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.
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