Smart Agritech E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
The main goal of Smart Agritech: Robotics, AI, and Internet of Things (IoT) in Agriculture is to explore how emerging technologies such as robotics, artificial intelligence (AI), and IoT can be leveraged to improve efficiency, sustainability, and productivity in agriculture.
Agriculture has always been a vital sector of the global economy, providing food and raw materials for industries and households. However, with the growing population, changing climate conditions, and limited resources, the agriculture sector faces numerous challenges. To address these challenges, farmers and agricultural companies are turning to advanced technologies such as Robotics, Artificial Intelligence (AI), and the Internet of Things (IoT).
This exciting new volume provides a comprehensive overview of the latest technological advances in agriculture, focusing on these three cutting-edge technologies. The book will explore the potential benefits of these technologies in improving agricultural efficiency, productivity, and sustainability.
Whether for the veteran engineer, scientist in the lab, student, or faculty, this groundbreaking new volume is a valuable resource for researchers and other industry professionals interested in the intersection of technology and agriculture.
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Table of Contents
Cover
Table of Contents
Title Page
Copyright Page
Preface
1 Introduction to Smart Farming: Definition, Importance and Trends
1.1 Introduction
1.2 Smart Farming
1.3 Internet of Things
1.4 Technologies Used in Smart Farming
1.5 Importance of Smart Farming
1.6 Role of IoT in Advanced Farming Practices
1.7 Trends of Smart Farming
References
2 Overview of Robotics in Agriculture: Types and Applications
2.1 Introduction
2.2 Background
2.3 Types of Robotics in Agriculture
2.4 Applications of Robotics in Agriculture
2.5 Advantages of Robotics in Agriculture
2.6 Limitations of Robotics in Agriculture
2.7 Future of Robotics in Agriculture
2.8 Case Studies and Examples
2.9 Implications and Recommendations
2.10 Conclusion
References
3 Digital Farming: The New Era of Agriculture-Opportunities and Challenges
3.1 Introduction
3.2 Tools and Methods Used in Digital Farming
3.3 Pandemic Effects on Traditional Farming
3.4 New Scope at Digital Farming
3.5 Challenges or Difficulties for Digital Farming
3.6 Future Scope and Benefits
References
4 Challenges and Barriers to Smart Farming Adaptation: A Technical, Economic, and Social Perspective
4.1 Introduction
4.2 Technical Challenges in Smart Farming Adaptation
4.3 Economic Barriers to Smart Farming Implementation
4.4 Social Obstacles to Smart Farming Adoption
4.5 Environmental Considerations in Smart Farming
4.6 Future Prospects for Smart Farming
4.7 Conclusion and Recommendations
References
5 Sustainable Development in Agriculture: Soil Management
5.1 Introduction
5.2 Reviewing the Need for Global Food Production to be Upgraded
5.3 Soil Quality and Its Impact
5.4 Emerging Technologies
5.5 Introduction and Theory of IoT
5.6 Several Sensors and How They are Used in Agriculture
5.7 Centralized Agriculture System
5.8 Conclusion
References
6 Concepts of Robotics, AI, and Internet of Things (IoT) in Agriculture
6.1 Introduction
6.2 General Challenges Faced in the Agriculture Industry
6.3 Role of Robotics, AI, and IoT in Agriculture
6.4 Benefits of Robotic and AI in Improving Agriculture
6.5 Collecting Data and Performing Analyses With the Help of the Internet of Things
6.6 Relationships of IoT and AI Strengthen the Agriculture Sector
6.7 Benefits of IoT in Agriculture
6.8 The Role of AI in Tomorrow’s Farming
6.9 Robots in Agriculture—Perceptions and Pros, Cons
6.10 Complications Associated With Robots Used in Agriculture
6.11 Conclusion
References
7 Data Analytics in Agriculture: Predictive Models and Real-Time Decision-Making
7.1 Introduction
7.2 Data Collection and Management in Agriculture
7.3 Challenges in Collecting and Managing Agricultural Data
7.4 Strategies for Effective Data Collection and Management
7.5 Predictive Models in Agriculture
7.6 Applications of Predictive Models in Agriculture
7.7 Real-Time Decision-Making in Agriculture
7.8 Integration of Predictive Models and Real-Time Decision Making in Agriculture
7.9 Future of Data Analytics in Agriculture
7.10 Concluding Remarks
7.11 Future Directions for Research and Practice
References
8 Examining the Role of IoT and AI in Revolutionizing Agriculture: A Smart Farming Approach
8.1 Introduction
8.2 IoT in Agriculture
8.3 Major Advantages of Using IoT in Agriculture
8.4 Major Disadvantages of Using IoT in Agriculture
8.5 AI in Agriculture
8.6 Benefits of Drones in Agriculture
8.7 Main Part of AI in Agriculture
8.8 Effects of AI in Agriculture
8.9 Combination of AI and IoT
8.10 Automatic Irrigation
8.11 Crop Health Monitoring
8.12 Supply Yields to the Demanded Areas
8.13 Conclusion
References
9 Smart AgriTech: Sensors & Networks
9.1 Introduction
9.2 IoT in Agriculture
9.3 Utilization of Sensor-Based IoT Devices in the Agricultural Sector
9.4 Networks for IoT in Agriculture
9.5 Communication Protocols Used in Agriculture
9.6 Case Studies and Examples of IoT Applications in Agriculture
9.7 The Future of IoT in Agriculture
9.8 Conclusion
References
10 Internet of Things in Agriculture: Sensor, Network and Communication Protocol
10.1 Introduction
10.2 IoT in Agriculture
10.3 Physical Design of IoT Architecture
10.4 IoT Network Levels and Stages
10.5 Characteristics of IoT
10.6 IoT Enabling Technology
10.7 IoT Challenges
10.8 IoT Communication Protocol
10.9 Sensors for IoT-Based Smart Agriculture
10.10 Sensor Interfacing and Control
10.11 Conclusion
References
11 An Irrigation System: Design, Implementation, and Benefits
11.1 Introduction
11.2 Smart Irrigation Composition
11.3 Advantages of Intelligent Irrigation Systems
11.4 Irrigation Methods
11.5 Review on Smart Irrigation Systems
11.6 Smart-Based Irrigation Techniques for Urban and Rural Areas
11.7 Barriers of Smart Irrigation Systems
11.8 Conclusion
References
12 Implementation of Smart Irrigation Systems
12.1 Introduction
12.2 Literature Survey
12.3 Irrigation System Design
12.4 Irrigation System Implementation Benefits
12.5 Irrigation System Benefits
12.6 Results and Discussion
12.7 Conclusion
References
13 Smart Plant-Based Wastewater Treatment for Agricultural Irrigation Systems
13.1 Introduction
13.2 Water Quality Parameters for Agricultural Irrigation
13.3 Wetland Method for Wastewater Treatment
13.4 Importance of Artificial Intelligence in Wastewater Treatment
13.5 Economic Feasibility of Smart Plant-Based Treatment for Agricultural Irrigation
13.6 Challenges Associated with Artificial Intelligence Application in Wastewater Treatment
13.7 Conclusion
References
14 Drones in Agriculture: Mapping, Monitoring, and Decision-Making
14.1 Introduction
14.2 Opportunities
14.3 Drone Mapping for Transforming Agriculture
14.4 Significance of Drone Monitoring in Precision Agriculture
14.5 Leveraging Drone for Real-Time Decision-Making in Agriculture
14.6 Common Usages of Agricultural Drones
14.7 Challenges
14.8 Summary and Scope
14.9 Conclusion
References
15 Investigation of Banana Plant Disease Detection Using Transfer Learning
15.1 Introduction
15.2 Literature Survey
15.3 Transfer Learning
15.4 Methodology
15.5 Result and Discussion
15.6 Conclusion
References
16 Exploring Dataset for Apple Leaf Disease Detection: A Focus on Horticulture
16.1 Introduction
16.2 Literature Review
16.3 Apple Leaf Disease Detection Mechanism
16.4 Significance of Dataset
16.5 Discussion
16.6 Conclusion
References
17 Optimizing Crop Yield Prediction Using Machine Learning Algorithms
17.1 Introduction
17.2 Related Work
17.3 Methodology
17.4 Machine Learning Algorithms for Crop Yield Prediction
17.5 Experimental Results and Analysis
17.6 Optimization Techniques
17.7 Challenges and Future Directions
17.8 Conclusion
References
18 Analyzing Smart Farming Technologies: A Study on Indian Farmers’ Adoption Trends
18.1 Introduction
18.2 Research Methodology
18.3 Objectives
18.4 Literature Review
18.5 Data Analysis
18.6 Key Findings
18.7 Discussions and Recommendations
18.8 Conclusion
References
19 Leveraging Linear Regression Model to Address Food Insecurity in the United States: A Smart Agritech Approach
19.1 Introduction
19.2 Literature Review
19.3 Data Sources
19.4 Preprocessing
19.5 Data Dictionary
19.6 Exploratory Data Analysis
19.7 Feature Engineering
19.8 Predictions
19.9 Conclusion
References
About the Editors
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Remote sensor data studies for crop yield analysis [10].
Table 3.2 Assessment of vegetable prices during the pandemic between...
Table 3.3 Precision crop growing sensor models [1].
Table 3.4 Smart farming monitoring system services [1].
Chapter 7
Table 7.1 Challenges for collecting data and strategies.
Chapter 11
Table 11.1 Barriers of smart irrigation systems.
Chapter 12
Table 12.1 Comparison of IoT transmission technologies.
Table 12.2 Comparison of irrigation technologies.
Table 12.3 Descriptive statistics analysis results.
Chapter 13
Table 13.1 Water quality guidelines for agricultural crop irrigation [5].
Chapter 15
Table 15.1 Specification of hyper parameter.
Table 15.2 Collection of captured banana leaf images data.
Table 15.3 Performance comparison analysis for the pre-trained network models.
Table 15.4 Computational time for various models.
Chapter 16
Table 16.1 Exploration of existing apple leaf dataset.
Chapter 17
Table 17.1 Summary of analysis.
Chapter 18
Table 18.1 Analysis of variance between the variables.
Table 18.2 Tests of between-subjects effects.
Chapter 19
Table 19.1 List of datasets and their sources.
Table 19.2 Data dictionary used in the analysis.
Table 19.3 Average FI rate in different communities.
Table 19.4 VIF factor of selected features.
Table 19.5 Performance table for all the models.
Table 19.6 Highest coefficients alongside their corresponding features.
Table 19.7 Performance data.
Table 19.8 Comparison table for FI rate and predicted FI rate.
List of Illustrations
Chapter 1
Figure 1.1 Agricultural decision support system framework.
Figure 1.2 Obstacles that impede the adoption of smart agriculture technology.
Figure 1.3 A depiction of IoT applications for smart farming.
Figure 1.4 Implementing innovative technologies at various stages.
Figure 1.5 A smart farming network based on the Internet of Things (IoT).
Chapter 2
Figure 2.1 Ancient farming [6].
Figure 2.2 Tractor [8].
Figure 2.3 Reaper [9].
Figure 2.4 Drones [9].
Figure 2.5 Autonomous tractors [11].
Figure 2.6 Drones [11].
Figure 2.7 Robot arms [14].
Figure 2.8 Harvesting robots [15].
Figure 2.9 Milking robots [16].
Figure 2.10 Weeding robots [18].
Figure 2.11 Vision robotics [19].
Figure 2.12 Blue river technology [20].
Figure 2.13 Harvest automation [21].
Figure 2.14 Strawberry harvesting robot [22].
Figure 2.15 Farm Wise robots [23].
Figure 2.16 Naïo weeding and harvesting robot [24].
Chapter 3
Figure 3.1 (a) Manual process of sowing seeds. (b) Automated...
Figure 3.2 (a) Soybean yield field [2]. (b) Stages of production...
Figure 3.3 Pandemic’s effects on the vegetable supply chain and agricultural...
Figure 3.4 Architecture of precision farming [1].
Chapter 4
Figure 4.1 Different economic barriers in the implementation of smart farming.
Figure 4.2 Percentage cost division in the implementation of smart farming.
Chapter 5
Figure 5.1 Role of AI in modern agriculture and farming [30].
Figure 5.2 Layered architecture/ecosystem of agro-IoT [32].
Figure 5.3 Different sensors.
Chapter 7
Figure 7.1 Applications of predictive models in agriculture.
Figure 7.2 Benefits of integrating predictive models and real-time...
Figure 7.3 Challenges of integration.
Chapter 8
Figure 8.1 AI and IOT in agriculture [21].
Figure 8.2 Arduino board parts for sensor connections [2].
Figure 8.3 Column graph for decreased water usage in agriculture...
Figure 8.4 Bar graph for increase in agriculture using IOT sensors.
Figure 8.5 IOT sensors used for smart farming.
Figure 8.6 Crop diagnosis cycle using AI.
Figure 8.7 Task done by drone.
Figure 8.8 Pie chart of AI in agriculture.
Figure 8.9 Process of AI with IOT in an agricultural field.
Figure 8.10 Connection between a physical farm and a virtual farm.
Figure 8.11 IOT in agriculture market size, 2021 to 2030 (USD billion).
Chapter 9
Figure 9.1 IoT-based climate condition monitoring for agri-farm...
Figure 9.2 IoT-based soil pattern monitoring system...
Figure 9.3 IoT-enabled devices for irrigation monitoring (source: Bing).
Figure 9.4 IoT-enabled drones for smart agriculture (source: Kim
et al.
[60]).
Figure 9.5 IoT in greenhouse monitoring (source: Farooq
et al.
[14]).
Figure 9.6 IoT-enabled devices for livestock monitoring...
Figure 9.7 Application of IoT in different fields of agriculture...
Figure 9.8 Mobile apps used for smart agriculture (source: Farooq
et al.
[14]).
Figure 9.9 Diagram of an MQTT system in agriculture.
Chapter 10
Figure 10.1 Application of Internet of Things in the modern world.
Figure 10.2 Physical design architecture of Internet of Things.
Figure 10.3 Network-level and stages of IoT-based system.
Figure 10.4 Embedded system computing devices.
Figure 10.5 IoT communication protocol architecture. (a) MQTT, (b) CoAP...
Figure 10.6 Various types of sensors for IoT agriculture application.
Figure 10.7 Pin description of Arduino UNO Revision 3.
Figure 10.8 Interfacing of soil moisture sensor using Arduino UNO board.
Figure 10.9 Interfacing of PIR sensor for motion detection using...
Figure 10.10 Interfacing of light dependent resistor as automatic light control...
Figure 10.11 Interfacing of ultrasonic sensor for object detection using ...
Figure 10.12 Interfacing of gas sensor using Arduino Uno board.
Figure 10.13 Interfacing of TMP36 sensor for temperature measurement...
Chapter 11
Figure 11.1 Smart irrigation modules.
Figure 11.2 Types of sensors and controllers.
Figure 11.3 Soil moisture sensor ...
Figure 11.4 (a) Evaporation sensors...
Figure 11.5 Rain sensors ...
Figure 11.6 Wind speed sensors...
Figure 11.7 Climate-based controller...
Figure 11.8 Benefits of smart irrigation systems.
Figure 11.9 Irrigation methods.
Figure 11.10 Drip irrigation smart system...
Figure 11.11 Sprinkler irrigation system [9].
Figure 11.12 Tank irrigation...
Figure 11.13 Tube well irrigation...
Figure 11.14 Canal irrigation...
Figure 11.15 Block diagram of a solar panel pumping module.
Chapter 12
Figure 12.1 Irrigation system layers and components.
Figure 12.2 Flow chart of implementing the step of the auto-watering model.
Figure 12.3 Block diagram of the proposed irrigation system.
Figure 12.4 Soil moisture level in automatic and manual irrigation methods.
Chapter 14
Figure 14.1 NDVI range.
Figure 14.2 Plant health through light sensors.
Figure 14.3 Rice field drone mapping for NDVI determination.
Figure 14.4 Crop health monitoring.
Figure 14.5 Real-time livestock monitoring by drone.
Figure 14.6 Thermal cameras arm drone for cattle scouting.
Figure 14.7 Field surveying.
Figure 14.8 Seed planting.
Figure 14.9 Crop spraying.
Figure 14.10 Land management.
Figure 14.11 Biomass estimation.
Chapter 15
Figure 15.1 Sample input images from real-time data set for four...
Figure 15.2 Deep-learning-based overall framework for plant disease detection.
Figure 15.3 Data augmentation of banana leaf image.
Figure 15.4 Recognition accuracy and loss for VGG19.
Figure 15.5 Recognition accuracy and loss for Inception Net.
Figure 15.6 Recognition accuracy and loss for Mobile Net.
Figure 15.7 Recognition accuracy and loss for ResNet152.
Figure 15.8 Confusion matrix of models: (a) ResNet152 model...
Chapter 16
Figure 16.1 Various types of apple leaf disease.
Figure 16.2 Thresholding segmentation.
Figure 16.3 Edge-based segmentation.
Figure 16.4 Process of support vector machine.
Figure 16.5 Process of random forest machine learning.
Figure 16.6 Working of a convolutional neural network.
Figure 16.7 Comparison between different datasets with size and author.
Chapter 17
Figure 17.1 Model evaluation.
Figure 17.2 Relation between fertilizer usage and crop yield.
Figure 17.3 Crop yield distribution.
Figure 17.4 Comparison between different models.
Figure 17.5 Hyperparameter tuning.
Chapter 18
Figure 18.1 Means plots for (a) external funding ...
Chapter 19
Figure 19.1 Business dataset before cleaning.
Figure 19.2 Business dataset after cleaning.
Figure 19.3 Descriptive statistics of the distribution of each feature.
Figure 19.4 Boxplot graph for features with significant outliers.
Figure 19.5 Heat map for inspecting multicollinearity.
Figure 19.6 Line plot graph of FI rate, unemployment, and houseless rates...
Figure 19.7 Line plot graph of grocery, restaurants, and wholesalers, by year.
Figure 19.8 Bar chart of rent and meal cost, by year.
Figure 19.9 Line chart of rent and meal cost, by year.
Figure 19.10 Line chart of FI rate in different communities, by year.
Figure 19.11 Line chart of unemployment rate in different communities, by year.
Figure 19.12 Heat map for food insecurity rates in five counties.
Figure 19.13 Choropleth map for US unsheltered rate in 2010.
Figure 19.14 Choropleth map for US unsheltered rates in 2019.
Figure 19.15 Choropleth map for unemployment rate by county in 2019.
Figure 19.16 Choropleth map for unemployment rate by county in 2020.
Figure 19.17 Choropleth map for percent people of color by county...
Figure 19.18 Choropleth map for food insecurity rate for the year 2009.
Figure 19.19 Choropleth map for food insecurity rate for the year 2018.
Figure 19.20 Histogram for food insecurity distribution, by restaurant count.
Figure 19.21 Food insecurity distribution, by one-bedroom rent prices.
Figure 19.22 Histogram for FI distribution, by unemployment rate.
Figure 19.23 Histogram for FI distribution, by houselessness rate.
Figure 19.24 Histogram for FI distribution, by race.
Figure 19.25 Scatter plot for FI rate vs. unemployment rate.
Figure 19.26 Scatter plot for FI rate vs. rent.
Figure 19.27 Scatter plot for FI rate vs. black population.
Figure 19.28 Scatter plot for FI rate vs. log rent.
Figure 19.29 Scatter plot for FI rate vs. white population.
Figure 19.30 Boxplot for the features with the largest outliers.
Figure 19.31 Boxplot of features after the reduction.
Figure 19.32 Model predicting food insecurity rates of the year 2018.
Figure 19.33 Model predicting food insecurity rates of the year 2020.
Figure 19.34 Feeding America’s projected FI rate for the year 2020.
Guide
Cover
Table of Contents
Title Page
Copyright
Preface
Begin Reading
About the Editors
Index
Also of Interest
End User License Agreement
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Smart Agritech
Robotics, AI, and Internet of Things (IoT) in Agriculture
Edited by
Santosh Kumar Srivastava
Durgesh Srivastava
Korhan Cengiz
and
Pramod Gaur
This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 9781394302956
Front cover images supplied by Adobe FireflyCover design by Russell Richardson
Preface
Agriculture has always been a vital sector of the global economy, providing food and raw materials for industries and households. However, with the growing population, changing climate conditions, and limited resources, the agriculture sector is facing numerous challenges. To address these challenges, farmers and agricultural companies are turning to advanced technologies such as robotics, artificial intelligence (AI), and the Internet of things (IoT).
This book entitled “Smart Agritech: Robotics, AI, and Internet of Things (IoT) in Agriculture” aims to provide a comprehensive overview of the latest technological advances in agriculture, focusing on the use of these three cutting-edge technologies. The book will explore the potential benefits of these technologies in improving agricultural efficiency, productivity, and sustainability.
Furthermore, the book will be written in a clear and concise manner that is accessible to a wide range of readers, from farmers and agribusiness professionals to researchers and students. It will be an ideal resource for those who wish to understand the potential of smart agriculture techniques and how they can be implemented to improve farm productivity, sustainability, and profitability. Finally, this book will provide a unique perspective by including case studies from developing countries, demonstrating how smart agriculture techniques can be adapted and applied in a variety of contexts. This sets it apart from many other books that focus primarily on developed countries and their agricultural systems.
Chapter 1
This chapter discusses how advanced technologies such as sensors, drones, and IoT are utilized in Smart farming to boost agricultural efficiency, productivity, and sustainability while addressing global food demand and environmental issues. It facilitates data collection and analysis to make informed decisions, optimizing crop growth and resource management by monitoring factors like soil moisture, temperature, and pests. Precision agriculture, employing data-driven farming techniques, and the use of autonomous robots and drones for tasks are highlighted. The IoT plays a vital role in monitoring variables, aiding waste reduction and productivity enhancement. Smart farming’s benefits include increased yield, cost reduction, and improved sustainability, making it a crucial solution for future food demands amidst population growth.
Chapter 2
This chapter explores the diverse range of robotic technologies revolutionizing agricultural operations. From autonomous drones to robotic harvesters, it examines the various types and their applications across different stages of farming. By providing insights into the integration of robotics, this chapter aims to showcase the potential benefits for farmers, including increased efficiency, reduced labor costs, and sustainable practices. Ultimately, this overview serves as a gateway to understanding how robotics is reshaping the landscape of agriculture worldwide.
Chapter 3
This chapter explores how digital innovations are reshaping traditional farming practices, presenting both opportunities and challenges. By embracing digital tools such as IoT, AI, and data analytics, farmers can enhance productivity, efficiency, and sustainability. However, this digital revolution also poses challenges such as data security, access, and the digital divide in rural areas. Through insightful analysis and case studies, this chapter navigates the complexities of digital farming, offering a comprehensive understanding of its potential and hurdles.
Chapter 4
This chapter explores the hurdles faced by farmers as they navigate technical complexities, economic constraints, and social factors in adopting smart farming practices. Through an in-depth examination, we aim to shed light on the intricate challenges inhibiting the widespread adoption of these transformative technologies in agriculture. By analyzing the interplay between technology, economics, and society, this chapter offers valuable insights into overcoming barriers and fostering the successful integration of smart farming solutions. Our exploration underscores the importance of understanding and addressing the diverse challenges encountered on the path toward sustainable and efficient agricultural practices.
Chapter 5
This chapter explores innovative strategies and technologies aimed at preserving soil health while maximizing productivity. By addressing soil degradation and nutrient depletion, we aim to foster resilient farming practices in the face of environmental challenges. Through a comprehensive examination of soil management techniques, we seek to empower farmers with the knowledge and tools needed to cultivate healthy soils and secure food production for future generations. Join us on a journey to unlock the potential of sustainable soil management in shaping the future of agriculture.
Chapter 6
This chapter explores the transformative potential of robotics, AI, and IoT in agriculture. We delve into how these cutting-edge technologies are revolutionizing traditional farming practices. From autonomous robots to AI-driven decision-making, uncover the ways in which agriculture is evolving. The integration of IoT devices offers real-time monitoring and data analysis for optimized crop management. Join us on a journey to discover the conceivable advancements reshaping the landscape of modern agriculture.
Chapter 7
This chapter delves into the transformative potential of data analytics within agriculture, focusing on predictive models and real-time decision-making. It explores how harnessing data can revolutionize farming practices, enhancing efficiency and sustainability. By employing advanced analytics techniques, farmers can make informed decisions based on real-time insights, optimizing crop yield and resource allocation. This chapter discusses the integration of data-driven approaches into traditional farming methods, highlighting the benefits of predictive modeling for anticipating crop performance and mitigating risks. Through case studies and examples, it illustrates the practical applications of data analytics in modern agriculture, paving the way for a more resilient and productive food system.
Chapter 8
This chapter explores the transformative impact of IoT and AI on modern agriculture, focusing on smart farming approaches. By examining the integration of advanced technologies like sensors, drones, and AI, it explores how they enhance efficiency, productivity, and sustainability in farming practices. Through data-driven insights and informed decision-making, smart farming optimizes crop growth and resource utilization while addressing global food demand and environmental concerns. This exploration underscores the pivotal role of precision agriculture and autonomous systems in revolutionizing traditional farming methods. Ultimately, this chapter offers a comprehensive analysis of how IoT and AI are reshaping the agricultural landscape, paving the way for a more sustainable and productive future.
Chapter 9
This chapter provides a comprehensive examination of how IoT is transforming agriculture, revolutionizing farming practices through the interconnection of physical devices via the internet. It delves into the applications of IoT in agriculture, showcasing its advantages such as heightened efficiency, reduced waste, and increased crop yields, while also addressing challenges like infrastructure costs and rural connectivity limitations. Emphasizing the pivotal role of sensor-based IoT, the chapter illustrates how sensors collect crucial data on crops, soil, weather, and livestock, thereby optimizing agricultural techniques. It also highlights the significance of selecting appropriate network and communication protocols tailored to diverse farm locations. Through real-world case studies spanning countries like India, Thailand, China, Malaysia, the Philippines, and the United States, the chapter vividly demonstrates IoT’s transformative influence on farming practices worldwide.
Chapter 10
This chapter delves into the transformative role of the Internet of things (IoT) in modern agriculture, focusing on sensors, networks, and communication protocols. It explores how IoT technologies revolutionize farming practices, offering insights into enhanced productivity, resource optimization, and sustainability. Through an examination of sensor deployment, network infrastructure, and communication protocols, we uncover the intricate web of interconnected systems driving agricultural innovation. This chapter provides a comprehensive overview of the IoT’s integration into agriculture, offering practical insights for farmers, researchers, and industry professionals. By understanding the dynamics of IoT in agriculture, readers gain valuable perspectives on the future of farming and its pivotal role in addressing global food security challenges.
Chapter 11
This chapter offers comprehensive insights into the design and implementation of irrigation systems, highlighting their significance in agricultural settings. Readers will explore various irrigation techniques, their practical applications, and the benefits they bring to crop cultivation. From traditional methods to cutting-edge technologies, this chapter provides a holistic view of irrigation systems’ evolution and their role in enhancing agricultural productivity. Whether you are a seasoned farmer or a curious enthusiast, this chapter aims to broaden your understanding of irrigation systems and their invaluable contributions to sustainable agriculture.
Chapter 12
This chapter introduces a real-time monitoring and auto-watering system employing IoT technology and predictive mathematical models to optimize crop growth by regulating water usage efficiently. The system, which monitors soil moisture levels and automatically irrigates crops, conserves water and promotes interoperability among various sensing data streams for comprehensive agricultural analytics. With water consumption reduced by over 75% and enhanced irrigation efficiency, the system proves invaluable, especially in remote and desert regions facing water and labor shortages. It not only addresses water scarcity but also fosters sustainable farming practices and economic growth in the agricultural sector through improved production and lowered maintenance costs. This proposed system stands as a sustainable and effective solution, ensuring a brighter future for smart agriculture.
Chapter 13
This chapter explains the innovative approach of utilizing plant-based wastewater treatment systems for agricultural irrigation. By focusing on smart technologies and sustainable practices, this explores how these systems offer a viable solution to water scarcity and pollution in agricultural settings. Through a combination of scientific research and practical applications, we aim to provide insights into the potential benefits and challenges of implementing such systems. From enhancing water quality to promoting eco-friendly agricultural practices, this chapter offers a comprehensive exploration of smart plant-based wastewater treatment methods. Ultimately, the goal is to contribute to the advancement of sustainable agricultural practices through the integration of innovative wastewater treatment solutions.
Chapter 14
This paper introduces the transformative role of drones in modern farming practices. The chapter delves into how drones revolutionize agriculture through precise mapping, real-time monitoring, and data-driven decision-making. It highlights the increasing significance of drone technology in enhancing crop management, resource utilization, and overall farm efficiency. Through this exploration, readers gain insights into the evolving landscape of agricultural innovation and the potential of drones to address pressing challenges in food production and sustainability. This preface sets the stage for an in-depth examination of the multifaceted applications and benefits of drones in shaping the future of agriculture.
Chapter 15
This chapter explains an innovative approach: leveraging transfer learning to identify banana plant diseases. By harnessing the power of deep learning models, we aim to revolutionize disease detection in one of the world’s most vital crops. Through meticulous investigation and experimentation, this chapter elucidates the potential of transfer learning in bolstering agricultural resilience. This chapter is dedicated to the investigation of banana plant disease detection through the lens of transfer learning.
Chapter 16
This chapter explains the meticulous examination of a dataset tailored for the identification of apple leaf diseases, illuminating its potential horticultural significance. Through the scrutiny of images depicting various afflictions like apple scab, cedar apple rust, and powdery mildew, this research endeavors to unlock insights vital for detecting and managing diseased foliage. Employing an array of computer vision techniques and algorithms, the dataset undergoes thorough evaluation to unveil pertinent characteristics and patterns crucial for leaf damage detection. The anticipated outcome of this endeavor is the development of an automated system poised to offer farmers and agronomists a reliable tool for real-time disease surveillance and management in orchards. Ultimately, the fruits of this research endeavor aspire to bolster the productivity and sustainability of the apple industry, fostering benefits for producers and consumers alike.
Chapter 17
This chapter delves into the realm of enhancing the accuracy and efficiency of machine learning algorithms for predicting crop yields. Through meticulous research, we explore the effectiveness of various methods including regression models, ensemble techniques, deep learning models, and support vector machines. Employing rigorous metrics such as MAE, RMSE, R2, precision, and recall, we meticulously evaluate these models against a meticulously curated dataset of agricultural parameters. Leveraging the “crop yield prediction dataset” alongside techniques like feature engineering and selection, we refine our models’ predictive capabilities. Our findings underscore the superiority of ensemble methods, deep learning models, and other advanced algorithms over traditional approaches, shedding light on critical factors influencing crop yield. The study emphasizes the significance of comprehensible models, laying the groundwork for future advancements in crop yield prediction and ultimately empowering farmers, agricultural planners, and policymakers to make informed decisions and foster sustainable agricultural practices.
Chapter 18
This chapter delves into the intricate world of smart farming technologies, examining their adoption trends among Indian farmers. As technology continues to reshape agricultural landscapes, understanding the dynamics of adoption becomes imperative. Through meticulous analysis and research, we aim to shed light on the factors influencing Indian farmers’ embrace of these innovations. From IoT sensors to AI-driven solutions, each technology holds the promise of revolutionizing traditional farming practices. Our exploration aims to provide valuable insights into the challenges and opportunities surrounding the integration of smart farming technologies in India.
Chapter 19
In the realm of tackling societal challenges, few issues are as pressing as food insecurity. This chapter delves into a pioneering application of the linear regression model, merging it with cutting-edge agritech solutions to confront this issue head on. By leveraging data-driven insights and technological innovations, we embark on a journey to transform the landscape of food access in the United States. This interdisciplinary approach bridges the gap between traditional agriculture and modern data science, offering a promising avenue for sustainable change. Join us as we explore the potential of smart agritech in addressing one of humanity’s most fundamental needs.
1Introduction to Smart Farming: Definition, Importance and Trends
Manoj Kumar Mahto1*, Santosh Kumar Srivastava2 and Basant Sah3
1Department of Computer Science & Engineering, Vignan Institute of Technology and Science, Deshmukhi, Hyderabad, India
2Department of CSE (AIML), GL Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, India
3KLEF, Vijayawada, India
Abstract
Smart farming is a cutting-edge agricultural approach that integrates advanced technologies to enhance efficiency, productivity, and sustainability. This method addresses the rising global demand for food due to population growth while aiming to conserve natural resources and mitigate climate change impacts. Utilizing technologies such as sensors, drones, robots, precision agriculture, and the Internet of Things (IoT), smart farming collects and analyses data to make informed decisions and optimize agricultural processes. By monitoring factors like soil moisture, temperature, nutrients, and pests, smart farming empowers farmers to manage crop growth more effectively. This technology-driven approach reduces input costs, boosts yields, and minimizes environmental impacts, presenting a sustainable alternative to traditional farming practices. With the global population projected to reach 9.7 billion by 2050, a 70% increase in food production will be necessary. Traditional methods, which often lead to soil degradation, water pollution, and biodiversity loss, are insufficient to meet this demand. Smart farming offers a viable solution by enabling higher food production with fewer resources. Precision agriculture is a significant aspect of smart farming, involving the collection of data on soil, crops, and weather through sensors, drones, and satellite imagery. This data is used to create detailed field maps, allowing for tailored farming techniques for specific zones. Another critical component is the use of autonomous robots and drones, which can automate labour-intensive tasks such as planting, harvesting, and monitoring crops. These technologies operate around the clock, providing real-time data and enabling rapid responses to changing conditions. The IoT plays a crucial role in smart farming by using sensors and devices to gather data, which is then analyzed centrally. This application includes monitoring soil moisture and temperature, managing irrigation systems, and optimizing equipment use. IoT integration helps farmers refine their practices and reduce waste, contributing to greater sustainability.
Keywords: Smart farming, sustainability, climate change, sensors, drones, Internet of Things (IoT), data analysis, crop management
1.1 Introduction
Smart farming is an innovative method of agriculture that utilizes the latest technologies to enhance the effectiveness and eco-friendliness of farming techniques. Traditional farming has always been a labor-intensive and time-consuming process, with farmers relying on experience and intuition to make decisions about planting, irrigation, fertilization, and pest control. However, the advent of new technologies has enabled farmers to gather and analyze vast amounts of data to make more informed decisions about their farming practices.
Smart agriculture encompasses the utilization of cutting-edge technologies such as the Internet of Things (IoT), machine learning, artificial intelligence (AI), and big data analytics to supervise and control agricultural activities. The term “Internet of Things” (IoT) denotes the networking of devices, sensors, and machines, which collect and exchange information through the internet. In agriculture, the IoT can be utilized to observe environmental variables like soil moisture, temperature, humidity, and other factors, offering farmers real-time insights into crop conditions. Utilizing big data analytics allows for the examination of vast volumes of data amassed through the IoT, empowering farmers to discern trends and patterns in areas such as crop development, weather fluctuations, and market dynamics. By implementing AI and machine learning algorithms, it is possible to predict crop yields, detect pests and diseases, and improve irrigation and fertilization techniques.
Smart farming has the potential to transform agriculture, offering numerous benefits to farmers, consumers, and the environment. Smart farming can enhance agricultural productivity and profitability by optimizing farming techniques, resulting in increased yields and reduced costs, while simultaneously reducing waste and enhancing crop quality. For consumers, this can mean a more stable and affordable food supply, as well as healthier and more sustainable food choices. Smart farming can have positive environmental impacts, including decreased usage of pesticides and fertilizers, water conservation, and mitigation of greenhouse gas emissions.
One of the key aspects of smart farming is precision agriculture, which involves using data-driven insights to optimize farming practices. Precision agriculture enables farmers to make informed decisions about when, where, and how to plant crops, as well as how much water and fertilizer to use. By adopting this method, it is possible to decrease waste and enhance yields, all while minimizing the environmental impact.
Vertical farming, an essential component of smart farming, entails cultivating crops in vertically stacked layers under controlled environments using artificial lighting. Vertical farming is a technique that can optimize the utilization of scarce space and resources, minimize water consumption, and reduce reliance on pesticides and herbicides. This approach can be particularly beneficial in urban areas, where space is at a premium and there is a growing demand for locally grown produce.
Smart farming is not without its challenges, however. Small- and medium-sized farmers may encounter a significant obstacle in adopting new technologies due to the high costs involved in their implementation. Furthermore, an imperative exists for increased education and training to facilitate farmers in comprehending the proficient utilization of these novel technologies. Furthermore, there are apprehensions about the security and confidentiality of data, along with the possibility of technology exacerbating current economic and social disparities.
Despite encountering numerous obstacles, smart farming has the capability to revolutionize agriculture by offering a more sustainable, lucrative, and effective approach to farming. As technology continues to advance and become more accessible, we can expect to see more farmers adopting smart farming practices in the years to come.
1.2 Smart Farming
In the past, agriculture was primarily focused on producing food on cultivated lands to ensure human survival and breeding animals. This era, referred to as traditional agriculture 1.0, heavily relied on manpower and animals. Basic farming tools, including sickles and shovels, were utilized to carry out farming activities. The majority of work was done through manual labor, resulting in low levels of productivity, as illustrated in Figure 1.1.
The advent of steam engines in the 19th century introduced novel machinery to the agricultural sector, which signified the commencement of the second agricultural era. During this era, farmers extensively employed agricultural equipment and chemicals, leading to significant enhancements in the efficiency and productivity of farms. Despite this, the overuse of chemicals had negative consequences, including chemical contamination, ecological deterioration, depletion of natural resources, and increased energy consumption.
The 20th century marked the advent of Agricultural era 3.0, driven by the swift progress in computation and electronics. This period marked the advent of programmed agricultural machinery, robotic techniques, and other innovative technologies that revolutionized agricultural processes, resulting in a significant improvement in efficiency. Policies were implemented to tackle the problems that arose during Agricultural era 2.0, emphasizing measures such as workload distribution, precise irrigation, decreased chemical usage, effective pest control technologies and location-specific nutrient application.
Agriculture 4.0, the prevailing phase of agriculture, is distinguished by the seamless amalgamation of state-of-the-art technologies, including the Internet of Things (IoT), big data analytics, cloud computing and remote sensing artificial intelligence. Utilizing inexpensive network and sensor platforms, the adoption of these cutting-edge technologies has led to noteworthy improvements in agricultural techniques, which have optimized production efficiency and reduced water and energy consumption, all while minimizing environmental impact [1]. Within intelligent agriculture, big data furnishes instant insights into agricultural scenarios, empowering farmers to make well-informed choices. Simultaneously, real-time programming rooted in artificial intelligence is ingrained within IoT devices, aiding farmers in arriving at suitable decisions [2].
Figure 1.1 Agricultural decision support system framework.
Advanced technology is utilized in smart farming to facilitate precision agriculture, enabling farmers to monitor their crops remotely. The automation of sensors and machinery, through this method, has transformed agricultural processes, elevating efficiency in areas such as harvesting and crop yields, resulting in a more productive farming workforce [3]. The fusion of technology has instigated a technological revolution in agriculture, replacing conventional farming methods with automated devices. The arrival of the Internet of Things has revolutionized traditional techniques and substantially transformed contemporary farming practices [4].
Throughout the early phases of the crop’s growth cycle, sensors diligently observe the crops to identify any unfavorable conditions with greater accuracy. Intelligent tools are employed throughout the entirety of the farming process, spanning from planting and cultivating crops to harvesting, storing, and transporting them. The efficient and profitable operation of the entire process is attributed to the precise use of various sensors that enable accurate monitoring capabilities. Moreover, online-available sensors that quickly gather data can offer location-specific and crop-specific insights for further evaluation in precision agriculture.
The agricultural industry has undergone a revolutionary transformation through smart agriculture technology, as it offers effective solutions to a myriad of crop production challenges. By employing sophisticated technologies like the Internet of Things (IoT), sensors, GPS, robotics, and data analytics, smart agriculture allows farmers to closely monitor their crops and make informed decisions. This approach optimizes spatial management practices, reduces excessive use of fertilizers and pesticides, and ultimately enhances crop production. An example of smart farming is the use of artificial neural network (ANN) models to manage irrigation water through a system called smart irrigation water management (SIWM). SIWM delivers up-to-the-minute data on irrigation effectiveness of water supply and demand, water productivity metrics to facilitate irrigation scheduling and decision-making. Climate-smart agriculture (CSA) is gaining popularity in developing countries due to its potential to enhance food security, bolster farming system resilience, and reduce greenhouse gas emissions. With the integration of Internet of Things (IoT) technology, smart agriculture enables real-time monitoring and data collection for various agricultural activities, such as irrigation, plant protection, fertilization, and disease forecasting. This combination holds promise for sustainable and efficient agricultural practices in the face of climate challenges. The employment of this technology offers numerous advantages, such as precise assessments of crops and soil, remote monitoring capabilities for farmers, effective utilization of natural resources like water, improved agricultural and livestock production, and real-time data collection on crops. Smart agriculture is an advanced form of precision agriculture that employs sophisticated technologies to gather diverse data on farming practices and allow remote management. It also incorporates efficient real-time maintenance solutions into its operations.
1.3 Internet of Things
Smart farming has emerged as a result of the introduction of a new technology through the Internet of Things (IoT), which enables remote device connectivity [5]. This innovation has gradually impacted numerous industries, such as healthcare, trade, energy, communications, and agriculture, by enhancing their proficiency and performance transversely diverse sectors.
Information on the current impact and potential future applications of IoT technology in farming is available, but as technology continues to advance, its role is expected to become increasingly important. This entails employing communication infrastructure, sensors, and intelligent devices, acquiring data, utilizing cloud-based intelligent information, leveraging mobile devices, and automating agricultural processes. All of these elements work together to facilitate decision-making based on data-driven insights (as illustrated in Figure 1.2).
By utilizing IoT technology, farmers can remotely monitor plants and animals, as well as retrieve information from mobile devices. Through the application of sensors and instruments, farmers can assess weather conditions and predict production levels. The Internet of Things (IoT) assumes a vital function in water management, overseeing and controlling water flow, gauging crop water needs, and refining water utilization [6]. By means of cloud connectivity and sensors, the condition and supply of water can be monitored from a distance, aligned with the demands of the soil and plants. While it is impossible for farmers to manually monitor every plant for nutrient deficiencies, pests, and diseases, IoT technology has allowed farmers to achieve a new level of precision in modern agriculture [7].
Figure 1.2 Obstacles that impede the adoption of smart agriculture technology.
The agricultural industry has been transformed by the widespread adoption of IoT technologies, which have greatly impacted the communication infrastructure. The agricultural sector has benefited from the use of smart devices, remote data collection, and the integration of sensors and vehicles with mobile devices and the internet. In addition, a range of capabilities, including cloud-based intelligent analysis, mechanization of various farming operations, interface development and decision-making have been integrated. These advancements have enabled resource optimization, climate impact control, and increased crop yields, making a significant impact on the industry.
Researchers have proposed a range of methods, systems, and tools to monitor and transmit crop data during different growth stages, tailored to specific crop and field characteristics. Manufacturers provide an assortment of communication devices, including robots, sensors, drones, and heavy machinery, designed for data collection and sharing purposes. In order to guarantee food and environmental safety, food and agriculture organizations, in conjunction with governmental bodies, have established policies and guidelines to govern the application of these technologies [8].
1.3.1 Fundamentals of IoT Applications in Smart Farming
An affordable and user-friendly tracking platform has been developed to offer comprehensive information on conventional farming practices, methods, tools, crop diseases and pests, etc. This information is sourced from various channels to support sustainable agriculture. Interactive agriculture enables users to conveniently access the data via multiple devices, including computers and mobile phones [9].
Robust models: The agricultural industry is distinct in its range of unique attributes, which include complexity, diversity, uncertainties and spatio-temporal variability surrounding the most effective harvesting methods and facilities.
Scalability: Achieving scalability of results is vital, particularly considering the vast differences in farm sizes. As such, planning and testing must be incrementally increased while minimizing costs.
Affordability: Ensuring affordability is crucial for the success of farming endeavours, and pricing must be reasonable to provide significant support. Employing standardized platforms, facilities, products and tools can aid in achieving equitable pricing.
Sustainability: Ensuring sustainability is a crucial concern given the intense economic pressures and fierce competition present global.
1.4 Technologies Used in Smart Farming
1.4.1 Global Positioning System (GPS)
Accordingly, GPS technology is capable of precisely recording latitude, longitude, and elevation data. By using signals transmitted from global positioning system satellites, GPS receivers can determine their position in real-time and continuously track their position while in motion. This information provides farmers with the ability to identify the exact location of field data, including pest occurrences, soil types, weeds, and other obstacles. With this system, farmers can attain accurate application of inputs, such as fertilizer, pesticide, seed, herbicide, and water, in precise areas of their fields, thereby enhancing resource utilization and efficiency [10].
1.4.2 Sensor Technologies
To determine soil texture and structure, nutrient levels, vegetation, temperature, humidity, air, vapor, and other related factors, various techniques such as photoelectricity, electromagnetics, conductivity, and ultrasound are employed. Remote sensing data facilitates the distinguishing of diverse crop varieties, categorization of pests and weeds, recognition of zones with soil and plant stress, and surveillance of drought conditions [11].
Taking into account elements like soil moisture, precipitation, nutrient concentrations, light availability, humidity, and leaf color is essential to guarantee the vitality and well-being of plants. A pragmatic method for monitoring plant health is to optimize temperature and low-intensity lighting while preserving energy and water via micro-irrigation. Specific sensors are employed to identify particular parameters, and when any of these parameters surpass a predetermined threshold, the sensors detect the variation and transmit the data to the microcontroller. The microcontroller then executes appropriate actions until the constraint returns to its optimal level [12].
Sensors are widely used in agriculture to collect information on various factors such as humidity, moisture, temperature, pressure, airflow, light, soil quality, location, and CO2 levels. Wireless sensors are widely utilized to gather data on crop conditions and other pertinent details due to their essential characteristics such as memory, coverage, portability, reliability, durability, and computational efficiency, which make them well suited for agricultural purposes. These sensors can either operate independently or integrate with sophisticated agricultural equipment and heavy machinery, depending on specific application requirements.
1.4.3 Variable-Rate of Technology (VRT) and Grid Soil Sampling
In agriculture, variable-rate technologies (VRT) utilize GIS-generated maps to forecast input delivery rates, ensuring that inputs are distributed at varying levels, at the right times, and in the correct locations. The systematic method of gathering soil samples and generating a map for each soil parameter is achieved through soil sampling in a grid pattern. A variable-rate applicator uses these maps as a foundation and uploads them for utilization in variable rate technology (VRT). With the aid of a computer and GPS receiver, the amount of fertilizer product or delivery is regulated and controlled according to the characteristics of the map [13].
Incorporating innovative technologies such as grid soil sampling and variable rate technology can enhance soil fertility management and evaluate nutrient distribution and yields. The process of grid sampling entails dividing a field into smaller cells or areas using overlaid grid lines and gathering composite samples from each area at the intersections. Interpolation methods are used to estimate soil-test values for unsampled locations based on grid sampling. This helps to account for field-specific variability of nutrients such as phosphorus and potassium, allowing for customized fertilizer and manure applications that enhance precision agriculture and nutrient management practices [14].
1.4.4 Geographic Information System (GIS)
The GIS is a system with hardware and software that compiles, stores, retrieves, and analyzes location and attribute data, generates maps and statistics to facilitate spatial analyses, and identifies the relationship between various factors that influence crop production in a given field. Beyond its functions in data storage and visualization, GIS is employed to assess various management approaches by integrating and modifying layers of data, contributing to informed decision-making [15].
1.4.5 Crop Management
By utilizing satellite imagery, farmers can obtain data on soil condition and crop performance changes that are attributable to topography in a field. This empowers them to carefully oversee factors that impact production, such as seeds, fertilizers, and pesticides, resulting in higher yields and efficiency.
Satellite imagery enables farmers to obtain real-time information on crop performance and soil conditions at a regional scale. Leveraging the reflective characteristics of vegetation, especially in red and near-infrared spectra, vegetation indices offer a means to predict the connection between crop biomass and yield experiments. The normalized difference vegetation index (NDVI) stands as the most widely employed metric to assess crop health and productivity, given its close association with leaf area index (LAI) and photosynthetic activity. Remote-sensing-derived indicators are interpreted and compared to previous or normal seasons in crop monitoring methods. Early crop yield valuation can be accomplished by examining the connection between vegetation indices and biomass before harvest. Automated field management comprises a range of functions, from basic tasks like crop production (yields), monitoring profit and loss, and forecasting weather to more complex responsibilities such as field mapping and soil nutrient monitoring [16].
1.4.6 Soil and Plant Sensors
New sensors have been designed with distinctive characteristics that distinguish them from existing sensors, as precision agriculture depends heavily on sensor technology to deliver data on soil properties, water status, and fertility [17].
Precision agriculture entails an agricultural methodology that leverages information technology to enhance both crop yield and quality, all the while minimizing resources like water, fertilizers, and pesticides. A vital element of precision agriculture lies in the real-time tracking of physical and chemical indicators within soil and plants, empowering farmers with informed insights to make strategic choices in crop management. By utilizing insights derived from data pertaining to soil attributes, fertility, and water conditions, farmers can fine-tune crop growth conditions, alleviate both biotic and abiotic stresses, and ultimately enhance yields. New sensors with enhanced features have been developed to improve monitoring effectiveness. These sensors, including optoelectronic, acoustic, nanostructured biosensors and impedance, can be used to detect pests. Furthermore, sensors for soil apparent electrical conductivity (ECa) can continually gather field surface data by penetrating deviations in soil texture and brininess. By leveraging insights obtained from data on soil properties, water status and fertility farmers can optimize crop growth conditions, mitigate biotic, increase yields, and abiotic stresses.
1.4.7 Rate Controllers
Rate controllers are used in a variety of applications, including agriculture, construction, and mining. In agriculture, they are used to apply fertilizers, pesticides, and herbicides at the correct rate. In construction, they are used to apply concrete, asphalt, and other materials at the correct rate. In mining, they are used to load and unload trucks and other vehicles at the correct rate.
