BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-it-2023//speaker//WCNFNQ BEGIN:VTIMEZONE TZID:GMT BEGIN:STANDARD DTSTART:20001029T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:GMT TZOFFSETFROM:+0100 TZOFFSETTO:+0000 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:BST TZOFFSETFROM:+0000 TZOFFSETTO:+0100 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-it-2023-7N8VLH@talks.staging.osgeo.org DTSTART;TZID=GMT:20230613T164500 DTEND;TZID=GMT:20230613T170000 DESCRIPTION:Crop traits monitoring is a fundamental step for controlling cr op productivity in the context of precision agriculture and field phenotyp ing. At present\, the usage of hyperspectral data in machine learning regr ession algorithms (MLRAs) has attracted increasing attention to alleviate the challenges associated with traditional crop trait measurements. Howeve r\, the performance assessment of such hyperspectral-based MLRA models for crop trait retrievals with respect to the well-known natural variations i n either structural or biochemical crop properties remains largely elusive . As such\, this experiment was set up to assess whether full-range hypers pectral data\, acquired by a handheld spectrometer (Spectral Evolution\; 3 50 – 2500 nm)\, as inputs to partial least squares regression (PLSR) and random forest (RF) models are capable of modeling different wheat crop tr aits at the canopy level. The examined crop traits were leaf area index (L AI)\, canopy water content (CWC)\, canopy chlorophyll content (CCC)\, and canopy nitrogen content (CNC). This approach allowed us\, as an overarchin g objective\, to compare the performance of the two aforementioned MLRA mo dels while also focusing on the physical interpretation of the modelling r esults for each particular crop trait. \nOverall\, PLSR provided remarkabl y higher accuracy\, tested with a cross-validation strategy\, as compared to RF for all the crop traits. More precisely\, PLSR denoted R2 (resp. nRM SE%) values of 0.72 (11.97)\, 0.77 (10.89)\, 0.70 (14.61)\, and 0.74 (14.3 8) for LAI\, CWC\, CCC\, and CNC\, respectively. All PLSR models indicated robust prediction capability with RPD values greater than 1.4\, and among st them\, CWC was found to have excellent prediction performance with an R PD higher than 2. However\, RF yielded less predictive models with R2 (res p. nRMSE%) values of 0.59 (14.59)\, 0.42 (17.42)\, 0.50 (18.86)\, and 0.42 (21.41) for LAI\, CWC\, CCC\, and CNC\, respectively. RF models for LAI a nd CCC showed good prediction capabilities (RPD > 1.4)\, whilst RF models of neither CWC nor CNC were reliable (RPD < 1.4). \nIn general\, RF band i mportance and PLSR regression coefficient results revealed physically- mea ningful and consistent patterns for each specific crop trait. Specific wav elengths at SWIR (1716-1745 nm) and NIR (1057-1120 nm)\, Green\, and the R ed-Edge bands respectively showed the highest importance for LAI retrieval . Water absorption regions around 910 nm and 1200 nm as well as the Red-Ed ge and Visible parts were of higher importance for the retrieval of CWC. T he best-performing bands were situated in Red-Edge and Green spectral chan nels for CCC retrieval. SWIR spectral regions between 1600-1800 nm and 210 0-2300 nm appeared to be important (in particular with respect to the othe r traits) alongside the Red-Edge part of the spectrum to retrieve CNC. \ nWe demonstrated that full-range hyperspectral data in combination with ML RA algorithms can provide accurate estimates of wheat crop traits at the c anopy level. The success of utilizing hyperspectral data in MLRA algorithm s was further highlighted by the physically-meaningful modelling performan ces in accordance with the subtle structural and biochemical crop properti es. Our results suggest that such spectroscopic hyperspectral-based MLRA a pproaches could be a powerful tool to accurately monitor crop status throu ghout the cropping season to improve high-throughput phenotyping activitie s and to further aid precision agricultural practices. DTSTAMP:20260512T203005Z LOCATION:Sala Videoconferenza @ PoliBa SUMMARY:Investigating PLSR and RF for retrieving wheat crop traits in a fie ld phenotyping experiment using full-range hyperspectral data: performance assessment and modelling interpretation - Ramin Heidarian Dehkordi\, Mirc o Boschetti\, Gabriele Candiani\, Federico Carotenuto\, Carla Cesaraccio\, Andrea Genangeli\, Beniamino Gioli\, Donato Cillis\, Marina_Ranghetti URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/7N8VLH/ END:VEVENT END:VCALENDAR