Browsing by Author "Quraishi, Mohammed Z."

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    On-line measurement of soil properties without direct spectral response in near infrared spectral range
    (Elsevier , 2013-08-01T00:00:00Z) Marín-González, Omar ; Kuang, Boyan Y.; Quraishi, Mohammed Z.; Munóz-García, Miguel Ángel; Mouazen, Abdul Mounem
    So far, the majority of reports on on-line measurement considered soil properties with direct spectral responses in near infrared spectroscopy (NIRS). This work reports on the results of on-line measurement of soil properties with indirect spectral responses, e.g. pH, cation exchange capacity (CEC), exchangeable calcium (Ca-ex) and exchangeable magnesium (Mg-ex) in one field in Bedfordshire in the UK. The on-line sensor consisted of a subsoiler coupled with an AgroSpec mobile, fibre type, visible and near infrared (vis-NIR) spectrophotometer (tec5 Technology for Spectroscopy, Germany), with a measurement range 305-2200 nm to acquire soil spectra in diffuse reflectance mode. General calibration models for the studied soil properties were developed with a partial least squares regression (PLSR) with one-leave-out cross validation, using spectra measured under non-mobile laboratory conditions of 160 soil samples collected from different fields in four farms in Europe, namely, Czech Republic, Denmark, Netherland and UK. A group of 25 samples independent from the calibration set was used as independent validation set. Higher accuracy was obtained for laboratory scanning as compared to on-line scanning of the 25 independent samples. The prediction accuracy for the laboratory and on-line measurements was classified as excellent/very good for pH (RPD = 2.69 and 2.14 and r(2) = 0.86 and 0.78, respectively), and moderately good for CEC (RPD = 1.77 and 1.61 and r(2) = 0.68 and 0.62, respectively) and Mg-ex (RPD = 1.72 and 1.49 and r(2) = 0.66 and 0.67, respectively). For Ca-ex, very good accuracy was calculated for laboratory method (RPD = 2.19 and r(2) = 0.86), as compared to the poor accuracy reported for the on-line method (RPD = 1.30 and r(2) = 0.61). The ability of collecting large number of data points per field area (about 12,800 point per 21 ha) and the simultaneous analysis of several soil properties without direct spectral response in the NIR range at relatively high operational speed and appreciable accuracy, encourage the recommendation of the on-line measurement system for site specific fertilisation
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    A prototype sensor for the assessment of soil bulk density
    (Elsevier Science B.V., Amsterdam., 2013-11-01T00:00:00Z) Quraishi, Mohammed Z.; Mouazen, Abdul Mounem
    A prototype bulk density sensor (PBDS) to assess soil bulk density (BD) has been developed and tested for top soil (0-15 cm). It is a multi-sensor kit, consisting of a penetrometer equipped with a visible and near-infrared (vis-NIR) spectrophotometer. Artificial neural network (ANN) was used to develop a BD prediction model, as a function of penetration resistance (PR), soil moisture content (MC), organic matter content (OMC) and clay content (CLC), using 471 samples collected from various fields across four European countries, namely, Czech Republic, Denmark, the Netherlands and the UK. While penetration resistance (PR) was measured with a standard penetrometer (30 degree cone of 1.26 cm2 cone-base area), MC, OMC and CLC were predicted with a vis-NIR (1650-2500 nm) spectrophotometer (Avantes, Eerbeek, The Netherlands). ANN was also used to model the vis-NIR spectra to predict MC, OMC and CLC. The PBDS was validated by predicting topsoil (0-0.15 m) BD of three selected validation fields in Silsoe experimental farm, the UK. The ANN BD model performed very well in training (coefficient of determination (R2) = 0.92 and root mean square error (RMSE) = 0.05 Mg m3), validation (R2 = 0.84 and RMSE = 0.08 Mg m3) and testing (R2 = 0.94 and RMSE = 0.04 Mg m3). The validation of PBDS for BD assessment in the three validation fields provided high prediction accuracy, with the highest accuracy obtained in Downing field (R2 = 0.95 and RMSE = 0.02 Mg m3). It can be concluded that the new prototype sensor to predict BD based on, a standard penetrometer equipped with a vis-NIR spectrophotometer and ANN model can be used for in situ assessment of BD. The PBDS can also be recommended to provide information about soil MC, OMC and CLC, as the ANN vis-NIR calibration models of these properties were of excellent performance.