Megan Long – författare

This collection features six peer-reviewed reviews on advances and in detecting and forecasting crop pests and diseases.The first chapter introduces the concept of machine learning to identify and diagnose crop diseases, focussing on the deep learning concept.The second chapter discusses recent advances in crop disease forecasting models, focussing on the application of precision agriculture technologies and Earth observation satellites to identify areas at risk of possible disease outbreaks.The third chapter explores the contribution of remote sensing in improving the ways in which plant health is monitored in response to exposure to biotic stresses, such as disease.The fourth chapter reviews how sensor technologies in combination with informatics and modern application technologies can contribute to more effective pest control.The fifth chapter assesses the role of decision support systems for pest monitoring and management through information technology, such as spectral indices and image-based diagnostics.The final chapter addresses key issues and challenges in pest monitoring and forecasting models, such as the limitation of some traps in attracting insects through the use of sex pheromones.

This book features five peer-reviewed reviews on machine vision applications in agriculture.The first chapter examines recent advances in machine vision technologies for the measurement of soil texture, structure and topography. The chapter also provides an overview of the basic principles of machine vision technologies, focussing on areas such as 3D surface modelling.The second chapter considers the use of machine learning methods to classify multiple diseases across several different crop types. The chapter also explains how deep learning for image analysis and classification works.The third chapter presents an overview of the use of machine learning for agri-robotics, including the main trends of the last decade. It also discusses the use of machine learning for data analysis and decision-making for perception and navigation.The fourth chapter addresses the prospects of machine vision application in plant factories with artificial lighting. The chapter also summarises recent research utilising this technology, including plant growth monitoring, robot operation assistance and fruit grading.The final chapter reviews advances in computer vision-based technologies for precision livestock farming. The chapter also reviews how automation in image analysis can promote smart management of livestock to improve health and welfare.