|Keywords:||Vis-NIR spectroscopy; horticulture; on-line measurement; nitrogen fertilizer; variable-rate|
|Full text PDF:||http://dspace.lib.cranfield.ac.uk/handle/1826/9211|
Fertiliser applications in vegetable crops are one of the main input costs of production. Thematic soil maps have been widely used for decades to characterise soil nutrients and, therefore, apply variable rate fertilisers. However, traditional variable rate methods used in soil sampling are time- consuming, costly and not accurate. Thus, they fail in providing a true estimate of the nutrients soil needs. To obtain better crop response to inputs, a rapid, non-destructive, timely and cost-effective soil analysis are needed to enable site-specific fertiliser applications. Proximal soil sensing with visible and near infrared (vis-NIR) spectroscopy is a promising tool to assist in variable rate applications. This thesis aims to develop reliable calibration models for a previously developed on-line visible (vis) and near infrared (NIR) spectroscopy sensor (Mouazen, 2006), for the prediction of soil properties in vegetable crop fields for a better N fertiliser management. Experiments were established in crops of cauliflower (Brassica oleracea) during 2013 season (two fields) and 2014 season (three fields), in UK. A mobile, fibre-type, vis–NIR spectrophotometer (AgroSpec, Tec5 Technology for Spectroscopy, Germany) with a measurement range of 305-2200 nm was used to measure soil spectra in diffuse reflectance mode, measuring up to ~1500 points per ha. Four different calibration sets were tested to establish the most accurate calibration model for moisture content (MC), soil organic carbon (OC), pH and total nitrogen (TN), using partial least squares (PLS) regression analysis selected according to different spectral library size and geographical scale: Scenario 1 (SC1 (local)), Scenario 2 (SC2 (regional)), Scenario 3 (SC3 (national)), Scenario 4 (SC4 (continental)). The best results in cross-validation were obtained for MC with SC2 (R2[R squared] = 0.89; RPD > 2.5), followed by SC4 (R2[R squared] = 0.88; RPD = 2.91-3.31, in 2013 and 2014, respectively); and SC1 and SC4 worked very well for MC on- line prediction (R2[R squared] > 0.90 and RPD > 2.5). SC3 and SC4 both provided the best performance for OC and TN in cross-validation, whereas no clear trend was observed for on-line prediction. Poor model performance was obtained for pH in on-line predictions (R2[R squared] < 0.30 and RPD < 0.9). Although the calibration models using the on-line vis-NIR sensor provided good and detailed information of the soil nutrients analysed, future research will be needed to estimate these properties more accurately, with the aim to develop reliable vis-NIR calibration models for the on-line measurement in vegetable crop fields.