Precision Agriculture Application for Sustainable Nitrogen Management of Justicia brandegeana Using Optical Sensor Technology

Freidenreich, Ariel; Barraza, Gabriel; Jayachandran, Krish; Khoddamzadeh, Amir Ali

doi:10.3390/agriculture9050098

Cited by 24 publications

(14 citation statements)

References 34 publications

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“…Average leaf chlorophyll content of the developed upper leaves was determined using the Soil-Plant Analyses Development (SPAD) 502 Plus Chlorophyll Meter every two weeks for eight weeks. Leaf chlorophyll content is one of the indicators of crop health [36]. In general, leaf chlorophyll and nitrogen content exhibit a positive correlation [37].…”

Section: Plant Height Leaf Chlorophyll Content and Bean Yieldmentioning

confidence: 99%

Effect of Salinity Stress and Microbial Inoculations on Glomalin Production and Plant Growth Parameters of Snap Bean (Phaseolus vulgaris)

et al. 2019

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Salinity is a major abiotic stress that can adversely affect plant growth, yield, other physiological parameters, and soil health. Salinity stress on biomass production of salt-sensitive crops, like snap bean (Phaseolus vulgaris), is a serious problem, and specifically in South Florida, USA, where saline soils can be found in major agricultural lands. Research studies focused on the ‘snap bean–Rhizobium–arbuscular mycorrhizal fungi (AMF)’ relationship under salinity stress are limited, and fewer studies have evaluated how this tripartite symbiosis affects glomalin production (GRSP), a glycoprotein released by AMF. A shade house experiment was conducted to elucidate the effects of three microbial inoculations (IC = inoculation control; IT1 = AMF and IT2 = AMF + Rhizobium) on three salinity treatments (SC = salinity control 0.6 dS m−1, S1 = 1.0 dS m−1, and S2 = 2.0 dS m−1) on snap bean growth and yield. Our results indicate that S2 reduced 20% bean biomass production, 11% plant height, 13% root weight, and 23% AMF root colonization. However, microbial inoculations increased 26% bean yield over different salinity treatments. Maximum salinity stress (S2) increased 6% and 18% GRSP production than S1 and SC, respectively, indicating the relative advantage of abiotic stress on AMF’s role in soil. Dual inoculation (IT2) demonstrated a beneficial role on all physiological parameters, biomass production, and GRSP synthesis compared to single inoculation (IT1) treatment with all three salinity levels.

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Section: Plant Height Leaf Chlorophyll Content and Bean Yieldmentioning

confidence: 99%

Effect of Salinity Stress and Microbial Inoculations on Glomalin Production and Plant Growth Parameters of Snap Bean (Phaseolus vulgaris)

et al. 2019

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“…A difference of this magnitude cannot be neglected in the operational phase for precision agriculture applications; even more so if used for multitemporal surveys. As a matter of fact, the map that shows the most homogeneous values in all cases is NDVI, which is widely used in most agriculture applications [57][58][59][60].…”

Section: Radiometric Consistencymentioning

confidence: 99%

Geometric and Radiometric Consistency of Parrot Sequoia Multispectral Imagery for Precision Agriculture Applications

et al. 2019

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This paper is about the geometric and radiometric consistency of diverse and overlapping datasets acquired with the Parrot Sequoia camera. The multispectral imagery datasets were acquired above agricultural fields in Northern Italy and radiometric calibration images were taken before each flight. Processing was performed with the Pix4Dmapper suite following a single-block approach: images acquired in different flight missions were processed in as many projects, where different block orientation strategies were adopted and compared. Results were assessed in terms of geometric and radiometric consistency in the overlapping areas. The geometric consistency was evaluated in terms of point cloud distance using iterative closest point (ICP), while the radiometric consistency was analyzed by computing the differences between the reflectance maps and vegetation indices produced according to adopted processing strategies. For normalized difference vegetation index (NDVI), a comparison with Sentinel-2 was also made. This paper will present results obtained for two (out of several) overlapped blocks. The geometric consistency is good (root mean square error (RMSE) in the order of 0.1 m), except for when direct georeferencing is considered. Radiometric consistency instead presents larger problems, especially in some bands and in vegetation indices that have differences above 20%. The comparison with Sentinel-2 products shows a general overestimation of Sequoia data but with similar spatial variations (Pearson’s correlation coefficient of about 0.7, p-value < 2.2 × 10−16).

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“…These sensors are based on multispectral images and use vegetation indices to obtain the best correlation with the nutrient status of the arable crops [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Plant Status and Fertilization Strategy through Multispectral Images

Lima

Krus

Valero

et al. 2020

Sensors

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A crop monitoring system was developed for the supervision of organic fertilization status on tomato plants at early stages. An automatic and nondestructive approach was used to analyze tomato plants with different levels of water-soluble organic fertilizer (3 + 5 NK) and vermicompost. The evaluation system was composed by a multispectral camera with five lenses: green (550 nm), red (660 nm), red edge (735 nm), near infrared (790 nm), RGB, and a computational image processing system. The water-soluble fertilizer was applied weekly in four different treatments: (T0: 0 mL, T1: 6.25 mL, T2: 12.5 mL and T3: 25 mL) and the vermicomposting was added in Weeks 1 and 5. The trial was conducted in a greenhouse and 192 images were taken with each lens. A plant segmentation algorithm was developed and several vegetation indices were calculated. On top of calculating indices, multiple morphological features were obtained through image processing techniques. The morphological features were revealed to be more feasible to distinguish between the control and the organic fertilized plants than the vegetation indices. The system was developed in order to be assembled in a precision organic fertilization robotic platform.

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Precision Agriculture Application for Sustainable Nitrogen Management of Justicia brandegeana Using Optical Sensor Technology

Cited by 24 publications

References 34 publications

Effect of Salinity Stress and Microbial Inoculations on Glomalin Production and Plant Growth Parameters of Snap Bean (Phaseolus vulgaris)

Effect of Salinity Stress and Microbial Inoculations on Glomalin Production and Plant Growth Parameters of Snap Bean (Phaseolus vulgaris)

Geometric and Radiometric Consistency of Parrot Sequoia Multispectral Imagery for Precision Agriculture Applications

Monitoring Plant Status and Fertilization Strategy through Multispectral Images

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