1. -- Application of drone and sensors in advanced farming: the future smart farming technology -- 2. -- Development and research of a greenhouse monitoring system -- 3. -- A cloud-computing model for implementing smart agriculture -- 4. -- Application of conversational artificial intelligence for farmer's advisory and communication -- 5. -- The use of an intelligent fuzzy logic controller to predict the global warming effect on agriculture: the case of the chickpea cicer arietinum L. -- 6. -- Using machine learning algorithms for mapping soil macronutrient elements variability with digital environmental data in an alluvial plain -- 7. -- A smart IoT framework for soil fertility enhancement assisted via deep neural networks -- 8. -- Plant disease detection with the help of advanced imaging sensors -- 9. -- Artificial intelligence-aided phenomics in high throughput stress phenotyping of plants -- 10. -- Plant disease detection using hybrid deep learning architecture in smart agriculture application -- 11. -- Classification of coffee leaf diseases through image processing techniques -- 12. -- The use of artificial intelligence to model oil extraction yields from seeds and nuts -- 13. -- Applications of artificial intelligence in pest management -- 14. -- Applying clustering technique for rainfall received by different district of Maharashtra state -- 15. -- Predicting rainfall for Aurangabad division of Maharashtra by applying auto-regressive moving average model (ARIMA) using Python programming.

An essential resource work for understanding how to design and develop smart applications for present and future problems of 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 provide powerful solutions to real-world problems. Smart applications have become commonplace. 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 facing 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 variables for agricultural applications. Researchers, public and private sector representatives, agriculture scientists, and students can use this book to develop sustainable and 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.

OCLC-licensed vendor bibliographic record.