Neil Boonham

This study uses supervised machine learning to combine 3D, multispectral, and thermal imaging data for improved detection of root rot in hydroponic parsley.

This study uses infrared thermography and machine learning to detect plant stress in vertical farms, finding that binary models trained on medial stress levels provide reliable probabilistic stress estimates despite spatiotemporal variations.

This paper develops a new digital color index, TREx, to assess plant health in both green-leaved and anthocyanin-rich crops using RGB data, showing strong correlation with chlorophyll measurements.

This study develops digital color analysis models to accurately assess pigment content and plant health in green and red leafy vegetables, showing that certain color features correlate strongly with chlorophyll and anthocyanin levels.

The paper identifies Fusarium species causing foot rot in UK faba beans using molecular and microbiological methods, finding F. oxysporum as the most prevalent pathogen.

This study compares eDNA metabarcoding and targeted qPCR for detecting the great crested newt, finding qPCR more sensitive but metabarcoding with no threshold comparable at a stringent qPCR threshold.