Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods.

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times.

Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping.

Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner.

Vegetation cover dynamics along two Himalayan rivers: Drivers and implications of change.

Rivers are dynamic landscape features that change in response to natural and anthropogenic factors through hydrological, geomorphic and ecological processes. The severity and magnitude of human impacts on river system and riparian vegetation has dramatically increased over the last century with the proliferation of valley-spanning dams, intensification of agriculture, urbanization, and more widespread channel engineering. This study aims to determine how changes in geomorphic form and dynamics caused by these human alterations relate to changes in channels and riparian vegetation in the lower Beas and Sutlej Rivers.