Future Farming : Advancing Agriculture with Artificial Intelligence / edited by Praveen Kumar Shukla and Tushar Kanti Bera.

Format:

Book

Author/Creator:

Shukla, Praveen Kumar, Author.

Contributor:

Shukla, Praveen Kumar, editor.

Bera, Tushar Kanti, editor.

Language:

English

Subjects (All):

Agricultural innovations.

Artificial intelligence--Agricultural applications.

Artificial intelligence.

Physical Description:

1 online resource (166 pages)

Edition:

First edition.

Place of Publication:

Sharjah, United Arab Emirates : Bentham Science Publishers, [2023]

Summary:

Artificial Intelligence is vital to the evolution of agriculture into a smart industry. The objective of this book is to inform readers about how artificial intelligence is improving agriculture by exploring its applications. The book addresses several aspects of artificial intelligence applications in smart agriculture including, pest control, disease identification, weed detection, and security. Chapters are contributed by experts in agriculture, computer science and biotechnology. Key Themes: Advanced machine learning techniques for pest control and disease identificationAutomated recognition and classification of plant diseases, focusing on tomatoes and pearl milletIntegration of artificial intelligence for solar-powered robots to identify weeds and damages in vegetablesDevelopment of field prevention systems to deter wild animals in farming areasUtilization of machine learning for weather forecasting to facilitate smart agriculture practicesIntelligent crop planning and precision farming through AI applicationsIntegration of artificial intelligence and drones to enhance efficiency and effectiveness in smart farming operations Other features of the book include a list of references and simple summaries in each chapter to distil the information for readers. The book is a primary reference material for courses on automation in agriculture. It can also serve as a handbook for anyone interested in advances in farming.

Contents:

Intro

Table of Content

Title

BENTHAM SCIENCE PUBLISHERS LTD.

End User License Agreement (for non-institutional, personal use)

Usage Rules:

Disclaimer:

Limitation of Liability:

General:

FOREWORD

PREFACE

List of Contributors

Enhanced Machine Learning Techniques for Pest Control and Leaf Disease Identification

Abstract

INTRODUCTION

RELATED WORK

BACKGROUND STUDY

Artificial Neural Network (ANN)

Mayfly Optimization

Male Mayflie's Movement

Female Mayflie's Movement

Mating of Mayfly

BLACK WINDOW OPTIMIZATION

Mathematical Evaluation

PROPOSED METHODOLOGY

Pre-processing

Leaf Image from Plants - Segmentation Model Using Improved Canny Algorithm

Steps of Improved Canny Algorithm

Leaf Image Feature Selection Using Hybrid Black Widow Optimization Algorithm with Mayfly Optimization Algorithm (BWO-MA)

Pseudo-Code of the Hybrid (BWO-MA) Algorithm

Output: Objective Function's -RMSE

Leaf Image Classification Using (BWO-MA) with ANN

Hyper-Parameter Tuning With (BWO-MA)

RESULT AND DISCUSSION

Dataset Description

Evaluation &amp

Results

CONCLUSION

REFERENCES

Automatic Recognition and Classification of Tomato Leaf Diseases Using Transfer Learning Model

EXISTING WORKS

MATERIALS AND METHODS

Related Works

Convolution Neural Network

Convolution Layer

Activation Layer

Pooling Layer

Fully Connected Layer

SqueezeNet

PROPOSED WORK

Image Acquisition (Dataset)

Image Pre-Processing

Establishing a New Deep Network Using Transfer Learning

Recognition and Classification

EXPERIMENTAL RESULTS AND DISCUSSION

Experimental Setting and Environment

Evaluation Metrics

Experiment Deployment and Result Analysis

Comparison with Earlier Works

CONCLUSION AND FUTURE SCOPE

ACKNOWLEDGEMENTS.

REFERENCES

Detection and Categorization of Diseases in Pearl Millet Leaves using Novel Convolutional Neural Network Model

LITERATURE STUDY AND RELATED WORK

DATA AND METHODOLOGY

Data Acquisition

Data Pre-processing

Model Building and Validation

RESULTS AND ANALYSIS

CONCLUSION AND DISCUSSION

ACKNOWLEDGEMENTS

References

Artificial Intelligence-based Solar Powered Robot to Identify Weed and Damage in Vegetables

DIGITAL AGRICULTURE: IMPACT &amp

CHALLENGES

INTRODUCTION TO ROBOTICS

Robotics

Need of Robotics

Industrial Robots

Automation and Robotics

Control Systems for Robotics

Limited Sequence Robots (Non-Servo)

Point to Point Motion

Continuous Path Motion

Intelligent Robots

Presence of Movement for Robots in the Agriculture Sector

AN INTRODUCTION TO SOLAR ENERGY

Photovoltaic Effect on Solar Generation

Solar Cell: Construction and Working

LOAD CALCULATION OF SOLAR PANELS

For DC Loads

For AC Loads

Deciding Battery capacity

SAMPLE SYSTEM DESIGN

AGRICULTURAL ROBOT

Mechanical Design of Agricultural Robot

WORKING OF SOLAR ROBOT

COMPUTER VISION AND MACHINE LEARNING

EVALUATING THE QUALITY OF VEGETABLES USING MACHINE VISION

CLASSIFICATION ALGORITHM

METHODS FOR COLOUR SELECTION AND EXTRACTION

Field Prevention System from Wild Animals

LITERATURE REVIEW

PROPOSED INNOVATION SYSTEM

Regular CNN

FLOWCHART

Algorithm:

SYSTEM REQUIREMENTS

OPERATING SYSTEM-

SOFTWARE REQUIREMENTS

HARDWARE REQUIREMENTS

DESIGN AND IMPLEMENTATION CONSTRAINTS

Sensors

Boards

Others

BLOCK DIAGRAM

HARDWARE RESULT

Acknowledgments

REFERENCES.

Weather Forecasting using Machine Learning for Smart Farming

WEATHER FORECAST USING LINEAR REGRESSION, AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND LONG-SHORT TERM MEMORY MODEL

Linear Regression

Auto-Regressive Integrated Moving Average (ARIMA)

Long short-term memory (LSTM)

The Architecture of LSTM Network

EXPERIMENTAL RESULTS

Intelligent Crop Planning and Precision Farming

Precision Farming

Need for Precision Farming

Precision Farming and changing times

Past

Present

Precision Farming: Scenario of India

Precision Farming: An add on

Tools and Techniques Used for Precision Farming

Global Positioning System (GPS)

Sensor Technologies

Geographic Information System (GIS)

Grid Soil Sampling and Variable-rate Fertilizer (VRT) Application

Crop Management

Soil and Plant Sensors

Rate Controllers

Precision Irrigation in Pressurized Systems

Software

Intelligent Crop Planning

Intelligent Crop Planning and Artificial Intelligence

Climate-smart Agriculture

Challenges that Remain

Data

Infrastructure

Artificial Intelligence and Drones in Smart Farming

CONTRIBUTION OF THE AGRICULTURE SECTOR IN DIFFERENT TERMS

Contribution to Employment

Contribution to Exports

Contribution to GDP

METHODS TO IMPROVE FARMING PRODUCTIVITY

Reformation of Land

Challenges

Inter-plantation

Smart Water Management

Heat Tolerant Varieties

Plant Protection

USE OF TECHNOLOGY IN AGRICULTURE TO OVERCOME CHALLENGES

Improvement in Productivity Through the Mechanization of Agriculture

Climate Forecasting Prediction Through Artificial Intelligence.

Improving Farm Yields and Supply Chain Management Uses Big Data.

Why Agricultural Drone Should be adopted?

How can Drones Support Indian Agriculture?

WORKING OF DRONE TECHNOLOGY

BEST DRONE PRACTICES

BENEFITS OF DRONE TECHNOLOGY

DISCUSSION

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references.

ISBN:

981-5124-72-2