Increasing global consumption of protein over the last five decades, coupled with concerns about the impact on emissions of animal‐based protein production, has created interest in alternative protein sources. Microbial proteins (MPs), derived through the fermentation of agro‐industrial byproducts, present a promising option. This review assesses a century of advancements in this domain. We conducted a comprehensive review and meta‐analysis, examining 347 relevant research papers to identify trends, technological advancements, and key influencing factors in the production of MP. The analysis covered the types of feedstocks and microbes, fermentation methods, and the implications of nucleic acid content on the food‐grade quality of proteins. A conditional inference tree model and Bayesian factor were used to ascertain the impact of various parameters on protein content. Out of all the studied parameters, such as type of feedstock (lignocellulose, free sugars, gases, and others), type of fermentation (solid, liquid, gas), type of microbe (bacteria, fungi, yeast, and mix), and operating parameters (temperature, time, and pH), the type of fermentation and microbe were identified as the largest influences on protein content. Gas and liquid fermentation demonstrated higher protein content, averaging 52% and 42%, respectively. Among microbes, bacterial species produced a higher protein content of 51%. The suitable operating parameters, such as pH, time, and temperature, were also identified for different microbes. The results point to opportunities for continued innovation in feedstock, microbes, and regulatory alignment to fully realize the potential of MP in contributing to global food security and sustainability goals.

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.

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.

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.