With the agricultural sector facing mounting pressure to reduce their carbon footprint, greater emphasis has been placed on improving existing components and practices, such as soil health and biodiversity, which have since emerged as key components to achieving regenerative agriculture. Sensors provide the opportunity to measure crop and soil health at unparalleled scales and resolution. Key developments in sensor technology will help improve our current understanding and optimisation of the complex agricultural systems that make up our global ecosystem. Advances in sensor technology for sustainable crop production provides a comprehensive review of the wealth of research on key developments in sensor technology to improve monitoring and management of crop health, soil health, weeds and diseases. This collection also reviews advances in proximal and remote sensing techniques to monitor soil health, such as spectroscopy and radiometrics, as well as how sensor technology can be optimised for more targeted irrigation, site-specific nutrient and weed management.

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.