Future Farming : Advancing Agriculture with Artificial Intelligence.

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 &
Results
CONCLUSION
REFERENCES
Automatic Recognition and Classification of Tomato Leaf Diseases Using Transfer Learning Model
Abstract
INTRODUCTION
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
Abstract
INTRODUCTION
LITERATURE STUDY AND RELATED WORK
DATA AND METHODOLOGY
Data Acquisition
Data Pre-processing
Model Building and Validation
Evaluation Metrics
RESULTS AND ANALYSIS
CONCLUSION AND DISCUSSION
ACKNOWLEDGEMENTS
References
Artificial Intelligence-based Solar Powered Robot to Identify Weed and Damage in Vegetables
Abstract
INTRODUCTION
DIGITAL AGRICULTURE: IMPACT &
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
CONCLUSION AND FUTURE SCOPE
ACKNOWLEDGEMENTS
REFERENCES
Field Prevention System from Wild Animals
Abstract
INTRODUCTION
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
CONCLUSION
Acknowledgments
REFERENCES.

Weather Forecasting using Machine Learning for Smart Farming
Abstract
INTRODUCTION
LITERATURE REVIEW
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
CONCLUSION
REFERENCES
Intelligent Crop Planning and Precision Farming
Abstract
INTRODUCTION
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
CONCLUSION
REFERENCES
Artificial Intelligence and Drones in Smart Farming
Abstract
INTRODUCTION
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
Challenges
Smart Water Management
Challenges
Heat Tolerant Varieties
Challenges
Plant Protection
Challenges
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
CONCLUSION
REFERENCES.