scholarly journals Evaluating the potential of consumer-grade smart cameras for low-cost stereo-photogrammetric Crop-Surface Monitoring

2014 ◽  
Vol XL-7 ◽  
pp. 43-49 ◽  
Author(s):  
S. Brocks ◽  
G. Bareth
Keyword(s):  
Plant Height ◽  
Precision Agriculture ◽  
Low Cost ◽  
Image Acquisition ◽  
Coefficient Of Determination ◽  
Average Height ◽  
Smart Cameras ◽  
Growth Variability ◽  
Growing Period ◽  
Surface Models

Crop-Surface-Models (CSMs) are a useful tool for monitoring in-field crop growth variability, thus enabling precision agriculture which is necessary for achieving higher agricultural yields. This contribution provides a first assessment on the suitability of using consumer-grade smart cameras as sensors for the stereoscopic creation of crop-surface models using oblique imagery acquired from ground-based positions. An application that automates image acquisition and transmission was developed. Automated image acquisition took place throughout the growing period of barley in 2013. For three dates where both automated image acquisition and manual measurements of plant height were available, CSMs were generated using a combination of AgiSoft PhotoScan and Esri ArcGIS. The coefficient of determination <i>R</i><sup>2</sup> between the average of the manually measured plant heights per plots and the average height of the developed crop surface models was 0.61 (<i>n</i> = 24). The overall correlation between the manually measured heights and the CSM-derived heights is 0.78. The average per plot of the manually measured plant heights in the timeframe covered by the generated CSMs range from 19 to 95 cm, while the average plant height per plot of the generated CSMs range from 2.1 to 69 cm. These first results show that the presented approach is feasible.

scholarly journals MULTI-TEMPORAL CROP SURFACE MODELS COMBINED WITH THE RGB VEGETATION INDEX FROM UAV-BASED IMAGES FOR FORAGE MONITORING IN GRASSLAND

2016 ◽  
Vol XLI-B1 ◽  
pp. 991-998 ◽  
Author(s):  
M. Possoch ◽  
S. Bieker ◽  
D. Hoffmeister ◽  
A. Bolten ◽  
J. Schellberg ◽  
...  
Keyword(s):  
Plant Height ◽  
Precision Agriculture ◽  
Dry Matter ◽  
Vegetation Index ◽  
Low Cost ◽  
Nutrient Use Efficiency ◽  
Height Distribution ◽  
Corrected Image ◽  
Surface Models ◽  
Multi Temporal

Remote sensing of crop biomass is important in regard to precision agriculture, which aims to improve nutrient use efficiency and to develop better stress and disease management. In this study, multi-temporal crop surface models (CSMs) were generated from UAV-based dense imaging in order to derive plant height distribution and to determine forage mass. The low-cost UAV-based RGB imaging was carried out in a grassland experiment at the University of Bonn, Germany, in summer 2015. The test site comprised three consecutive growths including six different nitrogen fertilizer levels and three replicates, in sum 324 plots with a size of 1.5×1.5 m. Each growth consisted of six harvesting dates. RGB-images and biomass samples were taken at twelve dates nearly biweekly within two growths between June and September 2015. Images were taken with a DJI Phantom 2 in combination of a 2D Zenmuse gimbal and a GoPro Hero 3 (black edition). Overlapping images were captured in 13 to 16 m and overview images in approximately 60 m height at 2 frames per second. The RGB vegetation index (RGBVI) was calculated as the normalized difference of the squared green reflectance and the product of blue and red reflectance from the non-calibrated images. The post processing was done with Agisoft PhotoScan Professional (SfM-based) and Esri ArcGIS. 14 ground control points (GCPs) were located in the field, distinguished by 30 cm × 30 cm markers and measured with a RTK-GPS (HiPer Pro Topcon) with 0.01 m horizontal and vertical precision. The errors of the spatial resolution in x-, y-, z-direction were in a scale of 3-4 cm. From each survey, also one distortion corrected image was georeferenced by the same GCPs and used for the RGBVI calculation. The results have been used to analyse and evaluate the relationship between estimated plant height derived with this low-cost UAV-system and forage mass. Results indicate that the plant height seems to be a suitable indicator for forage mass. There is a robust correlation of crop height related with dry matter (R² = 0.6). The RGBVI seems not to be a suitable indicator for forage mass in grassland, although the results provided a medium correlation by combining plant height and RGBVI to dry matter (R² = 0.5).

scholarly journals MULTI-TEMPORAL CROP SURFACE MODELS COMBINED WITH THE RGB VEGETATION INDEX FROM UAV-BASED IMAGES FOR FORAGE MONITORING IN GRASSLAND

2016 ◽  
Vol XLI-B1 ◽  
pp. 991-998 ◽  
Author(s):  
M. Possoch ◽  
S. Bieker ◽  
D. Hoffmeister ◽  
A. Bolten ◽  
J. Schellberg ◽  
...  
Keyword(s):  
Plant Height ◽  
Precision Agriculture ◽  
Dry Matter ◽  
Vegetation Index ◽  
Low Cost ◽  
Nutrient Use Efficiency ◽  
Height Distribution ◽  
Corrected Image ◽  
Surface Models ◽  
Multi Temporal

Remote sensing of crop biomass is important in regard to precision agriculture, which aims to improve nutrient use efficiency and to develop better stress and disease management. In this study, multi-temporal crop surface models (CSMs) were generated from UAV-based dense imaging in order to derive plant height distribution and to determine forage mass. The low-cost UAV-based RGB imaging was carried out in a grassland experiment at the University of Bonn, Germany, in summer 2015. The test site comprised three consecutive growths including six different nitrogen fertilizer levels and three replicates, in sum 324 plots with a size of 1.5×1.5 m. Each growth consisted of six harvesting dates. RGB-images and biomass samples were taken at twelve dates nearly biweekly within two growths between June and September 2015. Images were taken with a DJI Phantom 2 in combination of a 2D Zenmuse gimbal and a GoPro Hero 3 (black edition). Overlapping images were captured in 13 to 16 m and overview images in approximately 60 m height at 2 frames per second. The RGB vegetation index (RGBVI) was calculated as the normalized difference of the squared green reflectance and the product of blue and red reflectance from the non-calibrated images. The post processing was done with Agisoft PhotoScan Professional (SfM-based) and Esri ArcGIS. 14 ground control points (GCPs) were located in the field, distinguished by 30 cm × 30 cm markers and measured with a RTK-GPS (HiPer Pro Topcon) with 0.01 m horizontal and vertical precision. The errors of the spatial resolution in x-, y-, z-direction were in a scale of 3-4 cm. From each survey, also one distortion corrected image was georeferenced by the same GCPs and used for the RGBVI calculation. The results have been used to analyse and evaluate the relationship between estimated plant height derived with this low-cost UAV-system and forage mass. Results indicate that the plant height seems to be a suitable indicator for forage mass. There is a robust correlation of crop height related with dry matter (R² = 0.6). The RGBVI seems not to be a suitable indicator for forage mass in grassland, although the results provided a medium correlation by combining plant height and RGBVI to dry matter (R² = 0.5).

scholarly journals Terrestrial laser scanning for plant height measurement and biomass estimation of maize

2014 ◽  
Vol XL-7 ◽  
pp. 181-187 ◽  
Author(s):  
N. Tilly ◽  
D. Hoffmeister ◽  
H. Schiedung ◽  
C. Hütt ◽  
J. Brands ◽  
...  
Keyword(s):  
Plant Height ◽  
Precision Agriculture ◽  
Laser Scanning ◽  
Point Clouds ◽  
Time Frame ◽  
Coefficient Of Determination ◽  
Biomass Estimation ◽  
Height Measurement ◽  
Growing Period ◽  
Non Destructive

Over the last decades, the role of remote sensing gained in importance for monitoring applications in precision agriculture. A key factor for assessing the development of crops during the growing period is the actual biomass. As non-destructive methods of directly measuring biomass do not exist, parameters like plant height are considered as estimators. In this contribution, first results of multitemporal surveys on a maize field with a terrestrial laser scanner are shown. The achieved point clouds are interpolated to generate Crop Surface Models (CSM) that represent the top canopy. These CSMs are used for visualizing the spatial distribution of plant height differences within the field and calculating plant height above ground with a high resolution of 1 cm. In addition, manual measurements of plant height were carried out corresponding to each TLS campaign to verify the results. The high coefficient of determination (R² = 0.93) between both measurement methods shows the applicability of the presented approach. The established regression model between CSM-derived plant height and destructively measured biomass shows a varying performance depending on the considered time frame during the growing period. This study shows that TLS is a suitable and promising method for measuring plant height of maize. Moreover, it shows the potential of plant height as a non-destructive estimator for biomass in the early growing period. However, challenges are the non-linear development of plant height and biomass over the whole growing period.

scholarly journals EVALUATING DENSE 3D RECONSTRUCTION SOFTWARE PACKAGES FOR OBLIQUE MONITORING OF CROP CANOPY SURFACE

2016 ◽  
Vol XLI-B5 ◽  
pp. 785-789 ◽  
Author(s):  
S. Brocks ◽  
G. Bareth
Keyword(s):  
3D Reconstruction ◽  
Field Experiment ◽  
Point Clouds ◽  
Crop Growth ◽  
Multiple Time ◽  
Smart Cameras ◽  
Growth Variability ◽  
Growing Period ◽  
Software Packages ◽  
Dense 3D Reconstruction

Crop Surface Models (CSMs) are 2.5D raster surfaces representing absolute plant canopy height. Using multiple CMSs generated from data acquired at multiple time steps, a crop surface monitoring is enabled. This makes it possible to monitor crop growth over time and can be used for monitoring in-field crop growth variability which is useful in the context of high-throughput phenotyping. This study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale. A summer barley field experiment located at the Campus Klein-Altendorf of University of Bonn was observed by acquiring stereo images from an oblique angle using consumer-grade smart cameras. Two such cameras were mounted at an elevation of 10 m and acquired images for a period of two months during the growing period of 2014. The field experiment consisted of nine barley cultivars that were cultivated in multiple repetitions and nitrogen treatments. Manual plant height measurements were carried out at four dates during the observation period. The software packages Agisoft PhotoScan, VisualSfM with CMVS/PMVS2 and SURE are investigated. The point clouds are georeferenced through a set of ground control points. Where adequate results are reached, a statistical analysis is performed.

scholarly journals UROCHLOA GRASS GROWTH AS A FUNCTION OF NITROGEN AND PHOSPHORUS FERTILIZATION

2021 ◽  
Vol 39 (4) ◽  
pp. 492-504
Author(s):  
Maurício Luiz de Mello Vieira LEITE ◽  
Leandro Ricardo Rodrigues de LUCENA ◽  
Raul Caco Alves BEZERRA ◽  
Mirna Clarissa Rodrigues de ALMEIDA ◽  
Vicente José Laamon Pinto SIMÕES
Keyword(s):  
Plant Height ◽  
Low Cost ◽  
Perennial Grass ◽  
Coefficient Of Determination ◽  
Phosphorus Fertilization ◽  
Gamma Model ◽  
Nitrogen And Phosphorus ◽  
Phosphate Fertilization ◽  
Presence And Absence ◽  
Grass Growth

The urochloa grass (Urochloa mosambicensis) is a perennial grass, C4 plant, with a high photosynthetic rate and CO2 fixation, persistent to water deficit, adapted to a wide diversity of soils and hot climate regions. Thus, the objective was to evaluate the urochloa grass growth and define the best models to estimate plant height as a function of nitrogen and phosphate fertilization. The experimental design was completely randomized, in the 2 x 2 factorial design (presence and absence of nitrogen presence and absence of phosphorus), with four replications. Was used a dose of nitrogen and phosphorus equivalent to 100 kg.ha-1 of N and 150 kg.ha-1 of P2O5, respectively. The following models were used: linear, power, gamma andlogistic to estimate plant height as a function of the following explanatory variables: days after planting, nitrogen and phosphorus doses. The criteria used to determine the best model(s) were as follows: higher adjusted coefficient of determination, lower Akaike information criterion, lower sum of square of residuals and high Willmott index. The plant height in the absence of nitrogen and phosphorus and when applied 100 kg.ha-1 of N and 150 kg.ha-1 of P2O5 was estimated more accurately by the Gamma model with high power of explanation. The adoption of the Gamma model allows to estimate the U.  mosambicensis plant height, in a non-destructive manner, with high precision, speed and low cost, depending of age plant and nitrogen and phosphate fertilization.

scholarly journals EVALUATING DENSE 3D RECONSTRUCTION SOFTWARE PACKAGES FOR OBLIQUE MONITORING OF CROP CANOPY SURFACE

2016 ◽  
Vol XLI-B5 ◽  
pp. 785-789
Author(s):  
S. Brocks ◽  
G. Bareth
Keyword(s):  
3D Reconstruction ◽  
Field Experiment ◽  
Point Clouds ◽  
Crop Growth ◽  
Multiple Time ◽  
Smart Cameras ◽  
Growth Variability ◽  
Growing Period ◽  
Software Packages ◽  
Dense 3D Reconstruction

Crop Surface Models (CSMs) are 2.5D raster surfaces representing absolute plant canopy height. Using multiple CMSs generated from data acquired at multiple time steps, a crop surface monitoring is enabled. This makes it possible to monitor crop growth over time and can be used for monitoring in-field crop growth variability which is useful in the context of high-throughput phenotyping. This study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale. A summer barley field experiment located at the Campus Klein-Altendorf of University of Bonn was observed by acquiring stereo images from an oblique angle using consumer-grade smart cameras. Two such cameras were mounted at an elevation of 10 m and acquired images for a period of two months during the growing period of 2014. The field experiment consisted of nine barley cultivars that were cultivated in multiple repetitions and nitrogen treatments. Manual plant height measurements were carried out at four dates during the observation period. The software packages Agisoft PhotoScan, VisualSfM with CMVS/PMVS2 and SURE are investigated. The point clouds are georeferenced through a set of ground control points. Where adequate results are reached, a statistical analysis is performed.

scholarly journals The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Supakorn Harnsoongnoen ◽  
Nuananong Jaroensuk
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Low Cost ◽  
Measuring System ◽  
Coefficient Of Determination ◽  
Water Displacement ◽  
Non Invasive ◽  
Future Potential ◽  
Non Destructive ◽  
Egg Freshness

AbstractThe water displacement and flotation are two of the most accurate and rapid methods for grading and assessing freshness of agricultural products based on density determination. However, these techniques are still not suitable for use in agricultural inspections of products such as eggs that absorb water which can be considered intrusive or destructive and can affect the result of measurements. Here we present a novel proposal for a method of non-destructive, non-invasive, low cost, simple and real—time monitoring of the grading and freshness assessment of eggs based on density detection using machine vision and a weighing sensor. This is the first proposal that divides egg freshness into intervals through density measurements. The machine vision system was developed for the measurement of external physical characteristics (length and breadth) of eggs for evaluating their volume. The weighing system was developed for the measurement of the weight of the egg. Egg weight and volume were used to calculate density for grading and egg freshness assessment. The proposed system could measure the weight, volume and density with an accuracy of 99.88%, 98.26% and 99.02%, respectively. The results showed that the weight and freshness of eggs stored at room temperature decreased with storage time. The relationship between density and percentage of freshness was linear for the all sizes of eggs, the coefficient of determination (R2) of 0.9982, 0.9999, 0.9996, 0.9996 and 0.9994 for classified egg size classified 0, 1, 2, 3 and 4, respectively. This study shows that egg freshness can be determined through density without using water to test for water displacement or egg flotation which has future potential as a measuring system important for the poultry industry.

scholarly journals Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 256
Author(s):  
Pengfei Han ◽  
Han Mei ◽  
Di Liu ◽  
Ning Zeng ◽  
Xiao Tang ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Short Term Memory ◽  
Hot Spot ◽  
Low Cost ◽  
Field Evaluation ◽  
Pollution Monitoring ◽  
Coefficient Of Determination ◽  
Air Pollution Monitoring ◽  
National Monitoring

Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3–0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

Sign in / Sign up

Export Citation Format

Share Document