Lab

Abdul Mounem Mouazen's Lab

About the lab

SiTeMan (site specific technology for soil and crop management) research group.

Part of the department Environment at the Faculty of Bioscience Engineering, Ghent University.

Featured projects (1)

Project
Optimizing the seeding rate and depth with respect to soil heterogeneities, crop characteristics, topographical attributes and microclimate. In addition, to develop variable rate seeding technologies using sensor fusion of multiple soil and crop properties.

Featured research (27)

Previous works indicate that data fusion, compared to single data modelling can improve the assessment of soil attributes using spectroscopy. In this work, two different kinds of proximal soil sensing techniques i.e., mid-infrared (MIR) and X-ray fluorescence (XRF) spectroscopy were evaluated, for assessment of seven fertility attributes. These soil attributes include pH, organic carbon (OC), phosphorous (P), potassium (K), magnesium (Mg), calcium (Ca) and moisture contents (MC). Three kinds of spectra fusion (SF) (spectra concatenation) approaches of MIR and XRF spectra were compared, namely, spectra fusion-Partial least square (SF-PLS), spectra fusion-Sequential Orthogonalized Partial least square (SF-SOPLS) and spectra fusion-Variable Importance Projection-Sequential Orthogonalized Partial least square (SF-VIP-SOPLS). Furthermore, the performance of SF models was compared with the developed single sensor model (based on individual spectra of MIR and XRF). Compared with the results obtained from single sensor model, SF models showed improvement in the prediction performance for all studied attributes, except for OC, Mg, and K prediction. More specifically, the highest improvement was observed with SF-SOPLS model for pH [R2p = 0.90, root mean square error prediction (RMSEP) = 0.15, residual prediction deviation (RPD) = 3.30, and ratio of performance inter-quantile (RPIQ) = 3.59], successively followed by P (R2p = 0.91, RMSEP = 4.45 mg/100 g, RPD = 3.53, and RPIQ = 4.90), Ca (R2p = 0.92, RMSEP = 177.11 mg/100 g, RPD = 3.66, and RPIQ = 3.22) and MC (R2p = 0.80, RMSEP = 1.91%, RPD = 2.31, RPIQ = 2.62). Overall the study concluded that SF approach with SOPLS attained better performance over the traditional model developed with the single sensor spectra, hence, SF is recommended as the best SF method for improving the prediction accuracy of studied soil attributes. Moreover, the multi-sensor spectra fusion approach is not limited for only MIR and XRF data but in general can be extended for complementary information fusion in order to improve the model performance in precision agriculture (PA) applications.
External factors including moisture content negatively affect the prediction accuracy of soil organic carbon (SOC) using on-line visible and near-infrared (vis-NIR) spectroscopy. This study compared the performances of four algorithms to remove the moisture content effect [direct standardization (DS), piecewise direct standardization (PDS), external parameter orthogonalization (EPO), and orthogonal signal correction (OSC)] against non-corrected (NC) spectral models developed with partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and M5Rules regression. An on-line soil sensing platform coupled with a vis-NIR spectrophotometer (305-1700 nm) was used to scan twelve agricultural fields in Belgium and France. A total of 372 soil samples collected during the on-line measurement were divided into a calibration (260) and a prediction (112) dataset, using the Kennard-Stone algorithm. The latter set together with identical laboratory-measured 112 dry soil spectra formed a transfer dataset to develop EPO, DS and PDS correction matrices. Results showed that models after EPO, PDS and OSC corrections resulted in improved accuracy [coefficient of determination (R 2) = 0.60-0.82, root mean square error (RMSE) = 16.1-5.7 g kg − 1)], compared to the NC models (R 2 = 0.58-0.73, RMSE = 16.5-6.8 g kg − 1), whereas the DS (R 2 = − 0.10 to 0.26, RMSE = 26.8-21.9 g kg − 1) provided deteriorated prediction accuracy. The EPO and OSC models provided better prediction accuracy than that of the PDS corrected models. The OSC-M5Rules (R 2 = 0.82, RMSE = 5.7 g kg − 1) obtained the highest accuracy followed by EPO-M5Rules (R 2 = 0.74, RMSE = 6.7 g kg − 1) and NC-M5Rules (R 2 = 0.73, RMSE = 6.8 g kg − 1), which outperformed all PLSR, RF and SVM models. Therefore, on-line vis-NIR spectra should be corrected with the OSC algorithm before calibrating a machine learning model for accurate prediction of SOC.
Soil surveys with line-scanning platforms appear to have great advantages over the traditional methods used to collect soil information for the development of field-scale soil mapping and applications. These carry VNIR (visible and near infrared) spectrometers and have been used in recent years extensively for the assessment of soil fertility at the field scale, and the delineation of site-specific management zones (MZ). A challenging feature of VNIR applications in precision agriculture (PA) is the massiveness of the derived datasets that contain point predictions of soil properties, and the interpolation techniques involved in incorporating these data into site-specific management plans. In this study, fixed-rank kriging (FRK) geostatistical interpolation, which is a flexible, non-stationary spatial interpolation method especially suited to handling huge datasets, was applied to massive VNIR soil scanner data for the production of useful, smooth interpolated maps, appropriate for the delineation of site-specific MZ maps. Moreover, auxiliary Sentinel-2 data-based biophysical parameters NDVI (normalized difference vegetation index) and fAPAR (fraction of photosynthetically active radiation absorbed by the canopy) were included as covariates to improve the filtering performance of the interpolator and the ability to generate uniform patterns of spatial variation from which it is easier to receive a meaningful interpretation in PA applications. Results from the VNIR prediction dataset obtained from a pivot-irrigated field in Albacete, southeastern Spain, during 2019, have shown that FRK variants outperform ordinary kriging in terms of filtering capacity, by doubling the noise removal metrics while keeping the computation cost reasonably low. Such features, along with the capacity to handle a large volume of spatial information, nominate the method as ideal for PA applications with massive proximal and remote sensing datasets
Aggregate stability (AS) is an important parameter to evaluate soil resistance to erosion. The conventional determination methods to measure AS are time consuming, difficult and labour intensive. Visible (vis) and near infrared (NIR) spectroscopy could be a better alternative to the conventional determination methods of AS. This study explored the possibilities of estimating three AS indices, reflecting stability upon slow wetting (SW), fast wetting (FW) and mechanical breakdown (MB), using vis-NIR spectra data on some air-dried, non-sieved soils of the Belgian loam belt. Partial least squares regression (PLSR) was used to build calibration models for the three stability indices, using a calibration set accounting for 70% and a validation set of 30% of the total samples. Results showed that all three AS indices can be predicted to appreciable accuracies from vis-NIR-PLSR models [coefficient of determination (R²) = 0.72–0.80, residual prediction deviation (RPD) = 1.93–2.27, ratio of performance to interquartile range (RPIQ) = 2.23–4.09 and root mean square error (RMSE) = 0.29–0.52 mm]. The prediction results suggest that omission of sample pre-treatment by sieving or grinding may have very limited impact on the prediction accuracy. This opens up opportunities for the in-situ deployment of vis-NIR spectroscopy, provided that problems associated with variable soil moisture contents can be overcome.
This study evaluated the agronomic and economic prospects of Site-Specific Seeding (SSS) for consumption and seed potato production based on Management Zone (MZ) maps delineated with the fusion of multiple soil and crop attributes at four experimental sites in Belgium. Soil pH, organic carbon, P, K, Mg, Ca, Na, moisture content, cation exchange capacity, apparent electrical conductivity and crop normalized difference vegetation index were measured with an on-line visible and near-infrared reflectance spectroscopy sensor, electromagnetic induction sensor, and Sentinel-2 constellation, respectively. Spatial alignment of the different data layers generated a co-georeferenced data matrix for data fusion by k-means clustering. Per field MZ classes were ranked according to their fertility status and the prescription rule of sowing more seeds to the more fertile zones and vice versa was adopted and compared against a Uniform Rate Seeding (URS) treatment in a strip plot experiment. Cost–benefit analysis revealed that the SSS improved tuber yields, hence, increased gross margin (137.81 to 457.83 €/ha) of production compared to the URS, although SSS consumed relatively higher amount of seeds. The percentage of gross margin increase varied between 2.34 and 27.21%, with the highest profitability in fields with low productivity. Larger seed-to-seed spacing than the control increased the proportion of the most demanded and profitable tuber category, suggesting the seeding interval is a key determinant of tuber size distribution. It is suggested to adopt SSS for potato production using the proposed multi-sensor data-fusion approach to manage in-field soil and crop variabilities, and improve productivity and profitability.

Lab head

Abdul Mounem Mouazen
Department
  • Department of Environment
About Abdul Mounem Mouazen
  • I am a Senior Full Professor in precision soil and crop management and a group leader of Precision SCoRing Group at Ghent University, Belgium. I have a background in the application of engineering principles to soil and water management, with specific applications in soil dynamics, tillage, traction, compaction, mechanical weeding, and precision soil management. Abdul is a member of Editorial Boards of Soil & Tillage Research, Soil Research, Biosystems Engineering, Remote Sensing, & Soil Systems

Members (21)

Reuben N Okparanma
  • Rivers State University of Science and Technology
Muhammad Abdul Munnaf
  • Ghent University
Lalit Mohan Kandpal
  • Wageningen University & Research
Rebecca Whetton
  • University College Dublin
B. Kuang
  • Cranfield University
Simon Appeltans
  • Ghent University
Kirsty L. Hassall
Kirsty L. Hassall
  • Not confirmed yet
Xi Guo
Xi Guo
  • Not confirmed yet
Boyan Kuang
Boyan Kuang
  • Not confirmed yet
Rebecca L. Whetton
Rebecca L. Whetton
  • Not confirmed yet
Meihua Yang
Meihua Yang
  • Not confirmed yet
Rebecca L. Whetton
Rebecca L. Whetton
  • Not confirmed yet
Raed A. Al-Asadi
Raed A. Al-Asadi
  • Not confirmed yet
Mohammed Z. Quraishi
Mohammed Z. Quraishi
  • Not confirmed yet