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- ItemUAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds(Basel : MDPI, 2022) Li, Minhui; Shamshiri, Redmond R.; Schirrmann, Michael; Weltzien, Cornelia; Shafian, Sanaz; Laursen, Morten StigaardLeaf area index (LAI) and height are two critical measures of maize crops that are used in ecophysiological and morphological studies for growth evaluation, health assessment, and yield prediction. However, mapping spatial and temporal variability of LAI in fields using handheld tools and traditional techniques is a tedious and costly pointwise operation that provides information only within limited areas. The objective of this study was to evaluate the reliability of mapping LAI and height of maize canopy from 3D point clouds generated from UAV oblique imagery with the adaptive micro-terrain model. The experiment was carried out in a field planted with three cultivars having different canopy shapes and four replicates covering a total area of 48 × 36 m. RGB images in nadir and oblique view were acquired from the maize field at six different time slots during the growing season. Images were processed by Agisoft Metashape to generate 3D point clouds using the structure from motion method and were later processed by MATLAB to obtain clean canopy structure, including height and density. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for height and leaf density distribution along the canopy height. A simulation analysis based on the Sine function effectively demonstrated the micro-terrain model from point clouds. For the ground truth data, a randomized block design with 24 sample areas was used to manually measure LAI, height, N-pen data, and yield during the growing season. It was found that canopy height data from the 3D point clouds has a relatively strong correlation (R2 = 0.89, 0.86, 0.78) with the manual measurement for three cultivars with CH90 . The proposed methodology allows a cost-effective high-resolution mapping of in-field LAI index extraction through UAV 3D data to be used as an alternative to the conventional LAI assessments even in inaccessible regions.
- ItemImpact of Camera Viewing Angle for Estimating Leaf Parameters of Wheat Plants from 3D Point Clouds(Basel : MDPI, 2021) Li, Minhui; Shamshiri, Redmond R.; Schirrmann, Michael; Weltzien, CorneliaEstimation of plant canopy using low-altitude imagery can help monitor the normal growth status of crops and is highly beneficial for various digital farming applications such as precision crop protection. However, extracting 3D canopy information from raw images requires studying the effect of sensor viewing angle by taking into accounts the limitations of the mobile platform routes inside the field. The main objective of this research was to estimate wheat (Triticum aestivum L.) leaf parameters, including leaf length and width, from the 3D model representation of the plants. For this purpose, experiments with different camera viewing angles were conducted to find the optimum setup of a mono-camera system that would result in the best 3D point clouds. The angle-control analytical study was conducted on a four-row wheat plot with a row spacing of 0.17 m and with two seeding densities and growth stages as factors. Nadir and six oblique view image datasets were acquired from the plot with 88% overlapping and were then reconstructed to point clouds using Structure from Motion (SfM) and Multi-View Stereo (MVS) methods. Point clouds were first categorized into three classes as wheat canopy, soil background, and experimental plot. The wheat canopy class was then used to extract leaf parameters, which were then compared with those values from manual measurements. The comparison between results showed that (i) multiple-view dataset provided the best estimation for leaf length and leaf width, (ii) among the single-view dataset, canopy, and leaf parameters were best modeled with angles vertically at -45⸰_ and horizontally at 0⸰_ (VA -45, HA 0), while (iii) in nadir view, fewer underlying 3D points were obtained with a missing leaf rate of 70%. It was concluded that oblique imagery is a promising approach to effectively estimate wheat canopy 3D representation with SfM-MVS using a single camera platform for crop monitoring. This study contributes to the improvement of the proximal sensing platform for crop health assessment. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- ItemSimulating Cotton Growth and Productivity Using AquaCrop Model under Deficit Irrigation in a Semi-Arid Climate(Basel : MDPI AG, 2022) Aziz, Marjan; Rizvi, Sultan Ahmad; Sultan, Muhammad; Bazmi, Muhammad Sultan Ali; Shamshiri, Redmond R.; Ibrahim, Sobhy M.; Imran, Muhammad A.AquaCrop is a water-driven model that simulates the effect of environment and management on crop production under deficit irrigation. The model was calibrated and validated using three databases and four irrigation treatments (i.e., 100%ET, 80%ET, 70%ET, and 50%ET). Model performance was evaluated by simulating canopy cover (CC), biomass accumulation, and water productivity (WP). Statistics of root mean square error (RMSE) and Willmott’s index of agreement (d) showed that model predictions are suitable for non-stressed and moderate stressed conditions. The results showed that the simulated biomass and yield were consistent with the measured values with a coefficient of determination (R2) of 0.976 and 0.950, respectively. RMSE and d-index values for canopy cover (CC) were 2.67% to 4.47% and 0.991% to 0.998% and for biomass were 0.088 to 0.666 ton/ha and 0.991 to 0.999 ton/ha, respectively. Prediction of simulated and measured biomass and final yield was acceptable with deviation ˂10%. The overall value of R2 for WP in terms of yield was 0.943. Treatment with 80% ET consumed 20% less water than the treatment with 100%ET and resulted in high WP in terms of yield (0.6 kg/m3) and biomass (1.74 kg/m3), respectively. The deviations were in the range of −2% to 11% in yield and −2% to 4% in biomass. It was concluded that AquaCrop is a useful tool in predicting the productivity of cotton under different irrigation scenarios.
- ItemUsing SPOT-7 for Nitrogen Fertilizer Management in Oil Palm(Basel : MDPI AG, 2020) Yadegari, Mohammad; Shamshiri, Redmond R.; Shariff, Abdul Rashid Mohamed; Balasundram, Siva K.; Mahns, BenjaminEnvironmental concerns are growing about excessive applying nitrogen (N) fertilizers, especially in oil palm. Some conventional methods which are used to assess the amount of nutrient in oil palm are time-consuming, expensive, and involve frond destruction. Remote sensing as a non-destructive, affordable, and efficient method is widely used to detect the concentration of chlorophyll (Chl) from canopy plants using several vegetation indices (VIs) because there is an influential relation between the concentration of N in the leaves and canopy Chl content. The objectives of this research are to (i) evaluate and compare the performance of various vegetation indices (VIs) for measuring N status in oil palm canopy using SPOT-7 imagery (AIRBUS Defence & Space, Ottobrunn, Germany) to (ii) develop a regression formula that can predict the N content using satellite data to (iii) assess the regression formula performance on testing datasets by testing the coefficient of determination between the predicted and measured N contents. SPOT-7 was acquired in a 6-ha oil palm planted area in Pahang, Malaysia. To predict N content, 28 VIs based on the spectral range of SPOT-7 satellite images were evaluated. Several regression models were applied to determine the highest coefficient of determination between VIs and actual N content from leaf sampling. The modified soil-adjusted vegetation index (MSAVI) generated the highest coefficient of determination (R2 = 0.93). MTVI1 and triangular VI had the highest second and third coefficient of determination with N content (R2 = 0.926 and 0.923, respectively). The classification accuracy assessment of the developed model was evaluated using several statistical parameters such as the independent t-test, and p-value. The accuracy assessment of the developed model was more than 77%.
- ItemScientific Irrigation Scheduling for Sustainable Production in Olive Groves(Basel : MDPI AG, 2022) Aziz, Marjan; Khan, Madeeha; Anjum, Naveeda; Sultan, Muhammad; Shamshiri, Redmond R.; Ibrahim, Sobhy M.; Balasundram, Siva K.; Aleem, MuhammadThe present study aimed at investigating scientific irrigation scheduling (SIS) for the sustainable production of olive groves. The SIS allows farmers to schedule water rotation in their fields to abate crop water stress and maximize yields, which could be achieved through the precise monitoring of soil moisture. For this purpose, the study used three kinds of soil moisture sensors, including tensiometer sensors, irrometer sensors, and gypsum blocks for precise measurement of the soil moisture. These soil moisture sensors were calibrated by performing experiments in the field and laboratory at Barani Agricultural Research Institute, Chakwal in 2018 and 2019. The calibration curves were obtained by performing gravimetric analysis at 0.3 and 0.6 m depths, thereby equations were developed using regression analysis. The coefficient of determination (R2 ) at 0.3 and 0.6 m depth for tensiometer, irrometer, and gypsum blocks was found to be equal to 0.98, 0.98; 0.75, 0.89; and 0.82, and 0.95, respectively. After that, a drip irrigation system was installed with the calibrated soil moisture sensors at 0.3 and 0.6 m depth to schedule irrigation for production of olive groves as compared to conventional farmer practice, thereby soil moisture profiles of these sensors were obtained to investigate the SIS. The results showed that the irrometer sensor performed as expected and contributed to the irrigation water savings between 17% and 25% in 2018 and 2019, respectively, by reducing the number of irrigations as compared toother soil moisture sensors and farmer practices. Additionally, olive yield efficiencies of 8% and 9%were observed by the tensiometer in 2018 and 2019, respectively. The outcome of the study suggests that an effective method in providing sustainable production of olive groves and enhancing yield efficiency.