Advanced Ai Methods For Plant Disease And Pest Recognition
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Advanced AI Methods for Plant Disease and Pest Recognition by Jucheng Yang,Yalin Wu,Alvaro Fuentes,Sook Yoon,Tonghai Liu Pdf
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.
Human and Machine Learning by Jianlong Zhou,Fang Chen Pdf
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.
Diagnostics of Plant Diseases by Dmitry Kurouski Pdf
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.
Ubiquitous Networking by Halima Elbiaze,Essaid Sabir,Francisco Falcone,Mohamed Sadik,Samson Lasaulce,Jalel Ben Othman Pdf
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.
Advanced application technology for plant protection: Sensing, modelling, spraying system and equipment by Changyuan Zhai,Wei Qiu,Paul Weckler,Xiongkui He,Khawar Jabran Pdf
General Concepts in Integrated Pest and Disease Management by A. Ciancio,K.G. Mukerji Pdf
This, the first volume of the ‘Integrated Management of Plant Pests and Diseases’ book series, presents general concepts on integrated pest and disease management. Section one includes chapters on infection models, resurgence and replacement, plant disease epidemiology and effects of climate change in tropical environments. The second section includes remote sensing and information technology. Finally, the third section covers molecular aspects of the subject.
Advances in Neural Computation, Machine Learning, and Cognitive Research II by Boris Kryzhanovsky,Witali Dunin-Barkowski,Vladimir Redko,Yury Tiumentsev Pdf
This book describes new theories and applications of artificial neural networks, with a special focus on addressing problems in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large-scale neural models, brain–computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XX International Conference on Neuroinformatics, held in Moscow, Russia on October 8–12, 2018.
New Directions for Biosciences Research in Agriculture by National Research Council,Board on Agriculture,Research in Agriculture,Committee on Biosciences Pdf
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.
Advanced Communication and Intelligent Systems by Rabindra Nath Shaw,Marcin Paprzycki,Ankush Ghosh Pdf
This book constitutes selected papers presented at the First International Conference on Advanced Communication and Intelligent Systems, ICACIS 2022, held as a virtual event in October 2022. The 69 papers were thoroughly reviewed and selected from the 258 subissions. The book focuses on current development in the fields of communication and intelligent systems.
Encyclopedia of Machine Learning by Claude Sammut,Geoffrey I. Webb Pdf
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.
Applied Machine Learning and Data Analytics by M. A. Jabbar,Fernando Ortiz-Rodríguez,Sanju Tiwari,Patrick Siarry Pdf
This book constitutes the refereed proceedings of the 5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022, held in Reynosa, Tamaulipas, Mexico, during December 22–23, 2022. The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 89 submissions. They were organized in topical sections as follows: Machine learning, Healthcare and medical imaging informatics; biometrics; forensics; precision agriculture; risk management; robotics and satellite imaging.
Applications of artificial intelligence, machine learning, and deep learning in plant breeding by Maliheh Eftekhari,Chuang Ma,Yuriy L. Orlov Pdf
Artificial Intelligence (AI) is an extensive concept that can be interpreted as a concentration on designing computer programs to train machines to accomplish functions like or better than hu-mans. An important subset of AI is Machine Learning (ML), in which a computer is provided with the capacity to learn its own patterns instead of the patterns and restrictions set by a human programmer, thus improving from experience. Deep Learning (DL), as a class of ML techniques, employs multilayered neural networks. The application of AI to plant science research is new and has grown significantly in recent years due to developments in calculation power, proficien-cies of hardware, and software progress. AI algorithms try to provide classifications and predic-tions. As applied to plant breeding, particularly omics data, ML as a given AI algorithm tries to translate omics data, which are intricate and include nonlinear interactions, into precise plant breeding. The applications of AI are extending rapidly and enhancing intensely in sophistication owing to the capability of rapid processing of huge and heterogeneous data. The conversion of AI techniques into accurate plant breeding is of great importance and will play a key role in the new era of plant breeding techniques in the coming years, particularly multi-omics data analysis. Advancements in plant breeding mainly depend upon developing statistical methods that harness the complicated data provided by analytical technologies identifying and quantifying genes, transcripts, proteins, metabolites, etc. The systems biology approach used in plant breeding, which integrates genomics, transcriptomics, proteomics, metabolomics, and other omics data, provides a massive amount of information. It is essential to perform accurate statistical analyses and AI methods such as ML and DL as well as optimization techniques to not only achieve an understanding of networks regulation and plant cell functions but develop high-precision models to predict the reaction of new Genetically Modified (GM) plants in special conditions. The constructed models will be of great economic importance, significantly reducing the time, labor, and instrument costs when finding optimized conditions for the bio-exploitation of plants. This Research Topic covers a wide range of studies on artificial intelligence-assisted plant breeding techniques, which contribute to plant biology and plant omics research. The relevant sub-topics include, but are not restricted to, the following: • AI-assisted plant breeding using omics and multi-omics approaches • Applying AI techniques along with multi-omics to recognize novel biomarkers associated with plant biological activities • Constructing up-to-date ML modeling and analyzing methods for dealing with omics data related to different plant growth processes • AI-assisted omics techniques in the plant defense process • Combining AI-assisted omics and multi-omics techniques using plant system biology approaches • Combining bioinformatics tools with AI approaches to analyze plant omics data • Designing cutting-edge workflow and developing innovative AI biology methods for omics data analysis
Internet of Things and Machine Learning in Agriculture by Jyotir Moy Chatterjee,Abhishek Kumar,Pramod Singh Rathore,Vishal Jain Pdf
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.
Technology in Agriculture by Fiaz Ahmad,Muhammad Sultan Pdf
Food security is one of the primary themes of the United Nations’ Sustainable Development Goals. In this regard, agricultural engineering is considered the backbone of agriculture, and agricultural mechanization is considered a helpful way to enhance crop yield and farmers’ profitability. Technology in Agriculture presents research in the field of agricultural engineering technologies and applications in agricultural equipment engineering, biosystem engineering, energy systems engineering, and computers in agriculture. It provides an overview of recent advancements in agricultural engineering and examines key aspects of emerging technologies and their applications. In addition, the book explores modern methodologies such as artificial intelligence and machine learning for agricultural mechanization.
