Agriculture is one of the most fundamental human activities. As the farming capacity has expanded, the usage of resources such as land, fertilizer, and water has grown exponentially, and environmental pressures from modern farming techniques have stressed natural landscapes. Still, by some estimates, worldwide food production needs to increase to keep up with global food demand. Machine Learning and the Internet of Things can play a promising role in the Agricultural industry, and help to increase food production while respecting the environment. This book explains how these technologies can be applied, offering many case studies developed in the research world.

Plant diseases and pests cause significant losses to farmers and threaten food security worldwide. Monitoring the growing conditions of crops and detecting plant diseases is critical for sustainable agriculture. Traditionally, crop inspection has been carried out by people with expert knowledge in the field. However, regarding any activity carried out by humans, this activity is prone to errors, leading to possible incorrect decisions. Innovation is, therefore, an essential fact of modern agriculture. In this context, deep learning has played a key role in solving complicated applications with increasing accuracy over time, and recent interest in this type of technology has prompted its potential application to address complex problems in agriculture, such as plant disease and pest recognition. Although substantial progress has been made in the area, several challenges still remain, especially those that limit systems to operate in real-world scenarios.

Digital farming is an approach to farming in which crop yield is maximized while environmental impact is minimized. Integral to this approach is diagnostic sensing of plant disease and stress. This book examines innovative sensing technology such as satellite- and unmanned aerial vehicle (UAV)-based RGB and thermography imaging as well as hyperspectral, infrared, reflectance and Raman spectroscopy.

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.

Precision farming is an agricultural management system using global navigation satellite systems, geographic information systems, remote sensing, and data management systems for optimizing the use of nutrients, water, seed, pesticides and energy in heterogeneous field situations. This book provides extensive information on the state-of-the-art of research on precision crop protection and recent developments in site-specific application technologies for the management of weeds, arthropod pests, pathogens and nematodes. It gives the reader an up-to-date and in-depth review of both basic and applied research developments. The chapters discuss I) biology and epidemiology of pests, II) new sensor technologies, III) applications of multi-scale sensor systems, IV) sensor detection of pests in growing crops, V) spatial and non-spatial data management, VI) impact of pest heterogeneity and VII) precise mechanical and chemical pest control.

This book constitutes the refereed proceedings of the 7th International Symposium on Ubiquitous Networking, UNet 2021, held in May 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 revised full papers presented together with 6 invited papers and 3 special sessions were carefully reviewed and selected from 38 submissions. The papers are organized in topical sections: ubiquitous communication technologies and networking; tactile internet and internet of things; mobile edge networking and fog-cloud computing; artificial intelligence-driven communications; and data engineering, cyber security and pervasive services.

Authored by an integrated committee of plant and animal scientists, this review of newer molecular genetic techniques and traditional research methods is presented as a compilation of high-reward opportunities for agricultural research. Directed to the Agricultural Research Service and the agricultural research community at large, the volume discusses biosciences research in genetic engineering, animal science, plant science, and plant diseases and insect pests. An optimal climate for productive research is discussed.

This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.

"This book examines the practical applications and implementation of various machine learning techniques in various fields such as agriculture, medical, image processing, and networking"--

Remote Sensing in Precision Agriculture: Transforming Scientific Advancement into Innovation compiles the latest applications of remote sensing in agriculture using spaceborne, airborne and drones' geospatial data. The book presents case studies, new algorithms and the latest methods surrounding crop sown area estimation, determining crop health status, assessment of vegetation dynamics, crop diseases identification, crop yield estimation, soil properties, drone image analysis for crop damage assessment, and other issues in precision agriculture. This book is ideal for those seeking to explore and implement remote sensing in an effective and efficient manner with its compendium of scientifically and technologically sound information. - Presents a well-integrated collection of chapters, with quality, consistency and continuity - Provides the latest RS techniques in Precision Agriculture that are addressed by leading experts - Includes detailed, yet geographically global case studies that can be easily understood, reproduced or implemented - Covers geospatial data, with codes available through shared links