Crop Monitoring Using Sentinel-2 and UAV Multispectral Imagery: A Comparison Case Study in Northeastern Germany

Monitoring within-field crop variability at fine spatial and temporal resolution can assist farmers in making reliable decisions during their agricultural management; however, it traditionally involves a labor-intensive and time-consuming pointwise manual process. To the best of our knowledge, few studies conducted a comparison of Sentinel-2 with UAV data for crop monitoring in the context of precision agriculture. Therefore, prospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated from a more practical viewpoint closer to agricultural routines. Multispectral UAV and Sentinel-2 imagery was collected over three dates in the season and compared with reference data collected at 20 sample points for plant leaf nitrogen (N), maximum plant height, mean plant height, leaf area index (LAI), and fresh biomass. Higher correlations of UAV data to the agronomic parameters were found on average than with Sentinel-2 data with a percentage increase of 6.3% for wheat and 22.2% for barley. In this regard, VIs calculated from spectral bands in the visible part performed worse for Sentinel-2 than for the UAV data. In addition, large-scale patterns, formed by the influence of an old riverbed on plant growth, were recognizable even in the Sentinel-2 imagery despite its much lower spatial resolution. Interestingly, also smaller features, such as the tramlines from controlled traffic farming (CTF), had an influence on the Sentinel-2 data and showed a systematic pattern that affected even semivariogram calculation. In conclusion, Sentinel-2 imagery is able to capture the same large-scale pattern as can be derived from the higher detailed UAV imagery; however, it is at the same time influenced by management-driven features such as tramlines, which cannot be accurately georeferenced. In consequence, agronomic parameters were better correlated with UAV than with Sentinel-2 data. Crop growers as well as data providers from remote sensing services may take advantage of this knowledge and we recommend the use of UAV data as it gives additional information about management-driven features. For future perspective, we would advise fusing UAV with Sentinel-2 imagery taken early in the season as it can integrate the effect of agricultural management in the subsequent absence of high spatial resolution data to help improve crop monitoring for the farmer and to reduce costs

Impact of Camera Viewing Angle for Estimating Leaf Parameters of Wheat Plants from 3D Point Clouds

Estimation 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

Robotic Harvesting of Fruiting Vegetables: A Simulation Approach in V-REP, ROS and MATLAB

In modern agriculture, there is a high demand to move from tedious manual harvesting to a continuously automated operation. This chapter reports on designing a simulation and control platform in V-REP, ROS, and MATLAB for experimenting with sensors and manipulators in robotic harvesting of sweet pepper. The objective was to provide a completely simulated environment for improvement of visual servoing task through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. A simulated workspace, including an exact replica of different robot manipulators, sensing mechanisms, and sweet pepper plant, and fruit system was created in V-REP. Image moment method visual servoing with eye-in-hand configuration was implemented in MATLAB, and was tested on four robotic platforms including Fanuc LR Mate 200iD, NOVABOT, multiple linear actuators, and multiple SCARA arms. Data from simulation experiments were used as inputs of the control algorithm in MATLAB, whose outputs were sent back to the simulated workspace and to the actual robots. ROS was used for exchanging data between the simulated environment and the real workspace via its publish-and-subscribe architecture. Results provided a framework for experimenting with different sensing and acting scenarios, and verified the performance functionality of the simulator

Galaxy formation in the Planck cosmology - II. Star-formation histories and post-processing magnitude reconstruction

We adapt the L-Galaxies semi-analytic model to follow the star-formation histories (SFH) of galaxies -- by which we mean a record of the formation time and metallicities of the stars that are present in each galaxy at a given time. We use these to construct stellar spectra in post-processing, which offers large efficiency savings and allows user-defined spectral bands and dust models to be applied to data stored in the Millennium data repository. We contrast model SFHs from the Millennium Simulation with observed ones from the VESPA algorithm as applied to the SDSS-7 catalogue. The overall agreement is good, with both simulated and SDSS galaxies showing a steeper SFH with increased stellar mass. The SFHs of blue and red galaxies, however, show poor agreement between data and simulations, which may indicate that the termination of star formation is too abrupt in the models. The mean star-formation rate (SFR) of model galaxies is well-defined and is accurately modelled by a double power law at all redshifts: SFR proportional to $1/(x^{-1.39}+x^{1.33})$, where $x=(t_a-t)/3.0\,$Gyr, $t$ is the age of the stars and $t_a$ is the loopback time to the onset of galaxy formation; above a redshift of unity, this is well approximated by a gamma function: SFR proportional to $x^{1.5}e^{-x}$, where $x=(t_a-t)/2.0\,$Gyr. Individual galaxies, however, show a wide dispersion about this mean. When split by mass, the SFR peaks earlier for high-mass galaxies than for lower-mass ones, and we interpret this downsizing as a mass-dependence in the evolution of the quenched fraction: the SFHs of star-forming galaxies show only a weak mass dependence.Comment: Accepted version of the paper, to appear in MNRAS. Compared to the original version, contains more detail on the post-processing of magnitudes, including a table of rms magnitude errors. SFHs available on Millennium database http://gavo.mpa-garching.mpg.de/MyMillennium

Evaluating system of rice intensification using a modified transplanter: A smart farming solution toward sustainability of paddy fields in Malaysia

This paper presents the study reports on evaluating a new transplanting operation by taking into accounts the interactions between soil, plant, and machine in line with the System of Rice Intensification (SRI) practices. The objective was to modify planting claw (kuku-kambing) of a paddy transplanter in compliance with SRI guidelines to determine the best planting spacing (S), seed rate (G) and planting pattern that results in a maximum number of seedling, tillers per hill, and yield. Two separate experiments were carried out in two different paddy fields, one to determine the best planting spacing (S=4 levels: s1=0.16 m×0.3 m, s2= 0.18 m×0.3 m, s3=0.21 m×0.3 m, and s4=0.24 m×0.3 m) for a specific planting pattern (row mat or scattered planting pattern), and the other to determine the best combination of spacing with seed rate treatments (G=2 levels: g1=75 g/tray, and g2= 240 g/tray). Main SRI management practices such as soil characteristics of the sites, planting depth, missing hill, hill population, the number of seedling per hill, and yield components were evaluated. Results of two-way analysis of variance with three replications showed that spacing, planting pattern and seed rate affected the number of one-seedling in all experiment. It was also observed that the increase in spacing resulted in more tillers and more panicle per plant, however hill population and sterility ratio increased with the decrease in spacing. While the maximum number of panicles were resulted from scattered planting at s4=0.24 m×0.3 m spacing with the seed rate of g1=75 g/tray, the maximum number of one seedling were observed at s4=0.16 m×0.3 m. The highest and lowest yields were obtained from 75 g seeds per tray scattered and 70 g seeds per tray scattered treatment respectively. For all treatments, the result clearly indicates an increase in yield with an increase in spacing.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Adaptive Management Framework for Evaluating and Adjusting Microclimate Parameters in Tropical Greenhouse Crop Production Systems

High operational costs of greenhouse production in hot and humid climate condition due to the initial investments on structure, equipment, and energy necessitate practicing advanced techniques for more efficient use of available resources. This chapter describes design and concepts of an adaptive management framework for evaluating and adjusting optimality degrees and comfort ratios of microclimate parameters, as well as predicting the expected yield in greenhouse cultivation of tomato. A systematic approach is presented for automatic data collection and processing with the objective to produce knowledge‐based information in achieving optimum microclimate for high‐quality and high‐yield tomato. Applications of relevant computer models are demonstrated through case‐study examples for use in an iterative way to simulate and compare different scenarios. The presented framework can contribute to future studies for providing best management decisions such as site selection, optimum growing season, scheduling efficiencies, energy management with different climate control systems, and risk assessments associated with each task

Energy Systems and Applications in Agriculture

[No abstract available

Efficient Ground Surface Displacement Monitoring Using Sentinel-1 Data: Integrating Distributed Scatterers (DS) Identified Using Two-Sample t-Test with Persistent Scatterers (PS)

Combining persistent scatterers (PS) and distributed scatterers (DS) is important for effective displacement monitoring using time-series of SAR data. However, for large stacks of synthetic aperture radar (SAR) data, the DS analysis using existing algorithms becomes a time-consuming process. Moreover, the whole procedure of DS selection should be repeated as soon as a new SAR acquisition is made, which is challenging considering the short repeat-observation of missions such as Sentinel-1. SqueeSAR is an approach for extracting signals from DS, which first applies a spatiotemporal filter on images and optimizes DS, then incorporates information from both optimized DS and PS points into interferometric SAR (InSAR) time-series analysis. In this study, we followed SqueeSAR and implemented a new approach for DS analysis using two-sample t-test to efficiently identify neighboring pixels with similar behaviour. We evaluated the performance of our approach on 50 Sentinel-1 images acquired over Trondheim in Norway between January 2015 and December 2016. A cross check on the number of the identified neighboring pixels using the Kolmogorov–Smirnov (KS) test, which is employed in the SqueeSAR approach, and the t-test shows that their results are strongly correlated. However, in comparison to KS-test, the t-test is less computationally intensive (98% faster). Moreover, the results obtained by applying the tests under different SAR stack sizes from 40 to 10 show that the t-test is less sensitive to the number of image

UAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds

Leaf 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

Review of research progress on soil moisture sensor technology

Soil moisture is directly related to the amount of irrigation in agriculture and influences the yield of crops. Accordingly, a soil moisture sensor is an important tool for measuring soil moisture content. In this study, the previous research conducted in recent 2-3 decades on soil moisture sensors was reviewed and the principles of commonly used soil moisture sensor and their various applications were summarized. Furthermore, the advantages, disadvantages, and influencing factors of various measurement methods employed were compared and analyzed. The improvements were presented by several scholars have established the major applications and performance levels of soil moisture sensors, thereby setting the course for future development. These studies indicated that soil moisture sensors in the future should be developed to achieve high-precision, low-cost, non-destructive, automated, and highly integrated systems. Also, it was indicated that future studies should involve the development of specialized sensors for different applications and scenarios. This review research aimed to provide a certain reference for application departments and scientific researchers in the process of selecting soil moisture sensor products and measuring soil moisture