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 collection features five peer-reviewed literature reviews on decision support systems (DSS) in agriculture. The first chapter provides a review of DSS in agriculture, whilst addressing the key questions surrounding their use for farm soil and crop management. The different aspects of agricultural DSS design, implementation and operation are also discussed. The second chapter assesses the role of DSS for pest monitoring and management through information technology such as, remote sensing, GIS, spectral indices, image-based diagnostics, and phenology-based degree day models. The third chapter discusses the potential of implementing DSS within the growing mechanisation in greenhouses. It examines differences in development and application of deterministic explanatory and data-based models for real-time control and DSS. The fourth chapter explores the key issues associated with deploying DSS in precision agriculture, whilst also considering their human and social aspects. The chapter also considers how future research on DSS can be moulded to improve productivity in a precision agriculture setting. The final chapter outlines the importance of a participatory approach in DSS development, whilst also offering examples of climate-based DSS for crop and land management, pest and disease management, and livestock (dairy) management.