Nitrogen variable rate in pastures using optical sensors

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.

Download Full-text

Effect of Cultivar on Chlorophyll Meter and Canopy Reflectance Measurements in Cucumber

Sensors ◽

10.3390/s20020509 ◽

2020 ◽

Vol 20 (2) ◽

pp. 509 ◽

Cited By ~ 2

Author(s):

Romina de Souza ◽

Rafael Grasso ◽

M. Teresa Peña-Fleitas ◽

Marisa Gallardo ◽

Rodney B. Thompson ◽

...

Keyword(s):

Optical Sensors ◽

Optical Sensor ◽

Chlorophyll Concentration ◽

Vegetation Indices ◽

Canopy Reflectance ◽

Cucumis Sativus L ◽

N Content ◽

Chlorophyll Meter ◽

Leaf N Content ◽

Leaf N

Optical sensors can be used to assess crop N status to assist with N fertilizer management. Differences between cultivars may affect optical sensor measurement. Cultivar effects on measurements made with the SPAD-502 (Soil Plant Analysis Development) meter and the MC-100 (Chlorophyll Concentration Meter), and of several vegetation indices measured with the Crop Circle ACS470 canopy reflectance sensor, were assessed. A cucumber (Cucumis sativus L.) crop was grown in a greenhouse, with three cultivars. Each cultivar received three N treatments, of increasing N concentration, being deficient (N1), sufficient (N2) and excessive (N3). There were significant differences between cultivars in the measurements made with both chlorophyll meters, particularly when N supply was sufficient and excessive (N2 and N3 treatments, respectively). There were no consistent differences between cultivars in vegetation indices. Optical sensor measurements were strongly linearly related to leaf N content in each of the three cultivars. The lack of a consistent effect of cultivar on the relationship with leaf N content suggests that a unique equation to estimate leaf N content from vegetation indices can be applied to all three cultivars. Results of chlorophyll meter measurements suggest that care should be taken when using sufficiency values, determined for a particular cultivar

Download Full-text

Relationship between vegetation indices and agronomic performance of maize varieties under different nitrogen rates

Bioscience Journal ◽

10.14393/bj-v36n5a2020-47993 ◽

2020 ◽

Vol 36 (5) ◽

Author(s):

Marcela da Silva Flores ◽

Willian Meniti Paschoalete ◽

Fabio Henrique Rojo Baio ◽

Cid Naudi Silva Campos ◽

Ariane de Andréa Pantaleão ◽

...

Keyword(s):

Plant Height ◽

Precision Agriculture ◽

Vegetation Index ◽

Block Design ◽

Vegetation Indices ◽

Agronomic Performance ◽

N Content ◽

Maize Varieties ◽

Leaf N Content ◽

Leaf N

Precision agriculture is a set of techniques that assist the monitoring of the agronomic performance of the maize crop by using vegetation indices. This study aimed to verify the relationship between vegetation indices, plant height, leaf N content, and grain yield of three maize varieties, grown under high and low N as topdressing. The experiment was carried out at the Fundação de Apoio à Pesquisa Agropecuária de Chapadão (Fundação Chapadão), located in the municipality of Chapadão do Sul, during the 2017/2018 season. The experiment consisted of a randomized block design with four replications, arranged in a 3x2 split-plot scheme. The first factor (plots) corresponded to three open-pollinated maize varieties (BRS 4103, BRS Gorotuba, and SCS 154), and the second factor (subplots) consisted of two N rates applied as topdressing (80 and 160 kg- 1). All the evaluated variables showed varieties x N interaction. Vegetation indices in maize varieties were influenced by the N rate applied as topdressing. Normalized Difference Vegetation Index (NDVI) and Soil-adjusted Vegetation Index (SAVI) showed a higher correlation with plant height. At the same time, Normalized Difference Red Edge (NDRE) had a stronger association with leaf N content.

Download Full-text

COMPARING COMMERCIAL OPTICAL SENSORS FOR CROP MONITORING TASKS IN PRECISION VITICULTURE

Journal of Agricultural Engineering ◽

10.4081/jae.2009.1.11 ◽

2009 ◽

Vol 40 (1) ◽

pp. 11 ◽

Cited By ~ 6

Author(s):

Fabrizio Mazzetto ◽

Aldo Calcante ◽

Aira Mena

Keyword(s):

Remote Sensing ◽

Optical Sensors ◽

Precision Agriculture ◽

Digital Camera ◽

Test Results ◽

Crop Monitoring ◽

Pv Systems ◽

Cultural Conditions ◽

Precision Viticulture ◽

Increasing Demand

The emergence of precision agriculture technologies and an increasing demand for higher quality grape products has led to a growing interest in Precision Viticulture. Actually, cultural monitoring is the most important application in PV systems: it requires specific technologies able to investigate the cultural conditions. To this aim, typically remote sensing surveys are adopted. These, anyhow, involve technical, economical and organisational barriers hampering a wide diffusion of their application. In order to overcome these problems, it would be necessary to substitute and/or integrate remote sensing information with alternative ground sensing technologies, to be employed directly inside the vineyard. This paper considers a commercial optical sensor, the GreenSeeker, useful in ground sensing surveys, and it compares its performances in monitoring vine with results obtained by a multispectral digital camera used as a tester. The experimentation was carried out in a greenhouse, on an artificial row including 15 grapevines (Cabernet Sauvignon variety). In front of the row, it was fixed a metallic rail gauge in order to permit a longitudinal movement of the Greenseeker sensor. Each plant was investigated at three different heights with a 5 s data time acquisition. Simultaneously, photos of the same grapevine were took by a multispectral digital camera, in order to obtain NDVI values through image analysis. The multispectral digital camera, normally used for remote sensing survey in agriculture, was considered as a test. Results demonstrate a strength correlation (R2 = 0.97) between the NDVI values measured through the two methods. This shows the same behaviour of the two tools, according to crop vigour and stress conditions induced into the plants. Consequently the GreenSeeker can be considered as a suitable solution for cultural monitoring in viticulture.

Download Full-text

Different Sensor Systems for the Application of Variable Rate Technology in Permanent Crops

Tehnički glasnik ◽

10.31803/tg-20180213125928 ◽

2018 ◽

Vol 12 (3) ◽

pp. 188-195 ◽

Cited By ~ 1

Author(s):

Davor Petrović ◽

Željko Barač

Keyword(s):

Optical Sensors ◽

Precision Agriculture ◽

New Technologies ◽

Negative Impact ◽

Low Cost ◽

Application Rate ◽

Sensor Systems ◽

Infrared Sensors ◽

Ecological Principles

The paper presents a review of different sensory systems for trees’ characterization and detection in permanent crops and the detection of plant health status in crop conditions for the purpose of applying the variable application rate. The use of new technologies enables the use of variable inputs in production with the aim of increasing the economic profit and reducing the negative impact on the environment. World trends increasingly emphasize the use of various sensor systems to achieve precision agriculture and apply the following: ultrasonic sensors for the detection of permanent crops; LIDAR (optical) sensors for treetop detection and characterization; infrared sensors with similar characteristics of optical sensors, but with very low cost prices and N - sensors for variable nitric fertilization. The daily development of sensor systems applied in agricultural production improves the performance and quality of the machines they are installed on. With a more intensive use of sensors in agricultural mechanization, their price becomes more acceptable for widespread use by achieving high quality work with respect to the ecological principles of sustainable production.

Download Full-text

Application of RGB Images Obtained by UAV in Coffee Farming

Remote Sensing ◽

10.3390/rs13122397 ◽

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.

Download Full-text

Actual Evapotranspiration and Biomass of Maize from a Red–Green-Near-Infrared (RGNIR) Sensor on Board an Unmanned Aerial Vehicle (UAV)

Water ◽

10.3390/w12092359 ◽

2020 ◽

Vol 12 (9) ◽

pp. 2359 ◽

Cited By ~ 1

Author(s):

Robson Argolo dos Santos ◽

Everardo Chartuni Mantovani ◽

Roberto Filgueiras ◽

Elpídio Inácio Fernandes-Filho ◽

Adelaide Cristielle Barbosa da Silva ◽

...

Keyword(s):

Decision Making ◽

Precision Agriculture ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Low Cost ◽

Vegetation Indices ◽

Maize Crop ◽

Evaporative Fraction ◽

Dry Biomass

Surface reflectance data acquisition by unmanned aerial vehicles (UAVs) are an important tool for assisting precision agriculture, mainly in medium and small agricultural properties. Vegetation indices, calculated from these data, allow one to estimate the water consumption of crops and predict dry biomass and crop yield, thereby enabling a priori decision-making. Thus, the present study aimed to estimate, using the vegetation indices, the evapotranspiration (ET) and aboveground dry biomass (AGB) of the maize crop using a red–green-near-infrared (RGNIR) sensor onboard a UAV. For this process, 15 sets of images were captured over 61 days of maize crop monitoring. The images of each set were mosaiced and subsequently subjected to geometric correction and conversion from a digital number to reflectance to compute the vegetation indices and basal crop coefficients (Kcb). To evaluate the models statistically, 54 plants were collected in the field and evaluated for their AGB values, which were compared through statistical metrics to the data estimated by the models. The Kcb values derived from the Soil-Adjusted Vegetation Index (SAVI) were higher than the Kcb values derived from the Normalized Difference Vegetation Index (NDVI), possibly due to the linearity of this model. A good agreement (R2 = 0.74) was observed between the actual transpiration of the crop estimated by the Kcb derived from SAVI and the observed AGB, while the transpiration derived from the NDVI had an R2 of 0.69. The AGB estimated using the evaporative fraction with the SAVI model showed, in relation to the observed AGB, an RMSE of 0.092 kg m−2 and an R2 of 0.76, whereas when using the evaporative fraction obtained through the NDVI, the RMSE was 0.104 kg m−2, and the R2 was 0.74. An RGNIR sensor onboard a UAV proved to be satisfactory to estimate the water demand and AGB of the maize crop by using empirical models of the Kcb derived from the vegetation indices, which are an important source of spatialized and low-cost information for decision-making related to water management in agriculture.

Download Full-text

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w6-229-2017 ◽

2017 ◽

Vol XLII-2/W6 ◽

pp. 229-233 ◽

Cited By ~ 7

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.

Download Full-text

Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging

Sensors ◽

10.3390/s19183859 ◽

2019 ◽

Vol 19 (18) ◽

pp. 3859 ◽

Cited By ~ 18

Author(s):

Zhao ◽

Yuan ◽

Song ◽

Ding ◽

Lin ◽

...

Keyword(s):

Deep Learning ◽

Unmanned Aerial Vehicle ◽

Optical Sensors ◽

Network Architecture ◽

High Efficiency ◽

Low Cost ◽

Digital Camera ◽

Large Area ◽

Image Test ◽

Aerial Vehicle

Rice lodging severely affects harvest yield. Traditional evaluation methods and manual on-site measurement are found to be time-consuming, labor-intensive, and cost-intensive. In this study, a new method for rice lodging assessment based on a deep learning UNet (U-shaped Network) architecture was proposed. The UAV (unmanned aerial vehicle) equipped with a high-resolution digital camera and a three-band multispectral camera synchronously was used to collect lodged and non-lodged rice images at an altitude of 100 m. After splicing and cropping the original images, the datasets with the lodged and non-lodged rice image samples were established by augmenting for building a UNet model. The research results showed that the dice coefficients in RGB (Red, Green and Blue) image and multispectral image test set were 0.9442 and 0.9284, respectively. The rice lodging recognition effect using the RGB images without feature extraction is better than that of multispectral images. The findings of this study are useful for rice lodging investigations by different optical sensors, which can provide an important method for large-area, high-efficiency, and low-cost rice lodging monitoring research.

Download Full-text

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

10.2196/preprints.14029 ◽

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.

Download Full-text

Advances in automatic identification of flying insects using optical sensors and machine learning

Scientific Reports ◽

10.1038/s41598-021-81005-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Carsten Kirkeby ◽

Klas Rydhmer ◽

Samantha M. Cook ◽

Alfred Strand ◽

Martin T. Torrance ◽

...

Keyword(s):

Machine Learning ◽

Optical Sensors ◽

Precision Agriculture ◽

Insect Pests ◽

Crop Damage ◽

Automatic Identification ◽

Flying Insects ◽

Remote Sensor ◽

Promising Solution ◽

Environmentally Sensitive

AbstractWorldwide, farmers use insecticides to prevent crop damage caused by insect pests, while they also rely on insect pollinators to enhance crop yield and other insect as natural enemies of pests. In order to target pesticides to pests only, farmers must know exactly where and when pests and beneficial insects are present in the field. A promising solution to this problem could be optical sensors combined with machine learning. We obtained around 10,000 records of flying insects found in oilseed rape (Brassica napus) crops, using an optical remote sensor and evaluated three different classification methods for the obtained signals, reaching over 80% accuracy. We demonstrate that it is possible to classify insects in flight, making it possible to optimize the application of insecticides in space and time. This will enable a technological leap in precision agriculture, where focus on prudent and environmentally-sensitive use of pesticides is a top priority.

Download Full-text