NextGen Strategies and Tools

NextGen Strategies and Tools: Pioneering Innovations in Agriculture is a systematic, application-oriented examination of next-generation strategies and technologies that drive innovation in agriculture. The book focuses on integrating Artificial Intelligence (AI), Internet of Things (IoT), deep learning, robotics, and smart sensing technologies to address challenges in sustainability, precision farming, and agricultural productivity.

The book presents information on AI-based decision support frameworks, IoT-enabled precision agriculture, smart sensor networks for crop monitoring, and vision-based systems for disease and pest detection. Contributions explore deep learning and computer vision models applied to real-world agricultural scenarios, emphasizing scalability, accuracy, and deployment feasibility in both large-scale and resource-constrained environments. Practical implementations such as Agrobots, Raspberry Pi-based agricultural platforms, and automated fertilizer dispensing systems further demonstrate the applicability of the proposed methods.

In addition to technological advancements, the book examines the sustainability and environmental implications of adopting AI and IoT in agriculture. Key considerations such as efficient resource utilization, climate resilience, and environmentally responsible farming practices are discussed to provide a balanced and holistic perspective. By bridging theoretical foundations with practical deployment, the book supports the development of intelligent agricultural ecosystems.

As part of the NextGen Agriculture: Novel Concepts and Innovative Strategies series, this book is intended for postgraduate students, researchers, academicians, and professionals in computer science, AI, data science, electronics and communication engineering, agricultural engineering, and smart farming technologies. It also serves as a reference for policymakers and practitioners seeking to advance sustainable and intelligent agricultural systems.

Dr. Dhakshayani J is currently working as an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Information Technology Kottayam, Kerala. She completed her Ph.D. in Computer Science and Engineering from the National Institute of Technology Puducherry and obtained her Master’s degree from Pondicherry University, Puducherry. Her research domain focuses on precision agriculture, with a strong emphasis on applying image processing and computer vision techniques to real-world agricultural challenges. Her work spans crop disease detection using visual analytics, automated plant trait analysis, and high-throughput phenotyping through multimodal data fusion. She is currently leading an ongoing ANRF-sponsored research project titled “AI-Driven Multimodal Vision System for Rubber Tapping Optimization Using RGB, Thermal, and 3D Imaging in Kerala Plantations,” which focuses on integrating multiple visual modalities to enhance tapping efficiency, plant health assessment, and decision-making in rubber plantations. In addition to her research activities, Dr. Dhakshayani J serves as an active reviewer for several reputed international journals, including Scientific Reports, Wiley, Hindawi, and several Springer and IEEE international conferences.

Dr. Shashidhar R received his Bachelor of Engineering and Master of Technology degrees from Visvesvaraya Technological University (VTU), Belagavi, and his Ph.D. from JSS Science and Technology University (JSS STU), Mysuru. He is currently working as an Associate Professor in the Department of Electronics and Communication Engineering and serves as the Convener of the Institute Innovation Council at JSS Science and Technology University, Mysuru. He has published over 60 papers in Scopus- and SCI-indexed journals and has edited three Scopus-indexed books. He is an editorial board member of Scientific Reports (Nature Publishing Group) and Discover Imaging (Springer Nature).He has filed nine patents, of which seven have been granted and two are under final examination. He received the Best Paper Award for the paper titled “Music Emotion Recognition Using Convolutional Neural Network for Regional Languages” at AIKIIE-2023, Ballari. He was awarded Best Paper Presenter for the paper “Optimizing EMG Signal Analysis with Advanced Machine Learning Techniques” at ICSTEMSD-2025. He also received the Best Paper Award for “Speech Emotion Recognition Based on Speaker and Gender Dependences in Cross-Linguistic” at CCICT-2025. He received the Protsahan: Recognition of Research Publications Award from the IEEE Communications Society, Bangalore Section, during January 2020–September 2021 and October 2021–September 2022. He is an Executive Committee Member of the IEEE Bangalore Section and serves as the Secretary of the IEEE Mysuru Subsection. He has served in various roles in IEEE international conferences, including Technical Program Committee Chair at IEEE MysuruCon 2023 and ICETEG-2025, and Track Chair at IEEE MysuruCon 2022 and INDISCON 2023. He has over 1,000 Google Scholar citations, with an h-index of 17 and an i10-index of 27. His research interests include speech processing, biomedical signal and image processing, embedded systems, artificial intelligence, machine learning, deep learning, and quantum machine learning.

Dr. Vinayakumar Ravi (https://vinayakumarr.github.io/) is an Assistant Research Professor at Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia. His previous position was a Postdoctoral research fellow in developing and implementing novel computational and machine learning algorithms and applications for big data integration and data mining with Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. His current research interests include applications of data mining, Artificial Intelligence, machine learning (including deep learning) for biomedical informatics, Cyber Security, image processing, and natural language processing. He has more than 100 research publications in reputed IEEE conferences, IEEE Transactions and Journals. His publications include prestigious conferences in the area of Cyber Security, like IEEE S&P and IEEE Infocom. Dr. Ravi has received a full scholarship to attend Machine Learning Summer School (MLSS) 2019, London. He has organized a shared task on detecting malicious domain names (DMD 2018) as part of SSCC'18 and ICACCI'18. He received the Chancellor's Research Excellence Award in AIRA 2021 and his name was included in the World's Top 2% Scientists by Stanford University published in PLoS Biology.

Dr Roopashree S, currently an Associate Professor in the Department of Computer Science and Engineering, is a highly motivated and accomplished professional with over 16 years of experience across academia and industry. With 9 years of specialized research expertise in the area of machine learning, computer vision, deep learning and Generative AI. She has achieved significant milestones in her academic journey. Dr Roopashree is a dedicated researcher in the fields of Artificial Intelligence, Computer Vision, Machine Learning, Generative AI, Large Language Models, Small Language Models and Image Processing. She has an impressive track record, including the publication of four Indian patents and the grant of a German utility patent and two Australian design patents. As a Senior Member of IEEE and a lifetime member of the Computer Society of India, Dr Roopashree has made significant contributions to both research and academia. She has published 35+ research articles indexed in Web of Science, SCIE, and Scopus Journals/conferences. She is an active reviewer for leading SCIE journals. Her achievements include working on a funded multi-agent framework project on Indian medicinal plants, serving as an R&D consultant to start-ups, receiving sponsorship from the Karnataka State Council for Science and Technology (KSCST) for three projects, and securing first place in the IEEE Humanitarian Projects Proposal competition in 2020.

Dr. Deepti Gupta is an Assistant Professor at Texas A&M University-Central Texas. After receiving her PhD, she joined Goldman Sachs as a Cloud Security Architect. She also worked as a faculty member in the Department of Computer Science at Huston-Tillotson University, Austin. She received her Ph.D. degree in Computer Science from the University of Texas at San Antonio (UTSA) and also received her M.S. degree in Computer Science from UTSA. She has worked as an Adjunct Faculty in the Department of Computer Science at St. Edward University, Austin. Dr. Gupta’s research interests lie in the areas of security and privacy in the Internet of Things (IoT) leveraging cloud and edge computing. Her research interests also include the application of AI and Machine Learning to secure IoT and CPS infrastructures in various application domains, such as smart healthcare, wearable IoT and smart home. She is also interested in designing federated learning algorithms to deal with non-IID data using game theory. She also developed novel anomaly detection models and fine-grained access control models to develop secure infrastructure for IoT. She has several conference and journal publications, and also continually serves as an expert reviewer for various journals and technical program committees for several conferences and workshops. Dr. Gupta has received National Science Foundation (NSF) award. She is an active team member of IEEE ComSoc Young Professionals, AnitaB.org, WiCyS, and also co-chair of the N2Women fellowship.