Table Of Content

Artificial Intelligence and Smart Agriculture Applications An essential resource work for understanding how to design and develop smart applications for present and future problems in the field of agriculture. – Dr. Deepak Gupta, Maharaja Agrasen Institute of Technology, Delhi, India. As a result of the advances in Artificial Intelligence (AI), many aspects of daily life have been transformed by smart digital technology. Advanced intelligent algorithms can pro- vide powerful solutions to real-world problems. Smart applications have become com- monplace. All areas of life are being changed by smart tools developed to deal with complex issues challenging both humanity and the earth. Artificial Intelligence and Smart Agriculture Applications presents the latest smart agriculture applications developed across the globe. It covers a broad array of solutions using data science and AI to attack problems faced by agriculture worldwide. Features: ◾ Application of drones and sensors in advanced farming ◾ A cloud-computing model for implementing smart agriculture ◾ Conversational AI for farmer’s advisory communications ◾ Intelligent fuzzy logic to predict global warming’s effect on agriculture ◾ Machine learning algorithms for mapping soil macronutrient elements variability ◾ A smart IoT framework for soil fertility enhancement ◾ AI applications in pest management ◾ A model using Python for predicting rainfall The book examines not only present solutions but also potential future outcomes. It looks at the role of AI-based algorithms and the almost infinite combinations of vari- ables for agricultural applications. Researchers, public and private sector representatives, agriculture scientists, and students can use this book to develop sustainable solutions for smart agriculture. This book’s findings are especially important as the planet is facing unprecedented environmental challenges from over-farming and climate change due to global warming. Artificial Intelligence and Smart Agriculture Applications Edited by Utku Kose, V.B. Surya Prasath, M. Rubaiyat Hossain Mondal, Prajoy Podder, and Subrato Bharati First edition published 2023 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2023 selection and editorial matter, Utku Kose, V.B. Surya Prasath, M. Rubaiyat Hossain Mondal, Prajoy Podder, and Subrato Bharati; individual chapters, the contributors Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. 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Contents Foreword ................................................................................................................vii Preface .....................................................................................................................ix Acknowledgments ..................................................................................................xi Editors ...................................................................................................................xiii Contributors ........................................................................................................xvii 1 Application of Drones and Sensors in Advanced Farming: The Future Smart Farming Technology .......................................................1 KUMAR CHIRANJEEB, RAJANI SHANDILYA, AND KALI CHARAN RATH 2 Development and Research of a Greenhouse Monitoring System ..............................................................................31 MURAT KUNELBAYEV AND AMANTUR UMAROV 3 A Cloud-Computing Model for Implementing Smart Agriculture ...........47 M. ZHOU AND C. MATSIKA 4 Application of Conversational Artificial Intelligence for Farmer’s Advisory and Communication ........................................63 ANURAG SINHA AND DEN WHILREX GARCIA 5 The Use of an Intelligent Fuzzy Logic Controller to Predict the Global Warming Effect on Agriculture: Case of Chickpea (Cicer arietinum L.) ............................................................................87 H. CHEKENBAH, IMANE EL HASSANI, S. EL FATEHI, Y. HMIMSA, M. L. KERKEB, AND R. LASRI 6 Using Machine Learning Algorithms to Mapping of the Soil Macronutrient Elements Variability with Digital Environmental Data in an Alluvial Plain ..........................................107 FUAT KAYA AND LEVENT BAŞAYIĞIT v vi ◾ Contents 7 A Smart IoT Framework for Soil Fertility Enhancement Assisted via Deep Neural Networks ..................................................137 SANNIDHAN MANJAYA SHETTY, JASON ELROY MARTIS, AND SUDEEPA KEREGADDE BALAKRISHNA 8 Plant Disease Detection with the Help of Advanced Imaging Sensors ................................................................................163 SHIVAM SINGH, RAINA BAJPAI, MD. MAHTAB RASHID, BASAVARAJ TELI, AND GAGAN KUMAR 9 Artificial Intelligence-Aided Phenomics in High-Throughput Stress Phenotyping of Plants .............................................................185 DEBADATTA PANDA, M. KUMAR, L. MAHALINGAM, AND M. RAVEENDRAN 10 Plant Disease Detection Using Hybrid Deep Learning Architecture in Smart Agriculture Applications ...............................213 MURUGAN SUBRAMANIAN, NELSON IRUTHAYANATHAN, ANNADURAI CHINNAMUTHU, NIRMALA DEVI KATHAMUTHU, MANIKANDAN RAMACHANDRAN, AND AMBESHWAR KUMAR 11 Classification of Coffee Leaf Diseases through Image Processing Techniques .......................................................................233 ALI HAKAN IŞIK AND ÖMER CAN ESKICIOGLU 12 The Use of Artificial Intelligence to Model Oil Extraction Yields from Seeds and Nuts ...............................................................253 CHINEDU M. AGU, CHARLES C. ORAKWUE, AND ALBERT C. AGULANNA 13 Applications of Artificial Intelligence in Pest Management ..............277 MUHAMMAD KASHIF HANIF, SHOUKET ZAMAN KHAN, AND MARIA BIBI 14 Applying Clustering Technique for Rainfall Received by Different Districts of Maharashtra State ......................................301 NITIN JAGLAL UNTWAL 15 Predicting Rainfall for Aurangabad Division of Maharashtra by Applying Auto-Regressive Moving Average Model (ARIMA) Using Python Programming .............................................313 NITIN JAGLAL UNTWAL Index ...........................................................................................................327 Foreword As a result of innovative movements by the Artificial Intelligence, the field of agriculture has been open for many smart applications enabling the humankind to find effective solutions for the faced problems. As it is too critical to derive care- ful combinations of Artificial Intelligence techniques, both software and hardware components have been started to be used actively after the start of the 21st century. That is also a cause of the high momentum of hardware resources, which resulted in the speedy advances in software technology. Now, the 2000s is the era of smart technologies, triggering rise of physical technologies in the context of wearables, Internet of Things, and many other alternative technological components of practical life. Among all fields of life, agriculture has a unique role as it touches both human and the earth. Although it is not a new trend to use technology in agricultural applications, developing smart tools is a very recent research way to find out new solutions or derive improved solutions against the known findings. So, smart applications have been an active scientific focus to combine foundations of both Artificial Intelligence and agricultural research. As the target components are often physical objects in agricultural fields, there has been a great interest to design smart hardware-oriented applications but the background of all applications employs an active use of ana- lytic data processing steps and Artificial Intelligence models to give necessary pre- dictive or descriptive outputs. Use of Artificial Intelligence models often includes Machine/Deep Learning, rule-based inferencing roads, or intelligent optimization functions. Out of all these efforts, the target outcomes have been in the context of sustainable, effective, and efficient agriculture for the well-being of humankind and the earth. Connected with the mentioned points, this edited book project, Artificial Intelligence and Smart Agriculture Applications, provides the most recent research outcomes for meeting with the aims on well-being. I found that book as a com- prehensive view for different problem sides of smart applications. In detail, the book combines a total of fifteen chapters with a unique use of control models and Machine/Deep Learning techniques, by also covering important supportive data processing methods such as image processing. Some hot topics like plant diseases, global warming, agricultural monitoring, and sustainability are among target research interests in the related chapters. vii viii ◾ Foreword I believe that the readers will find that book as an essential resource work for understanding how to design and develop smart applications for present and future problems in the field of agriculture. In order to have an enjoyable mixture of knowledge and practice, I would like to suggest the readers to have a look to another book, Artificial Intelligence and Smart Agriculture Technology (CRC Press), by the same editors. While this book considers more about application side with findings and experiences, the technology-focused book gives detailed information about the literature and the background knowledge. The best way to have better advantages of both books may be choosing the starting point: theoretical or practical side. But the most important thing is to gather both sides somehow for a better knowledge- ability status for smart agricultural research. I would like to send my warm congratulations and thanks to the editors: Dr. Kose, Dr. Prasath, Dr. Mondal, Dr. Podder, and Dr. Bharati for their timely book contribution to the associated literature. While reading more about the theoretical side, it may be often possible to miss to learn about application experiences. So, this book will be a good supportive work for a wide audience, including researchers, experts, professionals, and even degree students. In order to leave a better, green world for the new generations, it is important to use smart technology. So, I invite all readers to spend their time to turn the pages and gain the necessary practical knowledge for smart agriculture applications. For a green future! Assist. Prof. Dr. Deepak Gupta Maharaja Agrasen Institute of Technology (MAIT), Dept. of Computer Science and Engineering, Delhi, India Preface Current technological era has caused the word of ‘smart’ to be a generic adjective for defining use of Artificial Intelligence algorithms in technological solutions. As a result of the unstoppable wind of the Artificial Intelligence, technological aspects of the daily life were transformed into an innovative version where digital outcomes of different human actions have been started to be used by advanced algorithms, which are capable of giving accurate solutions for real-world problems. So, rise of the smart applications has been a common thing for especially last decade. Eventually, all fields are currently highly enrolled in the use of smart tools for their problems and agriculture takes its place within first priorities, as it affects the way of both earth and humanity. This edited book employs the latest reports regarding international outcomes of smart agriculture applications. Titled as Artificial Intelligence and Smart Agriculture Applications, it provides a mixture of smart applications, considering different aspects of using Data Science and Artificial Intelligence combination for different problems of agriculture. As it is known, current developments in smart technology area require an optimum use of software and hardware components, as the Data Science and the latest formations of Artificial Intelligence and additional technologies (i.e. use of Deep Learning and image processing or running optimum predic- tive Machine Learning through Internet of Things – IoT – ecosystem) find their best performances in that near relation. So, we have done our best to gather the most competitive research in a total of fifteen chapters. In detail, the first chapter focuses on the future of smart agriculture, by discussing about drone technology. The second chapter provides information about design and development of a greenhouse monitoring solution. Next, the third chapter is based on a critical component: cloud computing to build up smart agriculture applications. The fourth chapter ensures research for the farmers and considers the use of Artificial Intelligence for advisory and communication purposes. The fifth chapter follows a very critical topic: global warming and ensures design of fuzzy controller to predict the effects of global warming. After that, the sixth chapter focuses on using Machine Learning for mapping of the soil macronutrient elements variability. The seventh chapter is based more on IoT use as building a framework for soil fertility enhancement through Deep Learning. The eighth and the ninth chapters give their ix