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Precision Agriculture Basics
With the growing popularity and availability of precision equipment, farmers and producers have access to more data than ever before. With proper implementation, precision agriculture management can improve profitability and sustainability of production. Precision Agriculture Basics is geared at students, crop consultants, farmers, extension workers, and practitioners that are interested in practical applications of site-specific agricultural management. Using a multidisciplinary approach, readers are taught to make data-driven on-farm decisions using the most current knowledge and tools in crop science, agricultural engineering, and geostatistics. Precision Agriculture Basics also features a stunning video glossary including interviews with agronomists on the job and in the field.
Practical Mathematics for Precision Farming
As the public and producers becomes more aware of the environmental and economic benefits of precision farming, there has been increased demand for quality training to accurately evaluate spatial variability within fields. Practical Mathematics in Precision Farming provides hand-on training and examples for certified crop consultants (CCAs), farmers, crop consultants, and students (both undergraduate and graduate) on how to conduct to conduct and analyze on-farm studies, write simple programs, use precision techniques to scout for pests and collect soil samples, develop management zones, determine the cost of production, assess the environmental consequences of precision techniques, understand soil test results, and develop site-specific nutrient and plant population algorithms. Using real agronomic examples, the reader is taught the crucial task of managing products and inputs for application at the right rate, place, and time.
Sensing Approaches for Precision Agriculture
Sensing Approaches for Precision Agriculture aims to bring together the ‘state of the art’ of the most popular sensing techniques and the current state of research on the application of sensors in Precision Agriculture (PA). Sensing is of great value in PA because it provides cheap and immediate data for management. This book gives a broad overview of sensing in PA and a coherent introduction for new professionals and research scientists. Readers are introduced to the potential applications of a range of different sensors, how they should be used properly and their limitations for use in PA. Chapters on specific topics and case studies provide depth and enable implementation of the methods by users. A general introduction about sensing techniques in PA is followed by Chapters 2–9 on the most important specific techniques in sensing and Chapters 10–13 include mini-case studies, each showing cutting-edge applications for different sensing methods. Finally, there is an Epilogue on how we expect sensors and analysis to develop.
Precision Agriculture for Sustainability and Environmental Protection
Precision agriculture (PA) involves the application of technologies and agronomic principles to manage spatial and temporal variation associated with all aspects of agricultural production in order to improve crop performance and environmental quality. The focus of this book is to introduce a non-specialist audience to the the role of PA in food security, environmental protection, and sustainable use of natural resources, as well as its economic benefits. The technologies covered include yield monitors and remote sensing, and the key agronomic principles addressed are the optimal delivery of fertilizers, water and pesticides to crops only when and where these are required. As a result, it is...
Engineering Mathematics and Artificial Intelligence
Explains the theory behind Machine Learning and highlights how Mathematics can be used in Artificial Intelligence Illustrates how to improve existing algorithms by using advanced mathematics and discusses how Machine Learning can support mathematical modeling Captures how to simulate data by means of artificial neural networks and offers cutting-edge Artificial Intelligence technologies Emphasizes the classification of algorithms, optimization methods, and statistical techniques Explores future integration between Machine Learning and complex mathematical techniques
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