scholarly journals Predicting plant available phosphorus using infrared spectroscopy with consideration for future mobile sensing applications in precision farming

2019 ◽  
Vol 21 (4) ◽  
pp. 737-761 ◽  
Author(s):  
Stefan Pätzold ◽  
Matthias Leenen ◽  
Peter Frizen ◽  
Tobias Heggemann ◽  
Peter Wagner ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Near Infrared ◽  
Model Performance ◽  
Precision Farming ◽  
Future Application ◽  
Available Phosphorus ◽  
Agricultural Practice ◽  
Mir Spectroscopy ◽  
Sensing Applications ◽  
Rapid Discrimination

Abstract Phosphorus (P) fertilisation recommendations rely primarily on soil content of plant available P (Pavl) that vary spatially within farm fields. Spatially optimized P fertilisation for precision farming requires reliable, rapid and non-invasive Pavl determination. This laboratory study aimed to test and to compare visible-near infrared (Vis–NIR) and mid-infrared (MIR) spectroscopy for Pavl prediction with emphasis on future application in precision agriculture. After calibration with the conventional calcium acetate lactate (CAL) extraction method, limitations of Vis–NIRS and MIRS to predict Pavl were evaluated in loess topsoil samples from different fields at six localities. Overall calibration with 477 (Vis–NIRS) and 586 (MIRS) samples yielded satisfactory model performance (R2 0.70 and 0.72; RPD 1.8 and 1.9, respectively). Local Vis–NIRS models yielded better results with R2 up to 0.93 and RPD up to 3.8. For MIRS, results were comparable. However, an overall model to predict Pavl on independent test data partly failed. Sampling date, pre-crop harvest residues and fertilising regime affected model transferability. Varying transferability could partly be explained after deriving the cellulose absorption index from the Vis–NIR spectra. In 62 (Vis–NIRS) and 67% (MIRS) of all samples, prediction matched the correct Pavl content class. Rapid discrimination between high, optimal and low P classes could be carried out on many samples from single fields thus marking an improvement over the common practice. However, Pavl determination by means of IR spectroscopy is not yet satisfactory for determination of precision fertilizer dosage. For introduction into agricultural practice, a standardized sampling protocol is recommended to help achieve reliable spectroscopic Pavl prediction.

THE APPLICATION OF REMOTE SENSING IN PRECISION AGRICULTURE

1970 ◽  
pp. 10-16
Author(s):  
S. Yu. Blokhina
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Spatial Resolution ◽  
Precision Agriculture ◽  
Near Infrared ◽  
Crop Yields ◽  
Temporal Frequency ◽  
Organic Matter Content ◽  
Sensing Applications ◽  
Remote Sensing Applications

The paper provides an overview of foreign literature on the remote sensing applications in precision agriculture. Remote sensing applications in precision agriculture began with sensors for soil organic matter content, and have quickly advanced to include hand held sensors to tractor or aerial or satellite mounted sensors. Wavelengths of electromagnetic radiation initially focused on a few key visible or near infrared bands, and nowadays electromagnetic wavelengths in use range from the ultraviolet to microwave portions of the spectrum. Spectral bandwidth has decreased dramatically with the advent of hyperspectral remote sensing, allowing improved analysis of crop stress, crop biophysical or biochemical characteristics and specific compounds. A variety of spectral indices have been widely implemented within various precision agriculture applications, rather than a focus on only normalized difference vegetation indices. Spatial resolution and temporal frequency of remote sensing imagery has increased significantly, allowing evaluation of soil and crop properties at fine spatial resolution at the expense of increased data storage and processing requirements. At present there is considerable interest in collecting remote sensing for operational management of soil and crop yields, as well as control over the spread of pests and weeds practically in real time.

scholarly journals WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming

2018 ◽  
Vol 10 (9) ◽  
pp. 1423 ◽  
Author(s):  
Inkyu Sa ◽  
Marija Popović ◽  
Raghav Khanna ◽  
Zetao Chen ◽  
Philipp Lottes ◽  
...  
Keyword(s):  
Neural Network ◽  
Precision Agriculture ◽  
Large Scale ◽  
Deep Neural Network ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Precision Farming ◽  
High Fidelity ◽  
Ground Sample ◽  
Global Consistency

The ability to automatically monitor agricultural fields is an important capability in precision farming, enabling steps towards more sustainable agriculture. Precise, high-resolution monitoring is a key prerequisite for targeted intervention and the selective application of agro-chemicals. The main goal of this paper is developing a novel crop/weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Although a map can be generated by processing single segmented images incrementally, this requires additional complex information fusion techniques which struggle to handle high fidelity maps due to their computational costs and problems in ensuring global consistency. Moreover, computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB (red, green, and blue) inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we provide an extensive analysis of 20 trained models, both qualitatively and quantitatively, in order to evaluate the effects of varying input channels and tunable network hyperparameters. Furthermore, we release a large sugar beet/weed aerial dataset with expertly guided annotations for further research in the fields of remote sensing, precision agriculture, and agricultural robotics.

scholarly journals Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning

AI ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 229-241
Author(s):  
Marcelo Chan Fu Wei ◽  
Leonardo Felipe Maldaner ◽  
Pedro Medeiros Netto Ottoni ◽  
José Paulo Molin
Keyword(s):  
Precision Agriculture ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Spectral Band ◽  
Model Performance ◽  
Important Predictor ◽  
Predictor Variables ◽  
Coefficient Of Determination ◽  
Statistical Parameters ◽  
Entire Dataset

Carrot yield maps are an essential tool in supporting decision makers in improving their agricultural practices, but they are unconventional and not easy to obtain. The objective was to develop a method to generate a carrot yield map applying a random forest (RF) regression algorithm on a database composed of satellite spectral data and carrot ground-truth yield sampling. Georeferenced carrot yield sampling was carried out and satellite imagery was obtained during crop development. The entire dataset was split into training and test sets. The Gini index was used to find the five most important predictor variables of the model. Statistical parameters used to evaluate model performance were the root mean squared error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). The five most important predictor variables were the near-infrared spectral band at 92 and 79 days after sowing (DAS), green spectral band at 50 DAS and blue spectral band at 92 and 81 DAS. The RF algorithm applied to the entire dataset presented R2, RMSE and MAE values of 0.82, 2.64 Mg ha−1 and 1.74 Mg ha−1, respectively. The method based on RF regression applied to a database composed of spectral bands proved to be accurate and suitable to predict carrot yield.

scholarly journals Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 27 ◽  
Author(s):  
Athos Agapiou
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Small Scale ◽  
Area Of Interest ◽  
Sensing Applications ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Elevation Model

Red–green–blue (RGB) cameras which are attached in commercial unmanned aerial vehicles (UAVs) can support remote-observation small-scale campaigns, by mapping, within a few centimeter’s accuracy, an area of interest. Vegetated areas need to be identified either for masking purposes (e.g., to exclude vegetated areas for the production of a digital elevation model (DEM) or for monitoring vegetation anomalies, especially for precision agriculture applications. However, while detection of vegetated areas is of great importance for several UAV remote sensing applications, this type of processing can be quite challenging. Usually, healthy vegetation can be extracted at the near-infrared part of the spectrum (approximately between 760–900 nm), which is not captured by the visible (RGB) cameras. In this study, we explore several visible (RGB) vegetation indices in different environments using various UAV sensors and cameras to validate their performance. For this purposes, openly licensed unmanned aerial vehicle (UAV) imagery has been downloaded “as is” and analyzed. The overall results are presented in the study. As it was found, the green leaf index (GLI) was able to provide the optimum results for all case studies.

scholarly journals Remote and Proximal Sensing Applications for Durum Wheat Nutritional Status Detection in Mediterranean Area

Agriculture ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 39
Author(s):  
Marco Fiorentini ◽  
Stefano Zenobi ◽  
Roberto Orsini
Keyword(s):  
Nutritional Status ◽  
Durum Wheat ◽  
Precision Agriculture ◽  
Near Infrared ◽  
Cropping Systems ◽  
Vegetation Indices ◽  
Mediterranean Area ◽  
N Availability ◽  
Proximal Sensing ◽  
Sensing Applications

Combining remote and proximal sensing in agriculture is essential to monitor crop spatial-temporal variability and to provide high-quality prescription maps for the precision agriculture applications. The study showed how different combinations of soil management (no tillage—NT vs. conventional tillage—CT) and nitrogen (N) fertilization levels (0.90 and 180 kg N ha−1) can affect the durum wheat nutritional status and development through vegetation indices computation and proximal sensing tool application. Chlorophyll and N crop content were measured, in addition a proximal sensing tool and multispectral imagery equipped on unmanned aerial vehicle were used. The N input is the key driver for durum wheat development (4.5 ± 0.92 t ha−1 on average), but when it was not provided the NT performed better than CT (2.51 ± 0.22 vs. 1.46 ± 0.28 t ha−1 respectively) in terms of grain yield. This is due to the greater content of organic matter and N availability which characterizes the NT system. The near infrared (NIR) band-based vegetation indices can well detect the durum wheat nutritional status (R2 = 0.70 on average). The showed results can provide an important contribution in the implementation of ago-environmental policies aimed at environmental impact of cereal-based-cropping systems reduction.

scholarly journals Comparison of the Concentration of Risk Elements in Alluvial Soils Determined by pXRF In Situ, in the Laboratory, and by ICP-OES

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 938
Author(s):  
Ladislav Menšík ◽  
Lukáš Hlisnikovský ◽  
Pavel Nerušil ◽  
Eva Kunzová
Keyword(s):  
Precision Agriculture ◽  
Alluvial Soil ◽  
Coefficient Of Determination ◽  
Fast Method ◽  
Agricultural Practice ◽  
Regression Equations ◽  
Icp Oes ◽  
Risk Elements ◽  
The Relationship

The aim of the study was to compare the concentrations of risk elements (As, Cu, Mn, Ni, Pb, Zn) in alluvial soil, which were measured by a portable X-ray fluorescence analyser (pXRF) in situ (FIELD) and in the laboratory (LABORATORY). Subsequently, regression equations were developed for individual elements through the method of construction of the regression model, which compare the results of pXRF with classical laboratory analysis (ICP-OES). The accuracy of the measurement, expressed by the coefficient of determination (R2), was as follows in the case of FIELD–ICP-OES: Pb (0.96), Zn (0.92), As (0.72), Mn (0.63), Cu (0.31) and Ni (0.01). In the case of LABORATORY–ICP-OES, the coefficients had values: Pb (0.99), Zn (0.98), Cu and Mn (0.89), As (0.88), Ni (0.81). A higher dependence of the relationship was recorded between LABORATORY–ICP-OES than between FIELD–ICP-OES. An excellent relationship was recorded for the elements Pb and Zn, both for FIELD and LABORATORY (R2 higher than 0.90). The elements Cu, Mn and As have a worse tightness in the relationship; however, the results of the model have shown its applicability for common use, e.g., in agricultural practice or in monitoring the quality of the environment. Based on our results, we can say that pXRF instruments can provide highly accurate results for the concentration of risk elements in the soil in real time for some elements and meet the principle of precision agriculture: an efficient, accurate and fast method of analysis.

scholarly journals Near-Infrared Tandem Organic Photodiodes for Future Application in Artificial Retinal Implants

2018 ◽  
Vol 30 (51) ◽  
pp. 1804678 ◽  
Author(s):  
Giulio Simone ◽  
Dario Di Carlo Rasi ◽  
Xander de Vries ◽  
Gaël H. L. Heintges ◽  
Stefan C. J. Meskers ◽  
...  
Keyword(s):  
Near Infrared ◽  
Future Application ◽  
Retinal Implants

scholarly journals Discrimination and Content Analysis of Fritillaria Using Near Infrared Spectroscopy

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yu Meng ◽  
Shisheng Wang ◽  
Rui Cai ◽  
Bohai Jiang ◽  
Weijie Zhao
Keyword(s):  
Infrared Spectroscopy ◽  
Quantitative Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Traditional Chinese Herbal Medicine ◽  
Total Alkaloids ◽  
Rapid Discrimination ◽  
Different Origins

Fritillaria is a traditional Chinese herbal medicine which can be used to moisten the lungs. The objective of this study is to develop simple, accurate, and solvent-free methods to discriminate and quantify Fritillaria herbs from seven different origins. Near infrared spectroscopy (NIRS) methods are established for the rapid discrimination of seven different Fritillaria samples and quantitative analysis of their total alkaloids. The scaling to first range method and the partial least square (PLS) method are used for the establishment of qualitative and quantitative analysis models. As a result of evaluation for the qualitative NIR model, the selectivity values between groups are always above 2, and the mistaken judgment rate of fifteen samples in prediction sets was zero. This means that the NIR model can be used to distinguish different species of Fritillaria herbs. The established quantitative NIR model can accurately predict the content of total alkaloids from Fritillaria samples.

scholarly journals Mode Sensitivity Exploration of Silica–Titania Waveguide for Refractive Index Sensing Applications

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7452
Author(s):  
Muhammad A. Butt ◽  
Andrzej Kaźmierczak ◽  
Cuma Tyszkiewicz ◽  
Paweł Karasiński ◽  
Ryszard Piramidowicz
Keyword(s):  
Refractive Index ◽  
High Performance ◽  
Near Infrared ◽  
Sol Gel ◽  
Cost Effective ◽  
Dip Coating ◽  
Infrared Wavelength ◽  
Refractive Index Sensing ◽  
Sensing Applications ◽  
Coating Method

In this paper, a novel and cost-effective photonic platform based on silica–titania material is discussed. The silica–titania thin films were grown utilizing the sol–gel dip-coating method and characterized with the help of the prism-insertion technique. Afterwards, the mode sensitivity analysis of the silica–titania ridge waveguide is investigated via the finite element method. Silica–titania waveguide systems are highly attractive due to their ease of development, low fabrication cost, low propagation losses and operation in both visible and near-infrared wavelength ranges. Finally, a ring resonator (RR) sensor device was modelled for refractive index sensing applications, offering a sensitivity of 230 nm/RIU, a figure of merit (FOM) of 418.2 RIU−1, and Q-factor of 2247.5 at the improved geometric parameters. We believe that the abovementioned integrated photonics platform is highly suitable for high-performance and economically reasonable optical sensing devices.

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