scholarly journals Application of RGB Images Obtained by UAV in Coffee Farming

2021 ◽  
Vol 13 (12) ◽  
pp. 2397
Author(s):  
Brenon Diennevam Souza Barbosa ◽  
Gabriel Araújo e Silva Ferraz ◽  
Luana Mendes dos Santos ◽  
Lucas Santos Santana ◽  
Diego Bedin Marin ◽  
...  
Keyword(s):  
Low Cost ◽  
Pearson Correlation ◽  
Vegetation Indices ◽  
Digital Camera ◽  
Production Cycle ◽  
Practical Application ◽  
Area Index ◽  
Rgb Images ◽  
Pearson Correlation Analysis ◽  
Coffee Farming

The objective of this study was to evaluate the potential of the practical application of unmanned aerial vehicles and RGB vegetation indices (VIs) in the monitoring of a coffee crop. The study was conducted in an experimental coffee field over a 12-month period. An RGB digital camera coupled to a UAV was used. Nine VIs were evaluated in this study. These VIs were subjected to a Pearson correlation analysis with the leaf area index (LAI), and subsequently, the VIs with higher R2 values were selected. The LAI was estimated by plant height and crown diameter values obtained by imaging, which were correlated with these values measured in the field. Among the VIs evaluated, MPRI (0.31) and GLI (0.41) presented greater correlation with LAI; however, the correlation was weak. Thematic maps of VIs in the evaluated period showed variability present in the crop. The evolution of weeds in the planting rows was noticeable with both VIs, which can help managers to make the decision to start crop management, thus saving resources. The results show that the use of low-cost UAVs and RGB cameras has potential for monitoring the coffee production cycle, providing producers with information in a more accurate, quick and simple way.

scholarly journals Combined Use of Low-Cost Remote Sensing Techniques and δ13C to Assess Bread Wheat Grain Yield under Different Water and Nitrogen Conditions

Agronomy ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 285 ◽  
Author(s):  
Salima Yousfi ◽  
Adrian Gracia-Romero ◽  
Nassim Kellas ◽  
Mohamed Kaddour ◽  
Ahmed Chadouli ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Grain Yield ◽  
Water Status ◽  
Low Cost ◽  
Vegetation Indices ◽  
Carbon Isotopic Composition ◽  
Canopy Temperature ◽  
N Fertilization ◽  
Canopy Area ◽  
Rgb Images

Vegetation indices and canopy temperature are the most usual remote sensing approaches to assess cereal performance. Understanding the relationships of these parameters and yield may help design more efficient strategies to monitor crop performance. We present an evaluation of vegetation indices (derived from RGB images and multispectral data) and water status traits (through the canopy temperature, stomatal conductance and carbon isotopic composition) measured during the reproductive stage for genotype phenotyping in a study of four wheat genotypes growing under different water and nitrogen regimes in north Algeria. Differences among the cultivars were reported through the vegetation indices, but not with the water status traits. Both approximations correlated significantly with grain yield (GY), reporting stronger correlations under support irrigation and N-fertilization than the rainfed or the no N-fertilization conditions. For N-fertilized trials (irrigated or rainfed) water status parameters were the main factors predicting relative GY performance, while in the absence of N-fertilization, the green canopy area (assessed through GGA) was the main factor negatively correlated with GY. Regression models for GY estimation were generated using data from three consecutive growing seasons. The results highlighted the usefulness of vegetation indices derived from RGB images predicting GY.

scholarly journals High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 655
Author(s):  
Marta García-Fernández ◽  
Enoc Sanz-Ablanedo ◽  
José Ramón Rodríguez-Pérez
Keyword(s):  
Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Amino Nitrogen ◽  
Correlation Coefficients ◽  
Total Polyphenol ◽  
Grape Quality ◽  
Rgb Images ◽  
Index 2 ◽  
Maturation Index

Remotesensing techniques can help reduce time and resources spent collecting samples of crops and analyzing quality variables. The main objective of this work was to demonstrate that it is possible to obtain information on the distribution of must quality variables from conventional photographs. Georeferenced berry samples were collected and analyzed in the laboratory, and RGB images were taken using a low-cost drone from which an orthoimage was made. Transformation equations were calculated to obtain absolute reflectances for the different bands and to calculate 10 vegetation indices plus two new proposed indices. Correlations for the 12 indices with values for 15 must quality variables were calculated in terms of Pearson’s correlation coefficients. Significant correlations were obtained for 100-berries weight (0.77), malic acid (−0.67), alpha amino nitrogen (−0.59), phenolic maturation index (0.69), and the total polyphenol index (0.62), with 100-berries weight and the total polyphenol index obtaining the best results in the proposed RGB-based vegetation index 2 and RGB-based vegetation index 3. Our findings indicate that must variables important for the production of quality wines can be related to the RGB bands in conventional digital images, potentially improving and aiding management and increasing productivity.

scholarly journals USING CALIBRATED RGB IMAGERY FROM LOW-COST UAVS FOR GRASSLAND MONITORING: CASE STUDY AT THE RENGEN GRASSLAND EXPERIMENT (RGE), GERMANY

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 229-233 ◽  
Author(s):  
U. Lussem ◽  
J. Hollberg ◽  
J. Menne ◽  
J. Schellberg ◽  
G. Bareth
Keyword(s):  
Precision Agriculture ◽  
Low Cost ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Fertilizer Application ◽  
Real Time Analysis ◽  
Rgb Images ◽  
Intensively Managed ◽  
Cost Efficient

Monitoring the spectral response of intensively managed grassland throughout the growing season allows optimizing fertilizer inputs by monitoring plant growth. For example, site-specific fertilizer application as part of precision agriculture (PA) management requires information within short time. But, this requires field-based measurements with hyper- or multispectral sensors, which may not be feasible on a day to day farming practice. Exploiting the information of RGB images from consumer grade cameras mounted on unmanned aerial vehicles (UAV) can offer cost-efficient as well as near-real time analysis of grasslands with high temporal and spatial resolution. The potential of RGB imagery-based vegetation indices (VI) from consumer grade cameras mounted on UAVs has been explored recently in several. However, for multitemporal analyses it is desirable to calibrate the digital numbers (DN) of RGB-images to physical units. In this study, we explored the comparability of the RGBVI from a consumer grade camera mounted on a low-cost UAV to well established vegetation indices from hyperspectral field measurements for applications in grassland. The study was conducted in 2014 on the Rengen Grassland Experiment (RGE) in Germany. Image DN values were calibrated into reflectance by using the Empirical Line Method (Smith & Milton 1999). Depending on sampling date and VI the correlation between the UAV-based RGBVI and VIs such as the NDVI resulted in varying R2 values from no correlation to up to 0.9. These results indicate, that calibrated RGB-based VIs have the potential to support or substitute hyperspectral field measurements to facilitate management decisions on grasslands.

Estimating Rice LAI Based on Digital Camera and Image Processing

2012 ◽  
Vol 500 ◽  
pp. 586-591 ◽  
Author(s):  
Gui Ying Pan ◽  
Lian Qing Zhou ◽  
Zhou Shi
Keyword(s):  
Image Processing ◽  
Low Cost ◽  
Digital Camera ◽  
Canopy Gap ◽  
Transmission Model ◽  
Distribution Model ◽  
Leaf Angle ◽  
Angle Distribution ◽  
Area Index ◽  
Gap Fraction

A fast, low-cost method for rice canopy leaf area index (LAI) estimation is proposed. Take photos of rice canopy with a 57° view angle from above using a common digital camera. Extract canopy gap fraction by digital image processing technology. Then LAI can be estimated using canopy gap fraction based on optical transmission model and Leaf angle distribution model. AccuPAR-LP80 and direct measurement were employed to provide Comparative data. Comparison of the three methods, we obtained high correlation coefficients (R²≥0.6). The result shows that the method is especially suitable for estimating LAI in early growth stage of rice.

scholarly journals Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models

2021 ◽  
Vol 12 ◽  
Author(s):  
Sayantan Sarkar ◽  
A. Ford Ramsey ◽  
Alexandre-Brice Cazenave ◽  
Maria Balota
Keyword(s):  
Soil Moisture ◽  
Vegetation Indices ◽  
Color Space ◽  
Digital Camera ◽  
Logistic Models ◽  
Cost Effective ◽  
Moisture Stress ◽  
Breeding Lines ◽  
Visual Rating ◽  
Rgb Images

Peanut (Arachis hypogaea L.) is an important crop for United States agriculture and worldwide. Low soil moisture is a major constraint for production in all peanut growing regions with negative effects on yield quantity and quality. Leaf wilting is a visual symptom of low moisture stress used in breeding to improve stress tolerance, but visual rating is slow when thousands of breeding lines are evaluated and can be subject to personnel scoring bias. Photogrammetry might be used instead. The objective of this article is to determine if color space indices derived from red-green-blue (RGB) images can accurately estimate leaf wilting for breeding selection and irrigation triggering in peanut production. RGB images were collected with a digital camera proximally and aerially by a unmanned aerial vehicle during 2018 and 2019. Visual rating was performed on the same days as image collection. Vegetation indices were intensity, hue, saturation, lightness, a∗, b∗, u∗, v∗, green area (GA), greener area (GGA), and crop senescence index (CSI). In particular, hue, a∗, u∗, GA, GGA, and CSI were significantly (p ≤ 0.0001) associated with leaf wilting. These indices were further used to train an ordinal logistic regression model for wilting estimation. This model had 90% accuracy when images were taken aerially and 99% when images were taken proximally. This article reports on a simple yet key aspect of peanut screening for tolerance to low soil moisture stress and uses novel, fast, cost-effective, and accurate RGB-derived models to estimate leaf wilting.

scholarly journals Nitrogen variable rate in pastures using optical sensors

2019 ◽  
Vol 40 (6Supl2) ◽  
pp. 2917 ◽  
Author(s):  
Lucas de Paula Corrêdo ◽  
Francisco de Assis de Carvalho Pinto ◽  
Domingos Savio Queiroz ◽  
Domingos Sárvio Magalhães Valente ◽  
Flora Maria de Melo Villar
Keyword(s):  
Optical Sensors ◽  
Precision Agriculture ◽  
Low Cost ◽  
Vegetation Indices ◽  
Digital Camera ◽  
Dry Mass ◽  
Chlorophyll Meter ◽  
Randomized Design ◽  
Leaf N Content ◽  
N Management

The use of optical sensors to identify the nutritional needs of agricultural crops has been the subject of several studies using precision agriculture techniques. In this work, we sought to overcome the lack of research evaluating the use of these techniques in the management of nitrogen (N) fertilizer in pastures. We evaluated the methodology of the nitrogen sufficiency index (NSI) in N management at variable rates (VR) using a portable chlorophyll meter. In addition, the use of color vegetation indices generated from a digital camera was evaluated as a low-cost alternative. The work was conducted in four management cycles at different times of year, evaluating the productivity and quality of Brachiaria brizantha cv. Xaraés grass. Three NSIs (0.85, 0.90 and 0.95) were evaluated, applying complementary doses of N according to the response of monitored plots using a chlorophyll meter and comparing the productivity and leaf N content of these treatments to the reference treatment (TREF), which received a single dose of N (150 kg ha-1). Together with these treatments, plots without N application (control) were analyzed, totaling five treatments with six replications in a completely randomized design. The dry mass productivity and N leaf concentration of the VR treatments were statistically equal to TREF in all management cycles (P < 0.05). Most color vegetation indices correlated significantly (P < 0.05) to the chlorophyll readings. The use of NSI methodology in pastures allows the same productivity gains, with significant input savings. In addition, the use of digital cameras presents itself as a viable alternative to monitoring the N status in pastures.

Feasibility study of technology-enabled prevention intervention for children and families (Preprint)

2019 ◽  
Author(s):  
Nikki Theofanopoulou ◽  
Katherine Isbister ◽  
Julian Edbrooke-Childs ◽  
Petr Slovák
Keyword(s):  
Emotion Regulation ◽  
Pilot Study ◽  
Low Cost ◽  
Digital Camera ◽  
Well Being ◽  
Structured Interview ◽  
Object A ◽  
Intervention Delivery ◽  
Parents And Children

BACKGROUND A common challenge within psychiatry and prevention science more broadly is the lack of effective, engaging, and scale-able mechanisms to deliver psycho-social interventions for children, especially beyond in-person therapeutic or school-based contexts. Although digital technology has the potential to address these issues, existing research on technology-enabled interventions for families remains limited. OBJECTIVE The aim of this pilot study was to examine the feasibility of in-situ deployments of a low-cost, bespoke prototype, which has been designed to support children’s in-the-moment emotion regulation efforts. This prototype instantiates a novel intervention model that aims to address the existing limitations by delivering the intervention through an interactive object (a ‘smart toy’) sent home with the child, without any prior training necessary for either the child or their carer. This pilot study examined (i) engagement and acceptability of the device in the homes during 1 week deployments; and (ii) qualitative indicators of emotion regulation effects, as reported by parents and children. METHODS In this qualitative study, ten families (altogether 11 children aged 6-10 years) were recruited from three under-privileged communities in the UK. The RA visited participants in their homes to give children the ‘smart toy’ and conduct a semi-structured interview with at least one parent from each family. Children were given the prototype, a discovery book, and a simple digital camera to keep at home for 7-8 days, after which we interviewed each child and their parent about their experience. Thematic analysis guided the identification and organisation of common themes and patterns across the dataset. In addition, the prototypes automatically logged every interaction with the toy throughout the week-long deployments. RESULTS Across all 10 families, parents and children reported that the ‘smart toy’ was incorporated into children’s emotion regulation practices and engaged with naturally in moments children wanted to relax or calm down. Data suggests that children interacted with the toy throughout the duration of the deployment, found the experience enjoyable, and all requested to keep the toy longer. Child emotional connection to the toy—caring for its ‘well-being’—appears to have driven this strong engagement. Parents reported satisfaction with and acceptability of the toy. CONCLUSIONS This is the first known study investigation of the use of object-enabled intervention delivery to support emotion regulation in-situ. The strong engagement and qualitative indications of effects are promising – children were able to use the prototype without any training and incorporated it into their emotion regulation practices during daily challenges. Future work is needed to extend this indicative data with efficacy studies examining the psychological efficacy of the proposed intervention. More broadly, our findings suggest the potential of a technology-enabled shift in how prevention interventions are designed and delivered: empowering children and parents through ‘child-led, situated interventions’, where participants learn through actionable support directly within family life, as opposed to didactic in-person workshops and a subsequent skills application.

scholarly journals ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Xujun Zhang ◽  
Chao Shen ◽  
Xueying Guo ◽  
Zhe Wang ◽  
Gaoqi Weng ◽  
...  
Keyword(s):  
High Efficiency ◽  
Low Cost ◽  
Pearson Correlation ◽  
Scoring Function ◽  
Web Server ◽  
Scoring Functions ◽  
Protein Ligand Interactions ◽  
Prediction Module ◽  
Ligand Interactions ◽  
Benchmark Datasets

AbstractVirtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein–ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.

scholarly journals Leaf area index estimation using top-of-canopy airborne RGB images

2021 ◽  
Vol 96 ◽  
pp. 102282
Author(s):  
Rahul Raj ◽  
Jeffrey P. Walker ◽  
Rohit Pingale ◽  
Rohit Nandan ◽  
Balaji Naik ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Rgb Images ◽  
Index Estimation
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