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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

SWAMP: A High-precision Smart Irrigation System

Drishti
Drishti Last Updated : 27 Sep, 2022
5 min read

This article was published as a part of the Data Science Blogathon.

How does Climate Smart Agriculture using Satellite Technology Helps in Farming | Farmsio

                                                          Source: farmsio

Introduction

Water is essential for ensuring food security for the entire world’s population, and agriculture is the main consumer amounting to up to 70% of freshwater. The water wastages are caused primarily due to leakages in distribution and irrigation systems. One of the most popular conventional methods, i.e. surface irrigation wastes a significant amount of water by wetting areas where no plants benefit. Localized irrigation can utilize water more efficiently and effectively, preventing over- and under-irrigation. However, to prevent under-irrigation, farmers often use more water than necessary, reducing productivity and wasting water. Therefore, solutions should be developed and deployed for sensing the amount of water needed by the plantation and for flowing the water to appropriate locations.

In this article, we will explore a high-precision smart irrigation system developed for agriculture as a part of the SWAMP project, which aims to tackle the challenges mentioned earlier with the help of the Internet of Things (IoT), data analytics, autonomous devices, and other related technologies. We will take a look at the Farmer App offered by SWAMP for smart water status monitoring and irrigation control.

Let’s start by discussing the challenges addressed by the SWAMP project!

Challenges Addressed by SWAMP Project

The SWAMP project has attempted to address the issues listed below:

1) Lowering the effort required to develop software for IoT-based smart applications.

2) Automating cutting-edge platforms and incorporating various technologies.

3) Incorporating diverse and cutting-edge sensors, particularly flying sensors (drones) that offer accuracy in the irrigation water supply.

4) Using Software platforms in conjunction with technologies such as IoT, Big Data, drones, etc for the deployment of pilot applications for smart water management.

5) Developing, evaluating, and validating new business models for leveraging IoT in smart water management settings.

6) Making flexible and adaptable technological components can adapt to various contexts and be replicable in various settings.

High-Precision Smart Irrigation System

Figure 1 shows the high-precision smart irrigation system concept that will be developed for agriculture as part of the SWAMP project. The main goal of the proposed idea is to make irrigation, water distribution, and consumption more efficient based on a comprehensive analysis of data gathered from all aspects of the system, including the natural water cycle and accumulated knowledge about growing specific plants. This solution detects all the leakages and losses and ensures better the availability of water in situations where water supply is limited, which benefits all parties.

Figure 1: High-precision irrigation system based on smart water management (Source: swamp-project)

As shown in Figure 1, broadly, there are three phases in a water management system for agriculture:

W1: Water reserve: Water reserves from various sources like lakes, rivers, dams, aquifers, etc., which follow the natural water cycle.

W2: Water distribution: Water is distributed from W1 to the location where it will be used (W3) via a network of elements such as pipes, pumps, valves, gateways, canals, etc. Water distribution may assume various configurations depending on the region/country, but getting water from a reserve to a field is always necessary. On the one hand, there are instances where water resources are prudently utilized and managed by a centralized authority, as in many Italian locations where consortia were in charge of managing a system of water distribution canals. On the other hand, sometimes W1, W2, and W3 are part of the same property and therefore are fully integrated, notably in many Brazilian agriculture areas. In the latter case, W2 is much simpler but needs to be managed carefully.

W3: Water consumption: In agriculture, one of the main uses of water is irrigation, which can be performed using various techniques.

The SWAMP platform will offer tools for gathering data from heterogeneous sensors, decision-making using various techniques, and modifying system behaviour by transmitting commands to actuators. The SWAMP platform will also offer real-time responses for adjusting the amount of irrigation based on environmental conditions. On the other hand, as adjustments are made to water distribution in a different timescale, the management loop for W2 will be longer. Integrating W2 and W3 management systems is also crucial because water usage triggers water distribution.

SWAMP Farmer App for Smart Water Status Monitoring and Irrigation Control

The Farmer App offers a user-friendly interface to smart water services utilizing the SWAMP platform. The services are accessed through the RESTful API and SWAMP API (See Figure 2).

Figure 2:  Diagram illustrating how services are accessed through SWAMP API. (Source: swamp-project)

The Farmer App enables monitoring of field water status, including real-time measurements and historical data in the form of charts (figure 3, on the left). A satellite view of the fields is also available, allowing the farmer to see things like farm divisions, probe locations, etc (figure 3, on the right). The farmer can also visualize and modify irrigation plans suggested by the SWAMP platform. The irrigation plan suggests the timing and volume of water of irrigation events for all fields (and their divisions), providing the farmer control over the water cycle in their farm.

Figure 3: Snapshot of Farmer App including i) current measurements and historical data in the form of charts (Left Image), ii) A satellite view of the fields showing farm divisions and probe locations, etc. (Left Image), (Source: swamp-project)

Conclusion

To summarize, in this article, we learned the following:

1) To manage and conserve water by preventing under-irrigation and over-irrigation SWAMP project has been proposed, which ensures that the water flows to appropriate locations as needed with the help of the Internet of Things (IoT), data analytics, autonomous devices, and other related technologies.

2) Challenges addressed by the SWAMP project include i) automating cutting-edge platforms and incorporating various technologies and sensors, ii) Developing, evaluating, and validating new business models for leveraging IoT in smart water management settings, iii) Making flexible and adaptable technological components such that they can adapt to various contexts and be replicable in various settings, etc.

3) A high-precision smart irrigation system concept is developed for agriculture as part of the SWAMP project. The main goal is to make irrigation, water distribution, and consumption more efficient based on a comprehensive analysis of data gathered from all aspects of the system, including the natural water cycle and accumulated knowledge about growing specific plants.

4) The Farmer App offers a user-friendly interface to smart water services utilizing the SWAMP platform. It enables monitoring of field water status, including real-time measurements and historical data in charts. The farmer can also visualize and modify irrigation plans suggested by the SWAMP platform.

That concludes this article. Thanks for reading. If you have any questions or concerns, please post them in the comments section below. Happy learning!

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

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Drishti
Drishti

I'm a Researcher who works primarily on various Acoustic DL, NLP, and RL tasks. Here, my writing predominantly revolves around topics related to Acoustic DL, NLP, and RL, as well as new emerging technologies. In addition to all of this, I also contribute to open-source projects @Hugging Face.
For work-related queries please contact: [email protected]

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