UAV Multispectral Data: A Reliable Approach for Managing Phosphate-Solubilizing Bacteria in Common Bean

Abstract

Remote sensing can offer stakeholders opportunities to make precise and accurate decisions on agricultural activities. For instance, farmers can exploit aircraft systems to acquire survey-level, high-resolution imagery data for crop and soil management. Therefore, the objective of this study was to analyze whether an unmanned aerial vehicle (UAV) allows for the assessment and monitoring of biofertilization of the common bean upon vegetation indices (VIs). The biological treatment of the legume crop included its inoculation with phosphate-solubilizing bacteria (PSB), namely Bacillus subtilis and B. megaterium. Indicators of photosynthetic performance, such as chlorophylls (a and b) and carotenoids, were measured from actively growing leaves to determine effectiveness. In addition, images were acquired in the field, both spatially and temporally, to establish functional relationships between biometric and computational features. Microorganisms manifested as growth-promoting agents to the crop as they significantly increased its quantities of light-harvesting pigments. VIs allowed for predicting their impact on photosynthetic performance, making them on-site markers of PSB. Therefore, this research can provide insights into the remote, non-destructive mapping of spectral changes in the common bean upon the application of PSB. Imagery data from UAV would enable producers to generate information on the crop to intervene in the field at the right time and place for improved utilization of biofertilizers.

Managing Soil and Plant Nutrients: Role of Microbial Phosphate Solubilisation

“…Achromobacter xylosoxidans China [26] Acinetobacter calcoaceticus Iran [27] Advenella mimigardefordensis Spain [28] Arthrobacter luteolus Iran [27] Aspergillus awamori Indonesia [29] Aspergillus niger India, China [30,31] Aspergillus terreus Indonesia [29] Bacillus amyloliquefaciens Italy [32] Bacillus aryabhattai IA20 Pakistan [33] Bacillus cereus Spain, Iran [27,28] Bacillus cereus MZUTZ01 India [34] Bacillus firmus Pakistan [35] Bacillus licheniformis Pakistan [35] Bacillus megaterium China, Brazil, Spain, Thailand [28,[36][37][38][39] Bacillus mojavensis Thailand [36] Bacillus pumilus Mexico [37] Bacillus safensis Pakistan [35] Bacillus safensis IALR1035 USA [38] Phosphorus in Soils and Plants…”

“…Bacillus siamensis Mexico [37] Bacillus subtilis India, Indonesia, Brazil [29,34,39] Bacillus subtilis IA6 Pakistan [33] Bacillus subtilis IALR1033 USA [38] Bacillus thuringensis MZUTZ13 India [34] Burkholderia cenocepacia Indonesia [29] Burkholderia cepacia Indonesia [29] Burkholderia cepacia ISOP5 China [40] Burkholderia fungorum Spain [28] Burkholderia gladioli India [34] Burkholderia seminalis Indonesia [29] Burkholderia vietnamiensis Iran [27] Cellulosimicrobium cellulans China [26] Enterobacter bugandensis Morocco [41] Enterobacter cloacae C8 China [42] Enterobacter hormaechei(LMG 27195) China [43] Funneliformis mosseae China [44] Funneliformis mosseae BEG234 Italy [32] Geobacillus stearothermophillus MZUTZ08 India [34] Klebsiella variicola Brazil [45] Nocardiopsis alba Morocco [46,47] Novosphingobium barchaimii(LL02) China [43] Novosphingobium resinovorum China [42] Ochrobactrum haematophilum China [26] Ochrobactrum pseudogrignonense Brazil [45] Ochrobactrum pseudogrignonense(CCUG30717) China [43] Paenibacillus polymyxa IA7 Pakistan [33] Pantoea agglomerans Tunisia, Morocco [41,48] Pantoea agglomerans IALR1325 USA [38] Pantoea agglomerans p v. P 5 Iran [49] Pantoea ananatis Brazil [45] Pantoea roadsii(LMG26273) China [43] Pantoea stewartii subsp. Indologenes Morocco [41] Pantoea vagans IALR611 USA…”

Yield Prediction of Four Bean (Phaseolus vulgaris) Cultivars Using Vegetation Indices Based on Multispectral Images from UAV in an Arid Zone of Peru

“…A dual multispectral camera MicaSense RedEdge-Mx + RedEdge-MX Blue (MicaSense, RedEdge, NC, USA) with ten multispectral bands (Coastal blue 444, Blue 475, Green 531, Green 560, Red 650, Red 668, Red Edge 705, Red Edge 717, Red Edge 740 and NIR 842) with a 1.2 MP global shutter was coupled to this UAV. The images were collected on 13 dates (33,40,47,54,61,68,76,82,89,97,103,110 and 118 DAS) (Figures S1 and S2)-sunny days with wind speeds less than 12 m/s-between 11:00 a.m. to 01:00 pm [20]. The flight plan was made with the DJI Pilot 2 UAV application, considering a frontal and lateral overlap of 80%, height of 30 m, speed of 4.5 m/s and the camera focused on the nadir (perpendicular to the ground surface), allowing a resolution of 2.08 cm/pixel to be obtained for multispectral images for each pixel.…”

Geotechnologies in Biophysical Analysis through the Applicability of the UAV and Sentinel-2A/MSI in Irrigated Area of Common Beans: Accuracy and Spatial Dynamics

“…Several vegetation indices can be utilized for remote sensing of agricultural areas, according to various pieces of research [15,[20][21][22], which are mathematical models constructed from the reflectance of the vegetative canopy, provided in different spectral bands and wavelengths of the electromagnetic spectrum, mainly in the visible (RGB) and near-infrared (NIR) bands [23,24]. These wavelengths provide crucial information about vegetation [25].…”