<p>Bananas are the fourth most important staple food source globally and are considered vital for economic development and food security in many countries. Current management of commercial banana crops is largely based on in-field visual appraisal and manual record keeping, with targeted agronomic activities guided by the manual tagging of individual plants in the field. Such activities can be labour-intensive, subjective and lacking rigour as they often rely on the experience of the individual undertaking the assessment. Remote sensing technologies play a fundamental, enabling role in precision agriculture and are becoming increasingly commonplace. Applications such as the monitoring of crop phenology to guide management activities, determining harvest readiness, pest and disease detection and yield forecasting using remote sensing have been adopted by other industries, but for banana these tasks are still currently undertaken manually. Little to no adoption of remote sensing applications exist in the banana industry and research into new applications is minimal. The low level of adoption is largely due to the unique phenology, morphology, propagation, and growing properties of banana plants that limit the use of whole-field remote sensing applications common in other crops. To address these knowledge gaps, this thesis developed methods and investigated the accuracies of ground and unoccupied aerial vehicle (UAV) based sensors for measuring several key needs of the Australian banana industry.<p><p>Robust spatio-temporal detection and delineation methods were developed and assessed for their ability to accurately represent individual banana plant crowns from UAV multispectral imagery. Furthering this concept of individual plant monitoring, a time series based on a 15-month UAV flight campaign was used to create and compare spectral and morphological data of individual plants over time, from initial establishment to harvest. Verification against infield measurements determined that UAV-based multi-temporal crop monitoring models of individual banana plants can be used for the determination of key phenological growth stages of banana plants (including establishment, flower emergence and harvest) and offer pre-harvest yield forecasts. Finally, the accuracies of both hyperspectral and multispectral data for measuring mite infestations on banana plants were investigated, with both sensors providing promising results. Overall, this research's findings and developed methods contribute important information that enhances crop knowledge and understanding. The methods presented have the potential to add novel precision agriculture applications to the banana industry that compensate for the unique growth and propagation of banana crops. These outcomes have the potential to improve and promote economic advancement and food security in the banana industry.<p>