Computers and Electronics in Agriculture

Journal: Computers and Electronics in Agriculture

Abbreviation

Comput. electron. agric.

Publisher

Elsevier

ISSN

0168-1699
1872-7107

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Publications1 - 10 of 22

A comparison of proximal and remote optical sensor platforms for N status estimation in winter wheat
Argento, Francesco; Merz, Quirina Noëmi; Perich, Gregor; et al. (2025)
Computers and Electronics in Agriculture
Monitoring crop N status by means of proximal and remote sensing data can help enhancing N use efficiency at various farm scales. This study compares five optical sensor platforms, commonly used in practice and research, based on their usability and accuracy in measuring crop N status at field level. The data were gathered in 2019 in two sites in northeast Switzerland that were cropped with winter wheat (Triticum aestivum). The optical sensor platforms employed included a Sentinel-2 satellite, two different unmanned aircraft systems (UAS fixed-wing and quadcopter), a tractor-mounted system, and a handheld field spectrometer. We used a power regression to compare the measured crop N uptake with spectral vegetation indices computed from the different sensors. The reported normalized difference red-edge (NDRE) index values were distributed in a broad range from 0.17 to 0.74, with the Sentinel-2 satellite records in the higher part of the range (0.59–0.74) and those of the handheld spectrometer in the low range (0.17–0.29). The study's key finding was the information collected was significantly different across the five sensing platforms, in terms absolute values from the sensors. However, the correlations between NDRE values from all sensors and the measured N uptake were comparably robust, with r > 0.8 a root mean square error ranging from 29 to 37 kg N/ha. Furthermore, the N application maps produced for the satellite and UAS platforms showed that the best compromise between detailed spatial resolution and matching of the working width of the machinery used was achieved by resampling the UAS-based maps at 10 m resolution with the calculation used in this study. We concluded that sensor-based N status assessment across different sensing levels can support the improvement of N use efficiency by allowing a more precise management of in-field variability, with the precondition of having a good calibration for climatic location and variety. However, factors such as the degree of detail needed to capture in-field variability while matching the working width should be evaluated for each specific case.
An R package to calculate potential environmental and human health risks from pesticide applications using the ‘Pesticide Load’ indicator applied in Denmark
Möhring, Niklas; Kudsk, Per; Nistrup Jørgensen, Lise; et al. (2021)
Computers and Electronics in Agriculture
This paper presents and discusses the “PesticideLoadIndicator” package, a new R-package to compute potential environmental and health effects of pesticide applications using the Danish ‘Pesticide Load’ indicator. The implementation in the R Statistical Language makes it easy for researchers, practitioners and institutions to compare potential pesticide risks for a wide range of applications and compute risk indicators at field-, crop-, farm-, regional- or national level. The tool is publicly available. It provides a possibility for the direct integration of pesticide properties needed for indicator computation (ecotoxicity, environmental fate and human health), from the Pesticide Properties Database or other sources and allows users to change default reference values and weights.
‘ShapeCostTUM’: A calculation tool for field geometry dependent cultivation and transport costs
Ptacek, Moritz; Frick, Fabian; Pahl, Hubert; et al. (2024)
Computers and Electronics in Agriculture
The costs of cultivating agricultural fields strongly depend, among other things, on field geometry and farm-field distance. Field size and shape influence farming efficiency at the micro level, which eventually translates into field market prices. We present a calculation tool capable of quantifying multiple categories of variable costs at the field level, considering field size, field shape, cropping plan, and transport on roads. Our tool calculates field geometry-dependent cultivation and transport costs, i.e., costs from working steps directly affected by field geometry or farm-field distance. The tool combines several up-to-date datasets on crop production and farm machinery. We present a typical use case to demonstrate the versatility of our tool. We assess the relevance of field geometry on cultivation and transport costs for both arable- and grassland and provide an overview of the sensitivity of our calculation method to several input variables. The results reveal that cultivation and transport costs are highly dependent on field shape and field size. Costs are influenced by various factors, i.e., the type of farming, the cropping rotation, the machinery used, and the underlying prices for fuel and labour. Transport costs depend on farm-field distance and road quality. Enclaves within a field increase the costs of cultivating the main plot. Developed initially for compensation of Bavarian farmers for sub-optimal field geometry after infrastructure projects, several other potential scientific and non-scientific use cases are presented.
A framework for the management of agricultural resources with automated aerial imagery detection
Saldaña Ochoa, Karla; Zifeng, Guo (2019)
Computers and Electronics in Agriculture
Virtual cold chain method to model the postharvest temperature history and quality evolution of fresh fruit – A case study for citrus fruit packed in a single carton
Wu, Wentao; Cronjé, Paul; Nicolai, Bart; et al. (2018)
Computers and Electronics in Agriculture
NU-Spidercam: A large-scale, cable-driven, integrated sensing and robotic system for advanced phenotyping, remote sensing, and agronomic research
Bai, Geng; Ge, Yufeng; Scoby, David; et al. (2019)
Computers and Electronics in Agriculture
Field-based high throughput plant phenotyping has recently gained increased interest in the efforts to bridge the genotyping and phenotyping gap and accelerate plant breeding for crop improvement. In this paper, we introduce a large-scale, integrated robotic cable-driven sensing system developed at University of Nebraska for field phenotyping research. It is constructed to collect data from a 0.4ha field. The system has a sensor payload of 30kg and offers the flexibility to integrate user defined sensing modules. Currently it integrates a four-band multispectral camera, a thermal infrared camera, a 3D scanning LiDAR, and a portable visible near-infrared spectrometer for plant measurements. Software is designed and developed for instrument control, task planning, and motion control, which enables precise and flexible phenotypic data collection at the plot level. The system also includes a variable-rate subsurface drip irrigation to control water application rates, and an automated weather station to log environmental variables. The system has been in operation for the 2017 and 2018 growing seasons. We demonstrate that the system is reliable and robust, and that fully automated data collection is feasible. Sensor and image data are of high quality in comparison to the ground truth measurements, and capture various aspects of plant traits such as height, ground cover and spectral reflectance. We present two novel datasets enabled by the system, including a plot-level thermal infrared image time-series during a day, and the signal of solar induced chlorophyll fluorescence from canopy reflectance. It is anticipated that the availability of this automated phenotyping system will benefit research in field phenotyping, remote sensing, agronomy, and related disciplines.
The multiple roles of environmental data visualization in evaluating alternative forest management strategies
Meitner, Michael J.; Sheppard, Stephen R.J.; Cavens, Duncan; et al. (2005)
Computers and Electronics in Agriculture
EOdal: An open-source Python package for large-scale agroecological research using Earth Observation and gridded environmental data
Graf, Lukas Valentin; Perich, Gregor; Aasen, Helge (2022)
Computers and Electronics in Agriculture
Earth Observation by means of remote sensing imagery and gridded environmental data opens tremendous opportunities for systematic capture, quantification and interpretation of plant–environment interactions through space and time. The acquisition, maintenance and processing of these data sources, however, requires a unified software framework for efficient and scalable integrated spatio-temporal analysis taking away the burden of data and file handling from the user. Existing software products either cover only parts of these requirements, exhibit a high degree of complexity, or are closed-source, which limits reproducibility of research. With the open-source Python library EOdal (Earth Observation Data Analysis Library) we propose a novel software that enables the development of fully reproducible spatial data science chains through the strict use of open-source developments. Thanks to its modular design, EOdal enables advanced data warehousing especially for remote sensing data, sophisticated spatio-temporal analysis and intersection of different data sources, as well as nearly unlimited expandability through application programming interfaces (APIs).
Mapping the dynamics of intensive forage acreage during 2008–2022 in Google Earth Engine using time series Landsat images and a phenology-based algorithm
Zhao, Haile; Zhou, Yi; Zhang, Guoliang; et al. (2024)
Computers and Electronics in Agriculture
The increasing demand for livestock feed in China has led to a remarkable increase in forage acreage, particularly in forage fields under intensive agricultural management. However, there are no maps currently available to demonstrate and document the spatial and temporal patterns of the continuous expansion of intensive forage. Here we proposed a pixel- and phenology-based approach to map intensive forage by utilizing time series Landsat images and the Google Earth Engine platform. Given the challenge of accurately identifying intensive forage fields in diverse regions using fixed multi-temporal Landsat data, along with the distinct phenological characteristics of intensive forage fields that undergo multiple annual harvests, we proposed an innovative data mining approach based on dynamic monthly composite images (DMCI). This approach aims to enhance the algorithm's efficiency and precision in identifying intensive forage. As a pilot study, we analyzed all available Landsat images (1732 scenes) from 2008 to 2022 in a county located in the farming-pastoral ecotone of northern China and tracked the historical dynamics of intensive forage expansion over five epochs at three-year intervals. The accuracy assessment of the intensive forage maps for the five epochs using validation sample points showed that the overall accuracy and Mathews correlation coefficient ranged from 97.1 % to 98.9 % and 0.93 to 0.98 respectively. The intensive forage acreage in the study area exhibited a dramatic expansion from 2008 to 2022, particularly after the 2012s. This study demonstrates the potential of our DMCI- and phenology-based algorithm and time series Landsat images for tracking the dynamics of intensive forage acreage at 30-m resolution in temperate single-cropping regions.
Gait determination and activity measurement in horses using an accelerometer
Burla, Joan-Bryce; Ostertag, Anic; Schulze Westerath, Heike; et al. (2014)
Computers and Electronics in Agriculture

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