Tebuthiuron is often used to control weed growth in sugarcane cultures. This herbicide is highly toxic and can persist in soil for up to 2 years according to its degradation half-life. Hence, its residual effect is highly hazardous for the environment and local habitants via leaching, surface runoff. Screening out of species of green manure as potential phytoremediators for tebuthiuron in soil, with and with no vinasse, accordingly is the scientific point of this study. Green manure species selected for the trial in greenhouse were jack bean [Canavalia ensiformis (L.) DC.], pigeon pea [Cajanus cajan (L. Millsp.)], velvet bean [Mucuna pruriens (L.) DC.)], and millet [Pennisetum glaucum (L.) R.Br.], and Crotalaria juncea L. as bioindicator of this herbicide. The determination/quantification of height, stem diameter, and number of leaves in all plants were monitored, as well as other morphological traits for drafting any inference on biomass production. Moreover, ecotoxicity bioassays were performed from soil samples at the beginning and at the end of the experiment. Results showed preliminary evidence of effective phytoremediation capacity by M. pruriens and P. glaucum in soils with tebuthiuron, as the growth of C. juncea was sustained. Both Gompertz approach and principal component analysis predicted that these green manure species could grow healthier and for longer periods in soils containing tebuthiuron and vinasse and, thus, reduce physiological anomalies due to ecotoxicity. The implications of this study may aid in the implementation of cost-effective strategies targeting decontamination of tebuthiuron in sugarcane crops with vinasse application in fertigation.

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Agricultural spray adjuvants (ASA) are widely used in pesticide applications to enhance the performance of pesticides. The aim of this research was to investigate the effects of ASA on static surface tension (SST) and foliar spray retention on coffee leaves. The SST of ASA at different concentrations was determined by the drop weight method. Spray retention on adaxial and abaxial coffee leaf surfaces was performed using a micro-sprayer at solution concentrations of 0, 0.01, 0.1, 0.5, and 1.0% v v-1. The ASA assessed were: polyether-polymethylsiloxane-copolymer (PPC); Nonylphenol ethoxylate; nonyl polyethylene glycol ether; mineral oil; nonylphenoxy polyethoxy ethanol; carboxyl copolymer of styrene and butadiene; primary aliphatic oxyalkylated alcohol plus carboxyl copolymer of styrene and butadiene; and soyal phospholipids and propionic acid. All ASA reduced the SST of the aqueous solutions. PPC provided the best performance in decreasing SST, reaching values below 20 mN m-1 at a concentration of 0.05% v v-1. Spray retention on leaves was influenced by adjuvant type as well as concentration. A very strong positive correlation between SST and spray retention on coffee leaves was observed. Decreasing the SST of the solution provided a reduction of spray retention when spraying was performed until runoff point.

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.

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Remote sensing can provide useful imagery data to monitor sugarcane in the field, whether for precision management or high-throughput phenotyping (HTP). However, research and technological development into aerial remote sensing for distinguishing cultivars is still at an early stage of development, driving the need for further in-depth investigation. The primary objective of this study was therefore to analyze whether it could be possible to discriminate market-grade cultivars of sugarcane upon imagery data from an unmanned aerial vehicle (UAV). A secondary objective was to analyze whether the time of day could impact the expressiveness of spectral bands and vegetation indices (VIs) in the biophysical modeling. The remote sensing platform acquired high-resolution imagery data, making it possible for discriminating cultivars upon spectral bands and VIs without computational unfeasibility. 12:00 PM especially proved to be the most reliable time of day to perform the flight on the field and model the cultivars upon spectral bands. In contrast, the discrimination upon VIs was not specific to the time of flight. Therefore, this study can provide further information about the division of cultivars of sugarcane merely as a result of processing UAV imagery data. Insights will drive the knowledge necessary to effectively advance the field’s prominence in developing low-altitude, remotely sensing sugarcane.

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