Modern Techniques for Agricultural Disease Management and Crop Yield Prediction

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Since agriculture is one of the key parameters in assessing the gross domestic product (GDP) of any country, it has become crucial to transition from traditional agricultural practices to smart agriculture. New agricultural technologies provide numerous opportunities to maximize crop yield by recognizing and analyzing diseases and other natural variables that may affect it. Therefore, it is necessary to understand how computer-assisted technologies can best be utilized and adopted in the conversion to smart agriculture. Modern Techniques for Agricultural Disease Management and Crop Yield Prediction is an essential publication that widens the spectrum of computational methods that can aid in agriculture disease management, weed detection, and crop yield prediction. Featuring coverage on a wide range of topics such as soil and crop sensors, swarm robotics, and weed detection, this book is ideally designed for environmentalists, farmers, botanists, agricultural engineers, computer engineers, scientists, researchers, practitioners, and students seeking current research on technology and techniques for agricultural diseases and predictive trends.

Author(s): N. Pradeep, Sandeep Kautish, C.R. Nirmala
Series: Advances in Environmental Engineering and Green Technologies
Publisher: Engineering Science Reference
Year: 2019

Language: English
Pages: 316
City: Hershey

Cover
Title Page
Copyright Page
Book Series
Editorial Advisory Board
Table of Contents
Detailed Table of Contents
Foreword
Preface
Acknowledgment
Chapter 1: Issues and Challenges in Smart Farming for Sustainable Agriculture
Chapter 2: Image Processing Techniques Aiding Smart Agriculture
Chapter 3: Expert System Design for Diagnosis of Diseases for Paddy Crop
Chapter 4: Deep Learning and Computer Vision in Smart Agriculture
Chapter 5: Computer Vision for Green Plant Segmentation and Leaf Count
Chapter 6: Automatic Data Acquisition and Spot Disease Identification System in Plants Pathology Domain
Chapter 7: Applications of Data Mining Techniques in Smart Farming for Sustainable Agriculture
Chapter 8: Agro Guardian
Chapter 9: A Study on Technology-LED Solutions for Fruit Grading to Address Post-Harvest Handling Issues of Horticultural Crops
Chapter 10: Optimized Data Mining Techniques for Outlier Detection, Removal, and Management Zone Delineation for Yield Prediction
Compilation of References
About the Contributors
Index