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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.

Multispectral sensors onboard unmanned aerial vehicles (UAV) have proven accurate and fast to predict sugarcane yield. However, challenges to a reliable approach still exist. In this study, we propose to predict sugarcane biometric parameters by using machine learning (ML) algorithms and multitemporal data through the analysis of multispectral images from UAV onboard sensors. The research was conducted on five varieties of sugarcane, as a way to make a robust approach. Multispectral images were collected every 40 days and the evaluated biometric parameters were: number of tillers (NT), plant height (PH), and stalk diameter (SD). Two ML models were used: multiple linear regression (MLR) and random forest (RF). The results showed that models for predicting sugarcane NT, PH, and SD using time series and ML algorithms had accurate and precise predictions. Blue, Green, and NIR spectral bands provided the best performance in predicting sugarcane biometric attributes. These findings expand the possibilities for using multispectral UAV imagery in predicting sugarcane yield, particularly by including biophysical parameters.

Pilotless aircraft systems will reshape our critical thinking about agriculture. Furthermore, because they can drive a transformative precision and digital farming, we authoritatively review the contemporary academic literature on UAVs from every angle imaginable for remote sensing and on-field management, particularly for sugarcane. We focus our search on the period of 2016–2021 to refer to the broadest bibliometric collection, from the emergence of the term “UAV” in the typical literature on sugarcane to the latest year of complete publication. UAVs are capable of navigating throughout the field both autonomously and semi-autonomously at the control of an assistant operator. They prove useful to remotely capture the spatial-temporal variability with pinpoint accuracy. Thereby, they can enable the stakeholder to make early-stage decisions at the right time and place, whether for mapping, re-planting, or fertilizing areas producing feedstock for food and bioenergy. Most excitingly, they are flexible. Hence, we can strategically explore them to spray active ingredients and spread entomopathogenic bioagents (e.g., Cotesia flavipes and Thricrogramma spp.) onto the field wherever they need to be in order to suppress economically relevant pests (e.g., Diatraea saccharalis, Mahanarva fimbriolata, sugarcane mosaic virus, and weeds) more precisely and environmentally responsibly than what is possible with traditional approaches (without the need to heavily traffic and touch the object). Plainly, this means that insights into ramifications of our integrative review are timely. They will provide knowledge to progress the field’s prominence in operating flying machines to level up the cost-effectiveness of producing sugarcane towards solving the sector’s greatest challenges ahead, such as achieving food and energy security in order to thrive in an ever-challenging world.

Surface quality is key for any adsorbent to have an effective adsorption. Because analyzing an adsorbent can be costly, we established an imagery protocol to determine adsorption robustly yet simply. To validate our hypothesis of whether stereomicroscopy, superpixel segmentation and fractal theory consist of an exceptional merger for high-throughput predictive analytics, we developed carbon-capturing biointerfaces by pelletizing hydrochars of sugarcane bagasse, pinewood sawdust, peanut pod hull, wheat straw, and peaty compost. The apochromatic stereomicroscopy captured outstanding micrographs of biointerfaces. Hence, it enabled the segmenting algorithm to distinguish between rough and smooth microstructural stresses by chromatic similarity and topological proximity. The box-counting algorithm then adequately determined the fractal dimension of microcracks, merely as a result of processing segments of the image, without any computational unfeasibility. The larger the fractal pattern, the more loss of functional gas-binding sites, namely N and S, and thus the potential sorption significantly decreases from 10.85 to 7.20 mmol CO2 g−1 at sigmoid Gompertz function. Our insights into analyzing fractal carbon-capturing biointerfaces provide forward knowledge of particular relevance to progress in the field’s prominence in bringing high-throughput methods into implementation to study adsorption towards upgrading carbon capture and storage (CCS) and carbon capture and utilization (CCU).

Imagery data prove useful for mapping gaps in sugarcane. However, if the quality of data is poor or the moment of flying an aerial platform is not compatible to phenology, prediction becomes rather inaccurate. Therefore, we analyzed how the combination of pixel size (3.5, 6.0 and 8.2 cm) and height of plant (0.5, 0.9, 1.0, 1.2 and 1.7 m) could impact the mapping of gaps on unmanned aerial vehicle (UAV) RGB imagery. Both factors significantly influenced mapping. The larger the pixel or plant, the less accurate the prediction. Error was more likely to occur for regions on the field where actively growing vegetation overlapped at gaps of 0.5 m. Hence, even 3.5 cm pixel did not capture them. Overall, pixels of 3.5 cm and plants of 0.5 m outstripped other combinations, making it the most accurate (absolute error ~0.015 m) solution for remote mapping on the field. Our insights are timely and provide forward knowledge that is particularly relevant to progress in the field’s prominence of flying a UAV to map gaps. They will enable producers to make decisions on replanting and fertilizing site-specific high-resolution imagery data.