The book is a comprehensive, application-oriented book that examines the growing role of Artificial Intelligence (AI), Machine Learning, Deep Learning, IoT, remote sensing, sensor networks, predictive analytics, image processing, drones and smart automation in modern horticulture.

The book brings together emerging AI-based approaches for improving the productivity, quality, sustainability and resource-use efficiency of horticultural production systems. It progresses from foundational and analytical concepts to practical applications covering crop monitoring, irrigation, disease and pest management, yield prediction, post-harvest operations, supply chains, greenhouse automation and climate-smart horticulture.

A major focus of the book is the application of Machine Learning and Deep Learning to horticultural data and images. The chapters explore how AI can analyse crop and environmental data, recognize visual symptoms, classify pests and diseases, estimate yields and support timely farm-management decisions.

The book also highlights the integration of AI with IoT, sensors, remote sensing and drones. These technologies enable real-time crop-health monitoring, precision irrigation, automated spraying, greenhouse control and data-driven management of horticultural crops.

Particular attention is given to plant disease detection, early pest identification and integrated pest management, demonstrating how predictive and image-based AI systems can support early intervention and reduce crop losses. Post-harvest applications are also addressed through AI-based quality assessment and grading of fruits and vegetables, while AI-driven cold-chain optimization extends the discussion to horticultural supply systems.

The book further explores smart greenhouse automation and the use of AI for climate-smart horticulture, emphasizing the potential of intelligent technologies to help horticultural systems adapt to changing environmental conditions.

Major Areas Covered

• Future trends and research directions in AI-based horticulture

• Machine Learning for horticultural data analysis and decision-making

• Deep Learning architectures for image-based applications

• Remote sensing and IoT-based crop-health monitoring

• Precision irrigation using Machine Learning and sensor networks

• Predictive modelling for horticultural yield estimation

• AI-based plant disease detection

• Early pest detection and classification

• AI-enabled Integrated Pest Management

• Post-harvest quality assessment and grading

• AI-based cold-chain optimization

• Drone-based crop monitoring and spraying

• Smart greenhouse automation

• AI for climate-smart and sustainable horticulture

The book will be useful for students, teachers, researchers and professionals in horticulture, agriculture, agricultural engineering, plant pathology, entomology, precision agriculture and allied sciences, as well as researchers and practitioners working in AI, Machine Learning, IoT, remote sensing, computer vision and smart farming technologies.

Overall, the book presents a technology-driven perspective on the future of horticulture, demonstrating how AI can move horticultural production from conventional management toward precision, predictive, automated and climate-resilient systems.

Dr. P. R. Anisha is an Associate Professor in the Department of Computer Science &  Engineering at Stanley College of Engineering and Technology for Women, Hyderabad, with over nine years of teaching and research experience. She holds a PhD from K L University and has published more than 35 research papers in reputed international journals and conferences. Her research interests include Artificial Intelligence, Machine Learning, Image Processing, IoT, and data-driven healthcare. She has served as a Special Session Chair at various national and international conferences and is an active member of professional bodies such as ACM and IAENG. She has also co-authored books on C and C++ programming and is recognized as a motivational speaker, contributing significantly to academic and professional communities.

Mrs. Qamer Fatima, received her M.Tech degree in Computer Science and Engineering, from Osmania University. She is currently working as an Assistant Professor in the Department of Computer Science at Stanley College of Engineering and Technology for Women, Hyderabad, India. She has over 12 years of teaching experience and works in the areas of Artificial Intelligence, Machine Learning, Deep Learning, and Healthcare Applications. She has 1 journal paper (under publication), 4 international conference publications, and 6 book chapters in reputed indexed volumes. She is also a Research Intern at the Centre for Image and Vision Computing (CIVC), Multimedia University, Malaysia, and serves as a Technical Program Committee (TPC) member for CSNT 2026 (IEEE).

Dr. C Kishor Kumar Reddy, currently working as Professor, Dept. of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Hyderabad, India. He has research and teaching experience of more than 12 years. He has published more than230+ research papers in National and International Conferences, Book Chapters, and Journals indexed by Scopus and others. He is an author for 2 text books and 35+ edited books. He is a member of ISTE, CSI, IAENG, UACEE, IACSIT. His research areas include: Bioinformatics, Neuroscience, Remote Sensing, Deep Learning, Intelligent Systems.

Mrs. T. Monika Singh received her MTech degree in Computer Science and Engineering Department from Osmania University. Currently Pursuing Ph.D. in IT Department in Annamalai University. From 2017 she is working as faculty in Computer Science and Engineering Department at Stanley College of Engineering and Technology for Women. She possesses extensive experience in both teaching and research. She is certified as a project-based learning mentor by Wipro and holds a Talent Next certification in Java Full Stack. Her research interest area is Machine Learning. She has various paper publications in national and international journals. In addition to her credits, she also has a patent in her credits

Dr. Jyothi Paranthaman is an accomplished academic and researcher currently serving as a faculty member in the Faculty of Engineering and Technology at Botho University. With a strong background in engineering and applied sciences, her areas of expertise include smart technologies, computational modeling, and interdisciplinary applications in engineering education. She has contributed to several research projects, academic publications, and curriculum development initiatives aimed at integrating technology-driven solutions into teaching and real-world problem-solving. Dr. Paranthaman is committed to fostering innovation and excellence in engineering education across emerging domains.

Chapter 1. Future Trends and Research Directions in AI-Based Horticultural Systems
Vasanth P, Yasodha M, Nithiyasri S, Archana Anokhe, Krishna Priyan Ra K and Megha R Nair

Chapter 2. Machine Learning Techniques for Horticultural Data Analysis and Decision
Tekuri Vamsi Krishna, Angajala Sai Subhiksh, V Hanisha, S Sai Kiran and Polimera Lakshmi Bhanu

Chapter 3. Deep Learning Architectures for Image-Based Horticultural Applications
Shugufta Fatima, Harika Koormala, Ayesha Siddiqua and Srinath Doss

Chapter 4. AI-Based Crop Health Monitoring Using Remote Sensing and IoT Technologies
Anil Patel, Rudra Pratap Singh, Ankit Rai, Swati Medha and Pradeep Kumar Dalal

Chapter 5. Precision Irrigation Management Using Machine Learning and Sensor Networks
Sirigineni Sai Kiran, Tekuri Vamsi Krishna, Angajala Sai Subhiksh, V. Hanisha, Polimera Lakshmi Bhanu and Chetan Kumar Chowdam

Chapter 6. Predictive Modelling for Yield Estimation in Horticultural Crops
Gangadaran Muhilan, Keerthivasan Ragupathi, Sherly J, Sangeeth Shyam Sundar S S, Selvaprabu Palanivel and Virata G

Chapter 7. AI-Based Plant Disease Detection Using Image Processing and Deep Learning
Shugufta Fatima, Lasya Vedula, Suresh Kumar Badhagouni and Srinath Doss

Chapter 8. Early Pest Detection and Classification Using Artificial Intelligence Techniques
Angajala Sai Subhiksh, V. Hanisha, Tekuri Vamsi Krishna, Sirigineni Sai Kiran and Nithin Reddy Gangireddy

Chapter 9. Integrated Pest Management Using AI and Predictive Analytics
Muhammad Usama, Qudrat Ullah, Waqas Haider, Muhammad Ali Amir, Irfan Haidri, Muhammad Qasim, Mujahid Farid and Tahira Yasmeen

Chapter 10. AI-Based Post-Harvest Quality Assessment and Grading of Fruits and Vegetables
Rishi Kant

Chapter 11. Cold Chain Optimization Using Artificial Intelligence in Horticultural Supply
S. R. Hemalatha, M. Ganesh, Harinath Nagineni and Patnool Mohammed Muzamil Basha

Chapter 12. AI-Powered Drones for Crop Monitoring and Spraying Applications
Muhammad Usama, Qudrat Ullah, Waqas Haider, Muhammad Ali Amir, Irfan Haidri, Muhammad Qasim, Mujahid Farid and Tahira Yasmeen

Chapter 13. Smart Greenhouse Automation Using AI and IoT Integration
Keerthivasan Ragupathi and Gangadaran Muhilan

Chapter 14. AI for Climate-Smart Horticulture: Adapting to Environmental Changes
Rishi Kant