Bayesian machine learning with uncertainty quantification for detecting weeds in crop lands from low altitude remote sensing (2022 - )
Weeds are significant contributors (about 12% of global crop production) to crop yield and quality decline. Farmers use different approaches such as chemical or biological herbicides to eliminate weeds. However, excess use of herbicides leads to the pollution of soils, water, and air, putting the above and below ground wildlife biodiversity at risk. Alternative weed mitigation strategies must be designed and promoted. The site-specific weed management (SSWM) approach has been proposed consisting of varying weed management strategies within a crop field to suit the weed population's variation in density, location, and composition. The first step in implementing a SSWM strategy is accurate and timely detection and mapping of weeds. The high temporal, spatial, and spectral remote sensing information is required to capture detailed within-field variability, which can only be met by using the Low Altitude Remote Sensing platform and lightweight hyperspectral imaging sensors that can be deployed locally and at varying conditions.
This research aims to use hyperspectral multi-temporal Low altitude remote-based time-series imagery combined with field data and advanced machine learning (ML) techniques to detect and discriminate weeds in croplands. To achieve this aim, the project will i) monitor the weed population during different growth stages and preprocess of hyperspectral data and field data for better detecting weeds, ii) test a combination of ML and image processing algorithms for detecting weeds across a range of field and growing stage conditions, iii) apply the framework for multi-temporal analysis to track weed distribution and conditions for theuptake by precision farming techniques in our study regions.
Peer-reviewed Publications (journal or conference)
Other (presentations at conferences or preprints)