked to a 10% increase in yield.

Clustering Results: Three distinct soil health clusters were identified, with the best-performing cluster showing 15% higher yield due to optimal moisture and nutrient levels.

Draft Report:

Project Title: Maximizing Yield through Soil Health Monitoring

Summary:

This project focuses on developing an AI-powered soil health monitoring system aimed at maximizing crop yields by analyzing real-time soil metrics such as moisture, pH, and nutrients. By using data from IoT sensors, this project demonstrates how data science and machine learning models can predict future crop yields and suggest optimal soil management practices. The results indicate that specific soil health trends, such as moisture and nutrient levels, are strongly correlated with increased crop yields, providing actionable insights for farmers.

Introduction:

Soil health is a fundamental factor in ensuring sustainable agricultural productivity. In many regions, however, overuse and erosion have deteriorated soil conditions, making it difficult for farmers to maintain optimal crop yields. Traditional methods of soil health management often lack the precision and timeliness required for modern agriculture. The development of IoT technology allows real-time soil health monitoring, giving farmers the ability to collect continuous data on key soil metrics. By applying data science and machine learning techniques, this project aims to build an AI-powered soil monitoring system capable of predicting crop yields and recommending effective soil management practices. This system will analyze moisture levels, pH, and nutrient content to forecast crop performance, promoting sustainable soil usage and boosting agricultural output.

Business Objective:

The business goal of this project is to help farmers optimize their crop yields by leveraging AI-driven insights from soil health data. Specifically, the project aims to predict future crop yields and offer recommendations for soil management based on real-time metrics such as soil moisture, pH, and nutrients.

Statement of the Problem:

The current challenge faced by farmers is the inability to monitor and manage soil health effectively, leading to reduced crop yields. This problem is exacerbated by soil degradation due to overuse and poor management. The absence of an AI-powered system that can analyze real-time data and offer actionable insights is a significant barrier to sustainable farming.

Methodology:

The project utilizes IoT sensors to collect real-time soil data, which is then analyzed using machine learning models. Data from variables such as soil moisture, pH, nitrogen, phosphorus, and potassium levels are used as input features for predictive models like Random Forest and Gradient Boosting Machines. Time-series analysis was employed to track changes in soil health over time, while clustering techniques identified patterns in soil metrics that were associated with higher crop yields.

Assumptions and Ethical Considerations:

Assumptions include the availability of accurate IoT sensor data and historical crop yield records. Ethical considerations involve ensuring data privacy and transparency in data collection and analysis.

Assumed Results:

The Random Forest model achieved a high prediction accuracy (R² = 0.89) in forecasting crop yields. The results show that soil moisture levels between 30-32% were associated with a 7% increase in crop yield. Optimal nitrogen levels (above 50 mg/kg) led to a 10% increase in yield. Clustering identified three soil health clusters, with the most productive one showing a 15% higher yield due to balanced moisture and nutrient levels.

Discussion:

The results highlight the importance of real-time soil health monitoring in maximizing agricultural productivity. Higher soil moisture levels were consistently linked with increased crop yields, suggesting that precise irrigation management is critical. Nutrient levels, particularly nitrogen, played a significant role in crop performance, underscoring the need for optimized fertilizer use. The AI-powered system demonstrated its potential by accurately predicting crop yields, providing farmers with actionable insights to improve soil health management. Clustering techniques helped identify optimal soil conditions, leading to more targeted soil treatment strategies. Overall, the integration of IoT data, advanced analytics, and machine learning offers a promising solution for sustainable farming.

Conclusions:

The project successfully demonstrated how AI and data science can be used to monitor soil health and predict crop yields. By leveraging real-time soil data from IoT sensors, farmers can make informed decisions that lead to better soil management and higher yields. The use of machine learning models provided valuable insights into soil health trends, enabling more sustainable agricultural practices.

The Way Forward:

Future work should focus on expanding the dataset to include more regions and crops, as well as integrating weather and climate data for even more accurate yield predictions. Continuous data collection and refinement of machine learning models will be key to improving the system's performance.

Implementation Plan for Key Insights:

Deploy IoT sensors across farms to continuously monitor soil health.

Integrate the AI-powered soil monitoring system into farm management tools.

Use predictive analytics to optimize irrigation and fertilization schedules.

Provide farmers with real-time recommendations for soil treatment based on AI analysis.

Remarks:

This project analysis serves as a practical guideline for understanding how advanced data analytics can be applied to agricultural datasets. The results, conclusions, and recommendations are based on assumed data, and users should apply their own datasets to achieve real-world results.

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# Python Code for Soil Health Monitoring and Crop Yield Prediction

# Dataset: 'df.csv'

import pandas as pd

import numpy as np

from sklearn.ensemble import RandomForestRegressor

from sklearn.model_selection import train_test_split, GridSearchCV

from sklearn.metrics import mean_squared_error, r2_score, classification_report, confusion_matrix

import matplotlib.pyplot as plt

from sklearn.cluster import KMeans

import seaborn as sns

from statsmodels.tsa.arima.model import ARIMA

# Load dataset

df = pd.read_csv('df.csv')

# Preprocessing

df['Date'] = pd.to_datetime(df['Date'])

df = df.set_index('Date')

# Define independent variables (soil metrics) and dependent variable (crop yield)

X = df[['Soil_Moisture', 'Soil_pH', 'Nitrogen', 'Phosphorus', 'Potassium']]

y = df['Crop_Yield']

# Split dataset 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)

# Random Forest Regressor for Crop Yield Prediction

model = RandomForestRegressor(n_estimators=100, random_state=42)

model.fit(X_train, y_train)

# Predictions

y_pred = model.predict(X_test)

# Evaluation for Question 1

print(fR² Score: {r2_score(y_test, y_pred)})

print(fMean Squared Error: {mean_squared_error(y_test, y_pred)})

# Plotting Actual vs Predicted Yields

plt.scatter(y_test, y_pred)

plt.xlabel(Actual Crop Yield)

plt.ylabel(Predicted Crop Yield)

plt.title(Actual vs Predicted Crop Yield)

plt.show()

# Question 2: Clustering Soil Health Data for Yield Optimization

kmeans = KMeans(n_clusters=3, random_state=42)

df['Cluster'] = kmeans.fit_predict(X)

# Visualize clusters based on Soil Moisture and Crop Yield

plt.scatter(df['Soil_Moisture'], df['Crop_Yield'], c=df['Cluster'], cmap='viridis')

plt.xlabel(Soil Moisture)

plt.ylabel(Crop Yield)

plt.title(Clusters of Soil Health vs Crop Yield)

plt.show()

# Question 3: Time-Series Analysis of Soil Metrics and Crop Yields

# Fitting ARIMA Model for Soil Moisture Trends

model_arima = ARIMA(df['Soil_Moisture'], order=(5, 1, 0))

model_arima_fit = model_arima.fit()

df['Soil_Moisture_Forecast'] = model_arima_fit.predict(start=0, end=len(df)-1)

# Plotting Soil Moisture and Forecasted Values

plt.figure(figsize=(10,6))

plt.plot(df.index, df['Soil_Moisture'], label='Actual Soil Moisture')

plt.plot(df.index, df['Soil_Moisture_Forecast'], label='Forecasted Soil Moisture', linestyle='—')

plt.xlabel(Date)

plt.ylabel(Soil Moisture)

plt.title(Time-Series Analysis of Soil Moisture)

plt.legend()

plt.show()

# Question 4: Predictive Modeling for Optimal Soil Management Practices

# Define a function to calculate soil health score based on multiple factors

def calculate_soil_health_score(moisture, pH, nitrogen, phosphorus, potassium):

# Assuming arbitrary weights for each factor

score = (0.25 * moisture) + (0.20 * (7 - abs(pH - 7))) + (0.20 * nitrogen) + (0.15 * phosphorus) + (0.20 * potassium)

return score

df['Soil_Health_Score'] = df.apply(lambda row: calculate_soil_health_score(

row['Soil_Moisture'], row['Soil_pH'], row['Nitrogen'], row['Phosphorus'], row['Potassium']), axis=1)

# Splitting into training and test sets for management recommendation model

X_score = df[['Soil_Health_Score']]

y_score = df['Crop_Yield']

X_train_score, X_test_score, y_train_score, y_test_score = train_test_split(X_score, y_score, test_size=0.3, random_state=42)

# Predicting crop yield based on soil health score using Random Forest

model_score = RandomForestRegressor(n_estimators=100, random_state=42)

model_score.fit(X_train_score, y_train_score)

# Predictions and Evaluation

y_pred_score = model_score.predict(X_test_score)

print(fR² Score for Soil Health Score Model: {r2_score(y_test_score, y_pred_score)})

print(fMean Squared Error for Soil Health Score Model: {mean_squared_error(y_test_score, y_pred_score)})

# Plot Actual vs Predicted Yield Based on Soil Health Score

plt.scatter(y_test_score, y_pred_score)

plt.xlabel(Actual Crop Yield)

plt.ylabel(Predicted Crop Yield)

plt.title(Actual vs Predicted Crop Yield Based on Soil Health Score)

plt.show()

# Question 5: Sentiment/Pattern Shifts Over Time

# Create rolling averages to see soil metrics' impact over time

df['Soil_Moisture_Rolling'] = df['Soil_Moisture'].rolling(window=7).mean()

df['Nitrogen_Rolling'] = df['Nitrogen'].rolling(window=7).mean()

# Plot the impact of soil health metrics over time on yield

plt.figure(figsize=(10,6))

plt.plot(df.index, df['Soil_Moisture_Rolling'], label='Soil Moisture (7-day rolling avg)', color='blue')

plt.plot(df.index, df['Crop_Yield'], label='Crop Yield', color='green')

plt.xlabel(Date)

plt.ylabel(Value)

plt.title(Soil Moisture and Crop Yield Trends Over Time)

plt.legend()

plt.show()

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Explanation of Python codes:

Question 1: Analyzing Soil Health Patterns Using Time-Series Data

In this part of the code, we aim to understand patterns in soil health using time-series analysis:

Data Loading and Preprocessing:

The dataset df.csv is loaded, and the 'Date' column is converted into a datetime format. The data is then indexed by this 'Date' column to prepare it for time-series analysis.

The dataset contains variables such as Soil Moisture, Soil pH, Nitrogen, Phosphorus, Potassium, and Crop Yield.

Independent and Dependent Variables:

Independent variables are the soil metrics (Soil Moisture, Soil pH, Nitrogen, Phosphorus, Potassium).

Dependent variable is Crop Yield, which we are trying to predict.

Random Forest Regressor:

A Random Forest Regressor is used to predict the crop yield based on the soil health metrics.

The dataset is split into training and testing sets (70% training, 30% testing) for model evaluation.

The model is fitted on the training data, and predictions are made on the testing data.

Model Evaluation:

The performance of the model is evaluated using R² score (coefficient of determination) and Mean Squared Error (MSE). A higher R² value indicates a better fit.

Visualization: A scatter plot is generated to compare the Actual vs Predicted Crop Yield, which helps in visualizing how well the model is performing.

Question 2: Clustering Soil Health Data for Yield Optimization

This part focuses on identifying patterns in the data through clustering to optimize soil health management practices:

Clustering with K-Means:

We apply the K-Means clustering algorithm to group data points based on similar soil health metrics (Soil Moisture, pH, Nitrogen, Phosphorus, and Potassium). Clustering helps identify trends in soil health conditions that result in higher or lower yields.

In this case, we create 3 clusters, where each cluster represents a different grouping of soil health conditions.

Visualization:

We visualize the clusters by plotting Soil Moisture against Crop Yield. Different colors represent different clusters, helping to identify how varying soil conditions impact crop yields.

Question 3: Time-Series Analysis of Soil Metrics and Crop Yields

Here, we conduct time-series analysis to predict how soil metrics evolve over time and how they impact crop yields:

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ARIMA Model for Time-Series Forecasting:

An ARIMA (AutoRegressive Integrated Moving Average) model is used to forecast future trends in Soil Moisture. ARIMA models are commonly used for time-series data to predict future values based on past behavior.

The model is fitted on the historical soil moisture data to forecast future soil moisture values, which helps in determining soil health changes over time.

Visualization:

A line plot is created to visualize both the Actual Soil Moisture values and the Forecasted Values. This gives insights into the expected future state of soil moisture, helping farmers make proactive soil management decisions.

Question 4: Predictive Modeling for Optimal Soil Management Practices

This section focuses on predicting optimal soil management practices by generating a Soil

Health Score:

Soil Health Score Calculation:

A custom function is created to compute a Soil Health Score based on multiple soil metrics (Moisture, pH, Nitrogen, Phosphorus, Potassium). We assign arbitrary weights to each of these metrics to compute a single health score.

This score serves as a composite indicator of the overall health of the soil, which can be used to make management decisions.

Predictive Modeling:

Similar to the earlier Random Forest model, we use the Soil Health Score as a single predictor to model the Crop Yield. This helps predict the yield based on an aggregated soil health indicator, making it easier to assess the impact of soil management interventions.

Model Evaluation:

The performance of the Soil Health Score Model is evaluated using R² score and Mean Squared Error (MSE). Higher R² scores and lower MSE values indicate that the soil health score is an effective predictor of crop yield.

Visualization:

A scatter plot is generated to visualize Actual vs Predicted Crop Yield based on the soil health score. This helps in understanding how the calculated score correlates with the actual crop yield.

Question 5: Sentiment/Pattern Shifts Over Time

This section tracks how soil metrics, such as moisture and nitrogen, change over time and how these shifts impact crop yield:

Rolling Average Calculation:

Rolling averages are computed for Soil Moisture and Nitrogen to smooth out short-term fluctuations and identify long-term trends. This is helpful to visualize how soil health metrics change over time.

Visualization:

A line plot is created to show the relationship between the rolling averages of soil metrics and the Crop Yield. This allows us to visualize the long-term trends and their impact on yield over time.

Overall Summary of the Code:

Data Loading and Preprocessing: We load and clean the dataset, setting up the necessary variables for analysis.

Random Forest for Yield Prediction: We use Random Forest to predict crop yield based on soil health metrics, and evaluate the model using standard metrics (R², MSE).

Clustering: We apply K-means clustering to group soil health data into clusters and analyze how these clusters relate to crop yield.

ARIMA Time-Series Analysis: We use ARIMA models to forecast future trends in soil moisture, helping to predict soil health conditions.

Soil Health Score Calculation: We develop a composite score based on various soil health factors, using it to predict crop yield.

Pattern Shift Analysis: We compute rolling averages to track long-term changes in soil metrics and visualize how these changes correlate with crop yields.

1.2. Climate-Smart Farming for Drought-Prone Regions

In regions frequently affected by drought, traditional farming methods are no longer sufficient to ensure crop survival. How can advanced data analytics help create AI-driven climate-smart farming systems to predict droughts, manage water resources, and improve irrigation efficiency? Investigate how machine learning can process historical weather patterns and real-time climate data to model future drought occurrences, determine the most drought-resistant crop varieties, and provide actionable insights to farmers about water usage, planting schedules, and soil preservation techniques.

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Introduction:

Agriculture is a critical sector, especially in regions prone to extreme climatic events such as droughts. In these areas, traditional farming methods often fail to ensure crop survival and maintain sustainable yields due to unpredictable environmental conditions. The rise of data science, artificial intelligence (AI), and machine learning (ML) offers new possibilities for tackling these challenges. Climate-smart farming leverages advanced analytics to help farmers adapt to changing climates by improving resource efficiency, such as water usage and irrigation, and enhancing crop resilience. By using historical weather data, real-time climate data, and machine learning models, farmers can predict droughts, manage water resources more effectively, and improve irrigation practices. This analysis aims to explore how AI-driven systems can forecast future drought events, identify drought-resistant crops, and provide actionable insights for water conservation, thereby ensuring the sustainability of agriculture in drought-prone regions.

Statement of the Problem:

Farmers in drought-prone regions face severe challenges in maintaining crop yields due to water scarcity and unpredictable drought events. Traditional farming practices are insufficient in addressing these issues, often leading to crop failure. The absence of data-driven decision-making tools hampers the ability to manage water resources effectively and adopt efficient irrigation techniques. Developing a climate-smart farming system that uses AI to predict droughts and optimize water use is essential for mitigating the effects of climate change on agriculture.

Business Objectives:

The primary objective is to develop an AI-powered climate-smart farming system that helps farmers in drought-prone regions:

Predict drought events using historical and real-time weather data.

Optimize water resource management through efficient irrigation techniques.

Identify drought-resistant crop varieties for improved agricultural resilience.

Provide actionable insights on planting schedules and soil conservation.

Stakeholders:

Farmers and Agricultural Managers

Government Agricultural Agencies

Data Scientists and AI Engineers

Environmental Organizations

Agronomists and Climate Scientists

Research Questions:

How can machine learning models analyze historical weather data to predict drought occurrences in a specific region?

Can AI models help identify the most drought-resistant crop varieties based on historical performance data?

How can real-time data be used to optimize water usage and improve irrigation efficiency?

How can data analytics support farmers in making better decisions regarding planting schedules and soil preservation?

Advanced Analytics to Conduct:

For Question 1:

Time-Series Analysis: Employ advanced time-series forecasting models such as Long Short-Term Memory (LSTM) networks and ARIMA to predict future drought events based on historical climate data, including temperature, rainfall, and humidity patterns.

For Question 2:

Classification and Predictive Modeling: Use classification algorithms like Random Forest and Gradient Boosting to determine the drought resistance of crop varieties based on performance metrics (yield, water usage) under different climatic conditions.

For Question 3:

Optimization and Simulation: Apply reinforcement learning models and genetic algorithms to optimize water usage. Simulate various irrigation strategies and assess their impact on crop yield and water conservation.

For Question 4:

Prescriptive Analytics: Use machine learning to generate prescriptive models that recommend the best planting schedules based on weather patterns and soil moisture levels. Data-driven insights can help optimize soil conservation techniques.

KPIs and Metrics:

Drought Prediction Accuracy: Measured using R², Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

Water Usage Efficiency: Percentage reduction in water usage due to optimized irrigation strategies.

Crop Yield Increase: The improvement in yield (tons/hectare) from using drought-resistant varieties and better irrigation methods.

Irrigation Scheduling Efficiency: Timeliness and accuracy of irrigation recommendations based on real-time data.

Dependent and Independent Variables: