Modern Techniques for Agricultural Disease Management and Crop Yield Prediction


Book Description

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.




Modern Techniques for Agricultural Disease Management and Crop Yield Prediction


Book Description

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.




Machine Learning and Deep Learning for Smart Agriculture and Applications


Book Description

Machine Learning and Deep Learning for Smart Agriculture and Applications delves into the captivating realm of artificial intelligence and its pivotal role in transforming the landscape of modern agriculture. With a focus on precision agriculture, digital farming, and emerging concepts, this book illuminates the significance of sustainable food production and resource management in the face of evolving digital hardware and software technologies. Geospatial technology, robotics, the Internet of Things (IoT), and data analytics converge with machine learning and big data to unlock new possibilities in agricultural management. This book explores the synergy between these disciplines, offering cutting-edge insights into data-intensive processes within operational agricultural environments. From automated irrigation systems and agricultural drones for field analysis to crop monitoring and precision agriculture, the applications of machine learning are far-reaching. Animal identification and health monitoring also benefit from these advanced techniques. With practical case studies on vegetable and fruit leaf disease detection, drone-based agriculture, and the impact of pesticides on plants, this book provides a comprehensive understanding of the applications of machine learning and deep learning in smart agriculture. It also examines various modeling techniques employed in this field and showcases how artificial intelligence can revolutionize plant disease detection. This book serves as a comprehensive guide for researchers, practitioners, and students seeking to harness the power of AI in transforming the agricultural landscape.




Crop Disease Recognition and Classification Using Deep Learning


Book Description

The world's largest agricultural need is high production; hence, most countries use modern techniques to boost crop yields. Advanced technology should increase yields. Other factors such as environmental stresses (pests, diseases, drought stress, nutritional deficits, and weeds) and pests affect plants at any stage. Thus, in agriculture, both quantity and quality are reduced. Crop diseases are the most important reason for quality and quantity losses in farming production. Such losses negatively affect the profit and production costs of stakeholders in farming. Conventionally, plant pathologists and farmers utilize their eyes to notice diseases and formulate decisions depending upon their knowledge that are often not precise and at times biased as in the earlier time a lot of types of diseases seems to be similar. This scheme paved the way for the needless usage of pesticides that resulted in high generation costs. Therefore, the requirement for a precise disease detector related to a consistent dataset to assist farmers is essential, particularly for the case of inexperienced and young ones . Advancements in computer vision help with the usage of ML or DL schemes. Moreover, there is a requirement for an earlier disease recognition system for protecting the yield over time. Accordingly, CNN is highly deployed in crop disease detection, and reasonable results are attained. Nevertheless, the crop disease images attained from lands were characteristically uncertain images that have a noteworthy effect on the enhancement of accuracy in crop disease recognition from images. There is a detrimental effect on agricultural output due to the prevalence of crop diseases, and increase food insecurity . The agricultural industry relies heavily on early identification of diseases, that prevention of crop diseases. Spots or scars on the leaves, stems, flowers, or fruits are common symptoms of crop diseases. Most of the time, anomalies can be diagnosed by looking for telltale signs that are specific to a given disease or pest. The leaves of crops are often the first to show signs of disease, making them an excellent starting point for diagnosis




Agri-Tech Approaches for Nutrients and Irrigation Water Management


Book Description

This book includes concepts, methodologies, and techniques used in soil nutrients and irrigation water management with regional and global prospects. This book accommodates up-to-date approaches to agricultural technologies along with future directions and compiles a wide range of articles ranging from soil moisture flow, nutrient dynamics, crop water estimation techniques, approaches to improve crop water productivity and soil health, crop simulation modeling, and remote sensing/GIS applications. The book also includes chapters on climate-resilient agriculture, advances in big data and machine-learning techniques, IoT, plasma technology, seed priming, and precision farming techniques and their environmental/economic impacts. Features: • Discusses applications sustainable technologies for soil nutrients and irrigation water management at multi-scale. • Covers application of remote sensing/GIS, big data and machine learning, IoT, plasma technology, seed priming, and precision farming techniques for nutrients and water management. • Reviews concepts, methodologies, and techniques being used in soil nutrients and irrigation water management. • Provides up-to-date information as well as future directions in the field of nutrients and agricultural water management. This book is aimed at researchers and graduate students in agriculture, water resources, environment, and irrigation engineering.




Data-Driven Farming


Book Description

In the dynamic realm of agriculture, artificial intelligence (AI) and machine learning (ML) emerge as catalysts for unprecedented transformation and growth. The emergence of big data, Internet of Things (IoT) sensors, and advanced analytics has opened up new possibilities for farmers to collect and analyze data in real-time, make informed decisions, and increase efficiency. AI and ML are key enablers of data-driven farming, allowing farmers to use algorithms and predictive models to gain insights into crop health, soil quality, weather patterns, and more. Agriculture is an industry that is deeply rooted in tradition, but the landscape is rapidly changing with the emergence of new technologies. Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture is a comprehensive guide that explores how the latest advances in technology can help farmers make better decisions and maximize yields. It offers a detailed overview of the intersection of data, AI, and ML in agriculture and offers real-world examples and case studies that demonstrate how these tools can help farmers improve efficiency, reduce waste, and increase profitability. Exploring how AI and ML can be used to achieve sustainable and profitable farming practices, the book provides an introduction to the basics of data-driven farming, including an overview of the key concepts, tools, and technologies. It also discusses the challenges and opportunities facing farmers in today’s data-driven landscape. Covering such topics as crop monitoring, weather forecasting, pest management, and soil health management, the book focuses on analyzing data, predicting outcomes, and optimizing decision-making in a range of agricultural contexts.




Artificial Intelligence Tools and Technologies for Smart Farming and Agriculture Practices


Book Description

There are various factors that influence the quality and quantity of agricultural products; among them, weather conditions play the most significant role in agriculture. More reliable weather forecasting enables farmers to make important planting and harvesting decisions that can enhance agricultural yield. Thus, there is a dire need to combine all available modern technologies and agricultural science for economic and environmentally sustainable crop production. In this direction, artificial intelligence (AI) serves as a budding solution in the domain of agriculture practices. Artificial Intelligence Tools and Technologies for Smart Farming and Agriculture Practices discusses various tools and technologies that can be used in smart farming and agriculture practice and explores the role of different emerging technologies like the internet of things, big data, machine learning, deep learning, and AI from agricultural prospects. Covering key topics such as farming, pests, soil, and weeds, this premier reference source is ideal for environmentalists, farmers, agriculturalists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.




New Generation of Organic Fertilizers


Book Description

This book provides a comprehensive overview of organic fertilizers and their importance in sustainable agriculture, biodiversity, and the environment. It presents new approaches, ideas, and trends on how to increase the effectiveness of chemical fertilizers as well as the resistance of plants against biotic and abiotic stress conditions. Chapters address such topics as the benefits of organic fertilizers over their chemical counterparts, vermicomposting, organic farming, insects in organic fertilizer production, and much more.




Handbook of Research on Agricultural Policy, Rural Development, and Entrepreneurship in Contemporary Economies


Book Description

Promoting rural entrepreneurship is a necessary step to limit the negative effects of classical agricultural policy based on a linear process and attracting secondary resources to the economic process. The analysis of agricultural policy and rural development in conjunction to entrepreneurship in terms of production may represent a further step in understanding the role and importance of diversifying the rural potentials in contemporary economies. The Handbook of Research on Agricultural Policy, Rural Development, and Entrepreneurship in Contemporary Economies is an essential publication of academic research that examines agricultural policy and its impact on shaping future resilient economy in rural areas and identifies green business models and new business patterns in rural communities. Covering a range of topics such as entrepreneurship, product management, and marketing, this book is ideal for researchers, policymakers, academicians, economists, agriculture professionals, rural developers, business investors, and students.




Digital Ecosystem for Innovation in Agriculture


Book Description

This book presents the latest findings in the areas of digital ecosystem for innovation in agriculture. The book is organized into two sections with thirteen chapters dealing with specialized areas. It provides the reader with an overview of the frameworks and technologies involved in the digitalization of agriculture, as well as the data processing methods, decision-making processes, and innovative services/applications for enabling digital transformations in agriculture. The chapters are written by experts sharing their experiences in lucid language through case studies, suitable illustrations, and tables. The contents have been designed to fulfill the needs of geospatial, data science, agricultural, and environmental sciences of universities, agricultural universities, technological universities, research institutes, and academic colleges worldwide. It helps the planners, policymakers, and extension scientists plan and sustainably manage agriculture and natural resources.