Editors: S. Gowrishankar, Hamidah Ibrahim , A. Veena, K.P. Asha Rani , A.H. Srinivasa

Data Science for Agricultural Innovation and Productivity

eBook: US $49 Special Offer (PDF + Printed Copy): US $95
Printed Copy: US $70
Library License: US $196
ISBN: 978-981-5196-18-4 (Print)
ISBN: 978-981-5196-17-7 (Online)
Year of Publication: 2024
DOI: 10.2174/97898151961771240101


Data Science for Agricultural Innovation and Productivity explores the transformation of agriculture through data-driven practices. This comprehensive book delves into the intersection of data science and farming, offering insights into the potential of big data analytics, machine learning, and IoT integration.

Readers will find a wide range of topics covered in 10 chapters, including smart farming, AI applications, hydroponics, and robotics. Expert contributors, including researchers, practitioners, and academics in the fields of data science and agriculture, share their knowledge to provide readers with up-to-date insights and practical applications. The interdisciplinary emphasis of the book gives a well-rounded view of the subject.

With real-world examples and case studies, this book demonstrates how data science is being successfully applied in agriculture, inspiring readers to explore new possibilities and contribute to the ongoing transformation of the agricultural sector. Sustainability and future outlook are the key themes, as the book explores how data science can promote environmentally conscious agricultural practices while addressing global food security concerns.

Key Features:
  • - Focus on data-driven agricultural practices
  • - Comprehensive coverage of modern farming topics with an interdisciplinary perspective
  • - Expert insights
  • - Sustainability and future outlook
  • - Highlights practical applications

Data Science for Agricultural Innovation and Productivity is an essential resource for researchers, data scientists, farmers, agricultural technologists, students, educators, and anyone with an interest in the future of farming through data-driven agriculture.


Researchers, data scientists, farmers, agricultural technologists, students, educators, and general readers.


Agriculture can provide for one of humanity's essential needs, which is food. Around the globe, agriculture is a source of employment besides providing for humankind's basic requirements. Agriculture now entails significantly more than merely planting a seed, raising a cow, or capturing a fish. To feed a huge population, an entire environment and a spate of individuals must work together. Innovation enables us to achieve more and better with less. Innovation is driven not just by technology breakthroughs, but also by creative methods of organising farmers and linking them to the information they want. Many smallholder farmers throughout the world continue to cultivate in the same manner that their forefathers did hundreds of years ago. Traditional farming methods may continue to be effective for some, but new tactics may assist many in significantly improving yields, soil quality, and natural capital, as well as food and nutrition security. Farmers can be grouped in novel ways to ensure that information reaches them more readily and efficiently. The kind and style of the extension itself have altered significantly throughout time. For example, developments in satellite mapping and information and communication technologies (ICTs) are already altering more traditional agricultural extension activities. Farming is getting more accurate and productive as a result.

The number of farm records in electronic format is growing daily, and informatics and data analysis are essential to analyse these huge records with diverse datasets.

This book reveals the use of different sensors to collect diverse farm data, which comprise chemical/pesticide tracking, harvest and yield records, planting records, shipping records, labor tracking, weather data, etc. Sustainable Agriculture focuses on meeting current requirements without compromising the ability of future generations to meet their own. An agriculture system that enables farms of all sizes to be profitable and contribute to their local economies is one that is both economically and socially sustainable. A system like this would prioritise people and communities over corporate interests, support the next generation of farmers, provide everyone with access to healthy food, and support the next generation of farmers. Because of the widely varying scales, dimensions, and volumes of electronic farm data, multidisciplinary solutions are needed for visual depiction and digital characterisation. Gains in agriculture have improved thanks to developments in machine learning. By offering detailed advice and insights about the crops, machine learning is a current technology that helps farmers reduce farming losses.

Farmers, vendors, theoreticians and engineers have used various software applications and servers for these uses. Because of problems with disease identification, a lack of interoperability brought on by vendor-locked agriculture systems, and security/privacy concerns regarding data storage, sharing, and usage, the agriculture domain is well known for suffering from heterogeneous and uneven data, delayed farm communications, and disparate work flow tools.

The material in the book is presented in a way to encourage researchers to think and indicate concepts that are introduced, which can solve real-world problems in the agricultural domain. Research and extension are crucial to innovation pathways. The contents of this book focus on,

Smart Farming - The future of agricultural technology is big data collection and analysis in agriculture to improve operational efficiency and reduce labour expenses. Based on a more precise and resource-efficient strategy, smart farming has the potential to offer more productive and sustainable agricultural output. IoT has encouraged the assumption that a smart network of sensors, actuators, cameras, robots, drones, and other connected devices would provide agriculture with new levels of control and automated decision-making, allowing for a lasting ecosystem of innovation.

Artificial Intelligence - It is progressively growing as a component of the agricultural industry's technological growth. It is playing a critical role in the agriculture sector and is altering the industry. AI protects the agriculture sector against a variety of threats, including climate change, population expansion, labour shortages, and food safety. The goal is to boost global food production and will not only help farmers improve efficiency, but they will also increase crop quantity and quality and ensure crops reach the market faster.

Machine Learning - With the emergence of IoT and other technologies, a fertile ground for real-time monitoring has emerged. Machine learning solutions in agriculture rely on real-time data to provide farmers with exponential advantages. AI and machine learning are powerful catalysts for improving remote facility security, yields, and pesticide efficacy.

Deep Learning- It is a relatively new, innovative approach for image processing and data analysis, with promising results and enormous potential. Deep learning has recently entered the agricultural sector after being effectively employed in other disciplines.

Hydroponics system - It is the cultivation of plants in a controlled setting. While indoor farming is not a new phenomenon, hydroponic farming, a more recent discovery, simplifies the growing process even more by eliminating all unneeded components of traditional farming. Small farmers, amateurs, and business companies all employ hydroponic production systems.

Robotics in Agriculture - Robotics will undoubtedly bring about an agricultural revolution. Although the road ahead is not very smooth, we must assess the feasibility, sustainability, and efficiency of providing the world's food demands. However, it will be exciting to observe how farmers, agribusinessmen, and consumers will use the potential of robotics and digital-mechanization to define the future of this sector.

Internet of Green Things - Green IoT is an evolution of IoT that reduces emissions and pollution through many elements while also having low operational costs and power usage. Green IoT is the future, especially as the world seeks new methods to combat climate change; this new domain offers several chances for enterprises.

Crop health monitoring - It encompasses the monitoring of several factors, such as temperature, humidity, precipitation, insect intrusion, and seed and soil quality, allowing for improved crop quality and health decisions. This requires rapid involvement of farm managers in cases of emergency, such as overnight freezing or pest incursions, even from remote areas where they are not accessible on-site.

Application of sensors in agriculture - Given the current circumstances and their adverse influence on traditional farming techniques, agriculture must be carried out more wisely, utilising innovative and cutting-edge technologies. It is the only method to give a solution and fulfil the world's population's infinite and expanding requirements. Farmers may now remotely record their crops and monitor their efficacy, manage agricultural pests, and take quick action to safeguard their crops from environmental threats by using smart sensors in agriculture.

Climate Adaptation Strategies - Climate change, which primarily affects hydro-meteorological threats, is a fact that is influencing the planet in a variety of ways. It manifests itself in a variety of ways, including a rise in the frequency and severity of floods, droughts, and high temperatures. Climate change has caused droughts, other extreme weather occurrences, and meteorological disasters in many nations in recent years. Effective management of climate change-induced difficulties causes localised techniques that may differ from one region of the world to the next, and even within a single country.

Web 3.0 for Farming- Web3, the third generation of the internet and simple video-based technologies in local languages, has the potential to transform agriculture. It refers to initiatives taken to develop a decentralised form of the internet based on blockchain technology and on user ownership, which has the potential to reverse old data paradigms and return power to farmers.

The precise focus of this handbook will be on the potential applications and use of data informatics in the area of the agriculture domain.

S. Gowrishankar
Department of Computer Science and Engineering
Dr. Ambedkar Institute of Technology
Bengaluru, Karnataka 560056

Hamidah Ibrahim
Department of Computer Science
Faculty of Computer Science and Information Technology
Universiti Putra
43400, Selangor, Malaysia

A. Veena
Department of Computer Science and Engineering
Dr. Ambedkar Institute of Technology
Bengaluru, Karnataka 560056

K. P. Asha Rani
Department of Computer Science and Engineering
Dr. Ambedkar Institute of Technology
Bengaluru, Karnataka 560056


A. H. Srinivasa
Department of Computer Science and Engineering
Dr. Ambedkar Institute of Technology
Bengaluru, Karnataka 560056