Correction of in-flight luminosity variations in multispectral UAS images, using a luminosity sensor and camera pair for improved biomass estimation in precision agriculture

2018 ◽  
Author(s):  
Jean-Marc Gilliot ◽  
Luis Mario Domenzain ◽  
Romain Faroux ◽  
Joël Michelin
Keyword(s):  
Precision Agriculture ◽  
Biomass Estimation

scholarly journals Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Agronomy ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 618 ◽  
Author(s):  
Samuel C. Hassler ◽  
Fulya Baysal-Gurel
Keyword(s):  
Precision Agriculture ◽  
Weed Management ◽  
Plant Stress ◽  
Vegetation Indices ◽  
Point Clouds ◽  
Unmanned Aircraft ◽  
Ease Of Use ◽  
Biomass Estimation ◽  
Trade Offs ◽  
Legal Constraints

Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing equipment such as imaging devices (RGB, multispectral, hyperspectral, near infra-red, RGB depth), gripping tools, or spraying equipment. Along with these wide-ranging peripherals and sensing equipment comes a great deal of data processing. Common tools to aid in this processing include vegetation indices, point clouds, machine learning models, and statistical methods. With any emerging technology, there are also a few considerations that need to be analyzed like legal constraints, economic trade-offs, and ease of use. This review then concludes with a discussion on the pros and cons of this technology, along with a brief outlook into future areas of research regarding UAS technology in agriculture.

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 ASSESSMENT OF VARIOUS REMOTE SENSING TECHNOLOGIES IN BIOMASS AND NITROGEN CONTENT ESTIMATION USING AN AGRICULTURAL TEST FIELD

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 137-141 ◽  
Author(s):  
R. Näsi ◽  
N. Viljanen ◽  
J. Kaivosoja ◽  
T. Hakala ◽  
M. Pandžić ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Nitrogen Content ◽  
Precision Agriculture ◽  
Regression Models ◽  
Biomass Estimation ◽  
Test Field ◽  
Linear Regression Models ◽  
Flexible Tool ◽  
Sensing Applications

Multispectral and hyperspectral imaging is usually acquired by satellite and aircraft platforms. Recently, miniaturized hyperspectral 2D frame cameras have showed great potential to precise agriculture estimations and they are feasible to combine with lightweight platforms, such as drones. Drone platform is a flexible tool for remote sensing applications with environment and agriculture. The assessment and comparison of different platforms such as satellite, aircraft and drones with different sensors, such as hyperspectral and RGB cameras is an important task in order to understand the potential of the data provided by these equipment and to select the most appropriate according to the user applications and requirements. In this context, open and permanent test fields are very significant and helpful experimental environment, since they provide a comparative data for different platforms, sensors and users, allowing multi-temporal analyses as well. Objective of this work was to investigate the feasibility of an open permanent test field in context of precision agriculture. Satellite (Sentinel-2), aircraft and drones with hyperspectral and RGB cameras were assessed in this study to estimate biomass, using linear regression models and in-situ samples. Spectral data and 3D information were used and compared in different combinations to investigate the quality of the models. The biomass estimation accuracies using linear regression models were better than 90 % for the drone based datasets. The results showed that the use of spectral and 3D features together improved the estimation model. However, estimation of nitrogen content was less accurate with the evaluated remote sensing sensors. The open and permanent test field showed to be suitable to provide an accurate and reliable reference data for the commercial users and farmers.

scholarly journals Vegetable Crop Biomass Estimation Using Hyperspectral and RGB 3D UAV Data

Agronomy ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1600
Author(s):  
Thomas Astor ◽  
Supriya Dayananda ◽  
Sunil Nautiyal ◽  
Michael Wachendorf
Keyword(s):  
Precision Agriculture ◽  
Prediction Accuracy ◽  
Production Systems ◽  
Predictive Performance ◽  
Point Clouds ◽  
Biomass Estimation ◽  
Vegetable Crops ◽  
Fresh Matter ◽  
Sensory Data ◽  
Increasing Trends

Remote sensing (RS) has been an effective tool to monitor agricultural production systems, but for vegetable crops, precision agriculture has received less interest to date. The objective of this study was to test the predictive performance of two types of RS data—crop height information derived from point clouds based on RGB UAV data, and reflectance information from terrestrial hyperspectral imagery—to predict fresh matter yield (FMY) for three vegetable crops (eggplant, tomato, and cabbage). The study was conducted in an experimental layout in Bengaluru, India, at five dates in summer 2017. The prediction accuracy varied strongly depending on the RS dataset used. For all crops, a good predictive performance with cross-validated prediction error < 10% was achieved. The growth stage of the crops had no significant effect on the prediction accuracy, although increasing trends of an underestimation of FMY with later sampling dates for eggplant and tomato were found. The study proves that an estimation of vegetable FMY using RS data is successful throughout the growing season. Different RS datasets were best for biomass prediction of the three vegetables, indicating that multi-sensory data collection should be preferred to single sensor use, as no one sensor system is superior.

scholarly journals An Automatic UAV Based Segmentation Approach for Pruning Biomass Estimation in Irregularly Spaced Chestnut Orchards

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 308 ◽  
Author(s):  
Salvatore Filippo Di Gennaro ◽  
Carla Nati ◽  
Riccardo Dainelli ◽  
Laura Pastonchi ◽  
Andrea Berton ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Vegetation Index ◽  
Central Italy ◽  
Cost Effective ◽  
Biomass Estimation ◽  
Geometric Features ◽  
Unsupervised Segmentation ◽  
Segmentation Approach ◽  
Pruning Residues ◽  
Strategic Support

The agricultural and forestry sector is constantly evolving, also through the increased use of precision technologies including Remote Sensing (RS). Remotely biomass estimation (WaSfM) in wood production forests is already debated in the literature, but there is a lack of knowledge in quantifying pruning residues from canopy management. The aim of the present study was to verify the reliability of RS techniques for the estimation of pruning biomass through differences in the volume of canopy trees and to evaluate the performance of an unsupervised segmentation methodology as a feasible tool for the analysis of large areas. Remote sensed data were acquired on four uneven-aged and irregularly spaced chestnut orchards in Central Italy by an Unmanned Aerial Vehicle (UAV) equipped with a multispectral camera. Chestnut geometric features were extracted using both supervised and unsupervised crown segmentation and then applying a double filtering process based on Canopy Height Model (CHM) and vegetation index threshold. The results show that UAV monitoring provides good performance in detecting biomass reduction after pruning, despite some differences between the trees’ geometric features. The proposed unsupervised methodology for tree detection and vegetation cover evaluation purposes showed good performance, with a low undetected tree percentage value (1.7%). Comparing crown projected volume reduction extracted by means of supervised and unsupervised approach, R2 ranged from 0.76 to 0.95 among all the sites. Finally, the validation step was assessed by evaluating correlations between measured and estimated pruning wood biomass (Wpw) for single and grouped sites (0.53 < R2 < 0.83). The method described in this work could provide effective strategic support for chestnut orchard management in line with a precision agriculture approach. In the context of the Circular Economy, a fast and cost-effective tool able to estimate the amounts of wastes available as by-products such as chestnut pruning residues can be included in an alternative and virtuous supply chain.

scholarly journals Comparing methods to estimate perennial ryegrass biomass: canopy height and spectral vegetation indices

2020 ◽  
Vol 22 (1) ◽  
pp. 205-225 ◽  
Author(s):  
Gustavo Togeiro de Alckmin ◽  
Lammert Kooistra ◽  
Richard Rawnsley ◽  
Arko Lucieer
Keyword(s):  
Perennial Ryegrass ◽  
Precision Agriculture ◽  
Vegetation Indices ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Biomass Estimation ◽  
Pasture Management ◽  
Accuracy And Precision ◽  
Spectral Vegetation Indices ◽  
Regression Techniques

AbstractPasture management is highly dependent on accurate biomass estimation. Usually, such activity is neglected as current methods are time-consuming and frequently perceived as inaccurate. Conversely, spectral data is a promising technique to automate and improve the accuracy and precision of estimates. Historically, spectral vegetation indices have been widely adopted and large numbers have been proposed. The selection of the optimal index or satisfactory subset of indices to accurately estimate biomass is not trivial and can influence the design of new sensors. This study aimed to compare a canopy-based technique (rising plate meter) with spectral vegetation indices. It examined 97 vegetation indices and 11,026 combinations of normalized ratio indices paired with different regression techniques on 900 pasture biomass data points of perennial ryegrass (Lolium perenne) collected throughout a 1-year period. The analyses demonstrated that the canopy-based technique is superior to the standard normalized difference vegetation index (∆, 115.1 kg DM ha−1 RMSE), equivalent to the best performing normalized ratio index and less accurate than four selected vegetation indices deployed with different regression techniques (maximum ∆, 231.1 kg DM ha−1). When employing the four selected vegetation indices, random forests was the best performing regression technique, followed by support vector machines, multivariate adaptive regression splines and linear regression. Estimate precision was improved through model stacking. In summary, this study demonstrated a series of achievable improvements in both accuracy and precision of pasture biomass estimation, while comparing different numbers of inputs and regression techniques and providing a benchmark against standard techniques of precision agriculture and pasture management.

Environmental Benefits of Precision Agriculture Adoption

2020 ◽  
pp. 637-656 ◽  
Author(s):  
Marco Medici ◽  
Søren Marcus Pedersen ◽  
Giacomo Carli ◽  
Maria Rita Tagliaventi
Keyword(s):  
Environmental Impacts ◽  
Precision Agriculture ◽  
New Technologies ◽  
Environmental Benefits ◽  
Herbicide Application ◽  
Pesticide Application ◽  
Policy Makers ◽  
Application Rates ◽  
Biodiversity Policy ◽  
Lime Application

The purpose of this study is to analyse the environmental benefits of precision agriculture technology adoption obtained from the mitigation of negative environmental impacts of agricultural inputs in modern farming. Our literature review of the environmental benefits related to the adoption of precision agriculture solutions is aimed at raising farmers' and other stakeholders' awareness of the actual environmental impacts from this set of new technologies. Existing studies were categorised according to the environmental impacts of different agricultural activities: nitrogen application, lime application, pesticide application, manure application and herbicide application. Our findings highlighted the effects of the reduction of input application rates and the consequent impacts on climate, soil, water and biodiversity. Policy makers can benefit from the outcomes of this study developing an understanding of the environmental impact of precision agriculture in order to promote and support initiatives aimed at fostering sustainable agriculture.

Precision Agriculture in the Part of Uttarakhand using Geospatial Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 2574-2579
Author(s):  
Divya Uniyal ◽  
◽  
Sourabh Dangwal ◽  
Govind Singh Negi ◽  
Saurabh Purohit ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Geospatial Techniques

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

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