Biotechnological frontiers in harnessing allelopathy for sustainable crop production.

Allelopathy, the phenomenon in which plants release biochemical compounds that influence the growth and development of neighbouring plants, presents promising opportunities for revolutionizing agriculture towards sustainability. This abstract explores the role of biotechnological advancements in unlocking the potential of allelopathy for sustainable crop production and its applications in agriculture, ecology, and natural resource management. By combining molecular, genetic, biochemical, and bioinformatic tools, researchers can unravel the complexities of allelopathic interactions and their potential for sustainable crop production and environmental stewardship. The development of novel management methods for weed control is getting a lot of attention with the introduction of new genetic technologies such as Gene drive, Transgene technologies, Gene silencing, Marker-assisted selection (MAS), and Clustered regularly interspaced short palindromic repeats (CRISPR-Cas9). By strengthening competitive characteristics these tools hold great promise for boosting crops' ability to compete with weeds. Considering recent literature, this review highlights the genetic, transcriptomics, and metabolomics approaches to allelopathy. Employing allelopathic properties in agriculture offer sustainable benefits like natural weed management, pest management, and reduced chemical pollution, but challenges include environmental factors, toxicity, regulatory hurdles, and limited resources. Effective integration requires continued research, regulatory support, and farmer education​. Also, we aimed to identify the biotechnological domains requiring more investigation and to provide the basis for future advances through this assessment.

Fertility and tillage intensity affect weed community diversity and functional structure in long-term organic systems.

Knowledge of how agricultural management interacts with weed seed banks and emergent weed communities is crucial for proactive weed management. Though studies have detailed how differences in disturbance and nutrient applications between organic and conventional herbicide-based systems affect weed communities, few have focused on these same factors in contrasting organic systems. This study assessed the seed banks and emergent weed communities from the most recent crop rotation cycle (2017-2022) of a long-term experiment, which compared four organic grain and forage cropping systems differing in nutrient inputs and soil disturbance. The high fertility (HF) system received high-rate nutrient applications, low fertility (LF) received low-rate applications, enhanced weed management (EWM) focused on weed control through frequent soil disturbance, and reduced tillage (RT) prioritized soil health with less intense or frequent soil disturbance. Soil samples for greenhouse germination assays were collected at the beginning (2017) and end (2022) of the rotation to explore how these four systems influenced seed bank dynamics over time. Weed community biomass was also sampled in each crop during this time. Treatment effects on weed abundance, taxonomic diversity, and community-weighted means and functional dispersion of weed traits were analyzed with generalized mixed-effect models. The RT system had the highest weed seed bank taxonomic diversity, and EWM had the lowest. RT and LF had higher functional dispersion of traits than HF in the seed bank. Weed seed bank communities in HF and RT were characterized by short, small-seeded, and early germinating weed species. However, seed banks were also labile: Differences between systems in seed density and all other mean trait values were dependent on the crop, which preceded seed bank sampling. Likewise, differences among emergent weed communities in the four systems depended on an interaction between crop species and their planting year. Results suggest that resource availability and intensity of disturbance act as weed community assembly filters in organic cropping systems. Organic growers seeking to design systems that balance weed management and production goals can use relatively low soil disturbance and nutrient application to increase weed community taxonomic or functional diversity without necessarily increasing weed biomass or seed bank density.

Bacterial allelopathy: an approach for biological control of weeds.

Weed infestation is one of the most damaging biotic factors to limit crop production by competing with the crop for space, water, and nutrients. Different conventional approaches are being used to cope with weed infestation, including labor intensive manual removal and the use of soil-degrading, crop-damaging, and environment-deteriorating chemical herbicides. The use of chemicals for weed control has increased 2-fold after the green revolution and their non-judicious use is posing serious threats to mankind, animals, and biodiversity. The detrimental effects of these approaches have shifted the researchers' attention from the last two decades towards alternate, sustainable, and eco-friendly approaches to cope with weed infestation. The recent approaches of weed control, including plant and microbial allelopathy have gained popularity during last decade. Farmers still use conventional methods, but the majority of farmers are very passionate about organic agriculture and describe it as a slogan in the developed world. The effectiveness of these approaches lies in host specificity by selective bacteria and differential response towards weeds and crops. Moreover, the crop growth promoting effect of microorganisms (allelopathic bacteria) possessing various growth promoting traits, that is, mineral solubilization, phytohormone production, and beneficial enzymatic activity, provide additional benefits. The significance of this review lies in the provision of a comprehensive comparison of the conventional approaches along with their potential limitations with advanced/biological weed control approaches in sustainable production. In addition, the knowledge imparted about weed control will contribute to a better understanding of biological control methods.

Recent technologies for glyphosate removal from aqueous environment: A critical review.

The growing demand for food has led to an increase in the use of herbicides and pesticides over the years. One of the most widely used herbicides is glyphosate (GLY). It has been used extensively since 1974 for weed control and is currently classified by the World Health Organization (WHO) as a Group 2A substance, probably carcinogenic to humans. The industry and academia have some disagreements regarding GLY toxicity in humans and its effects on the environment. Even though this herbicide is not mentioned in the WHO water guidelines, some countries have decided to set maximum acceptable concentrations in tap water, while others have decided to ban its use in crop production completely. Researchers around the world have employed different technologies to remove or degrade GLY, mostly at the laboratory scale. Water treatment plants combine different technologies to remove it alongside other water pollutants, in some cases achieving acceptable removal efficiencies. Certainly, there are many challenges in upscaling purification technologies due to the costs and lack of factual information about their adverse effects. This review presents different technologies that have been used to remove GLY from water since 2012 to date, its detection and removal methods, challenges, and future perspectives.

Phytotoxicity and Identification of Active Compounds from Elaeocarpus floribundus Blume Plant for Controlling Weeds.

Phytotoxic compounds isolated and identified from different plants have the ability to use as plant-based herbicides. Phytotoxic chemicals may be essential to weed management and environmental protection in order to reduce the indiscriminate use of synthetic pesticides. It has been reported that Elaeocarpus floribundus plant possesses phytotoxic compounds. The leaf extracts of this species demonstrated significant growth inhibition against the tested plants (dicot plant lettuce and plant monocot timothy) and inhibition was dose- and species-dependent pattern. Two phytotoxic compounds were separated using different purifications methods and identified as compounds 1 and 2. All phytotoxic compounds displayed potent growth limitation against the tested species (cress). The compound concentrations needed for the inhibition of 50% growth (IC50 value) of tested species ranged from 1.06 to 8.53 µM (micromolar). Findings of this research suggest that these compounds might be responsible for the phytotoxicity of Elaeocarpus floribundus plant. The results of this study may be helpful for the development of natural herbicide to control weeds.

Optimizing herbicide selection for pre-emergence control of itchgrass and cypressvine morningglory in sugarcane.

The aim of this study was to assess the efficacy of herbicides in association to control Rottboellia exaltata and Ipomoea quamoclit during pre-emergence while also to evaluate the potential impact on the sugarcane. The experimental design employed a randomized block with seven treatments and four replications. The treatments were: 1 - no herbicide application; 2 - indaziflam + sulfentrazone; 3 - indaziflam + diclosulam; 4 - indaziflam + tebuthiuron; 5 - flumioxazin + diclosulam, 6 - flumioxazin + pyroxasulfone and 7 - clomazone + sulfentrazone. The evaluated parameters were: percentage of weeds control, green coverage percentage (Canopeo® system), weed biomass (g m-2), itchgrass height, and sugarcane tiller. Several herbicide associations have been proven effective alternatives for managing itchgrass and cypressvine morningglory. The most successful treatments for itchgrass control were indaziflam + tebuthiuron (100%) and indaziflam + diclosulam (97%), whereas for cypressvine morningglory, the betters were indaziflam + sulfentrazone (97%), indaziflam + diclosulam (98%), indaziflam + tebuthiuron (97%), flumioxazin + diclosulam (94%), and clomazone + sulfentrazone (96%). All treatments reduced the weed biomass, with indaziflam + tebuthiuron being the safest option for protecting sugarcane.

Pre-emergence herbicides mixture in soybeans: Amaranthus hybridus control and crop selectivity on cover crops soil.

The objective was to evaluate the efficacy of pre-emergence herbicides mixture applied to the soil with and without dead cover crops (Sorghum bicolor) for the control of Amaranthus hybridus L. (smooth pigweed) and its selectivity in soybeans. This study was structured in split plot (2 × 6 + 2), where factor A plots (with and without dead cover) and factor B six herbicides mixture: flumioxazin + S-metolachlor (50.4 + 1,008 g a.i. ha-1), flumioxazin + imazethapyr (60 + 127.2 g a.i. ha-1), pyroxasulfone + sulfentrazone (137.6 + 160 g a.i. ha-1), diuron + sulfentrazone (400 + 200 g a.i. ha-1), metribuzin + S-metolachlor (326.4 + 1,344 g a.i. ha-1) and sulfentrazone + imazethapyr (200 + 100 g a.i. ha-1) and two untreated control plots. As for the results, the herbicides flumioxazin + S-metolachlor, flumioxazin + imazethapyr and pyroxasulfone + sulfentrazone showed excellent control (97-99%) and were not influenced by the plot with and without dead cover. They also showed higher yield soybeans (<2,244 kg ha-1). All herbicides were selective to the soybeans. Overall, pre-emergence herbicides and cover crops were efficient methods for the control of A. hybridus, which farmers should use to avoid losses in yield soybeans due to weed competition.

Application of Moringa leaves as soil amendment to tiger-nut for suppressing weeds in the Nigerian Savanna.

BACKGROUND: The allelopathic effect of Moringa (Moringa oleifera Lam.) leaves applied as organic manure in tiger nut (Cyperus esculentus L.) production on associated weeds was investigated in the guinea savanna of South West Nigeria, during the 2014 (September - November) and 2015 (June - August) wet seasons. METHODS: Five Moringa leaves rates (0, 2.5, 5.0, 7.5 and 10 t/ha) and three tuber sizes (0.28 g, 0.49 g and 0.88 g dry weight) were laid out in the main plot and sub-plot, respectively in a split-plot arrangement fitted into randomized complete block design and replicated three times. RESULTS: Parameters measured, which include, weed cover score (WCS), weed density (WD) and weed dry matter production (WDMP) were significantly (p<0.05) influenced in both years by Moringa leaf. In 2015, WCS, WD and WDMP significantly (p<0.05) reduced by 25-73%, 35-78% and 26-70% on Moringa leaves-treated plots respectively. There were significant (p<0.05) interactions between quantity of Moringa leaves incorporated and tuber size. The bigger the tuber and the higher the quantity of Moringa leaves incorporated the lower the WCS, WD and WDMP. CONCLUSIONS: Consequently, application of 10 t.ha- 1 Moringa leaves and planting of large or medium-sized tubers were recommended for optimum weed suppression in tiger nut production in South West Nigeria.

Rational design of Striga hermonthica-specific seed germination inhibitors.

The obligate hemiparasitic weed Striga hermonthica grows on cereal roots and presents a severe threat to global food security by causing enormous yield losses, particularly in sub-Saharan Africa. The rapidly increasing Striga seed bank in infested soils provides a major obstacle in controlling this weed. Striga seeds require host-derived strigolactones (SLs) for germination, and corresponding antagonists could be used as germination inhibitors. Recently, we demonstrated that the common detergent Triton X-100 is a specific inhibitor of Striga seed germination by binding noncovalently to its receptor, S. hermonthica HYPO-SENSITIVE TO LIGHT 7 (ShHTL7), without blocking the rice (Oryza sativa) SL receptor DWARF14 (OsD14). Moreover, triazole ureas, the potent covalently binding antagonists of rice SL perception with much higher activity toward OsD14, showed inhibition of Striga but were less specific. Considering that Triton X-100 is not suitable for field application and by combining structural elements of Triton and triazole urea, we developed two hybrid compounds, KK023-N1 and KK023-N2, as potential Striga-specific germination inhibitors. Both compounds blocked the hydrolysis activity of ShHTL7 but did not affect that of OsD14. Binding of KK023-N1 diminished ShHTL7 interaction with S. hermonthica MORE AXILLARY BRANCHING 2, a major component in SL signal transduction, and increased ShHTL7 thermal specificity. Docking studies indicate that KK023-N1 binding is not covalent but is caused by hydrophobic interactions. Finally, in vitro and greenhouse tests revealed specific inhibition of Striga seed germination, which led to a 38% reduction in Striga infestation in pot experiments. These findings reveal that KK023-N1 is a potential candidate for combating Striga and a promising basis for rational design and development of further Striga-specific herbicides.

Bacterial secondary metabolites: possible mechanism for weed suppression in wheat.

Chemical weed control is an effective method, but has proved hazardous for humans, environment, and soil biodiversity. Use of allelopathic bacteria may be more efficient and sustainable weed control measure. The bacterial inoculants have never been studied in context of their interaction with weed root exudates and precursor-dependent production of the natural phytotoxins (cyanide, cytolytic enzymes and auxin) by these strains to understand their weed suppression and wheat growth promotion abilities. Therefore, root exudates of Avena fatua, Phalaris minor, Rumex dentatus, and wheat were quantified and their role in microbial root colonization and secondary metabolite production, i.e., cyanide, cytolytic enzymes, phenolics, and elevated auxin concentration, was studied. The results depicted l-tryptophan and glycine as major contributors of elevated cyanide and elevated levels in weed rhizosphere by the studied Pseudomonas strains, through their higher root colonization ability in weeds as compared with wheat. Furthermore, the higher root colonization also enhanced p-coumaric acid (photosynthesis inhibitor by impairing cytochrome c oxidase activity in plants) and cytolytic enzyme (root cell wall degradation) concentration in weed rhizosphere. In conclusion, the differential root colonization of wheat and weeds by these strains is responsible for enhancing weed suppression (enhancing phytotoxic effect) and wheat growth promotion (lowering phytotoxic effect).

Hyperspectral Analysis for Discriminating Herbicide Site of Action: A Novel Approach for Accelerating Herbicide Research.

In agricultural weed management, herbicides are indispensable, yet innovation in their modes of action (MOA)-the general mechanisms affecting plant processes-has slowed. A finer classification within MOA is the site of action (SOA), the specific biochemical pathway in plants targeted by herbicides. The primary objectives of this study were to evaluate the efficacy of hyperspectral imaging in the early detection of herbicide stress and to assess its potential in accelerating the herbicide development process by identifying unique herbicide sites of action (SOA). Employing a novel SOA classification method, eight herbicides with unique SOAs were examined via an automated, high-throughput imaging system equipped with a conveyor-based plant transportation at Purdue University. This is one of the earliest trials to test hyperspectral imaging on a large number of herbicides, and the study aimed to explore the earliest herbicide stress detection/classification date and accelerate the speed of herbicide development. The final models, trained on a dataset with nine treatments with 320 samples in two rounds, achieved an overall accuracy of 81.5% 1 day after treatment. With the high-precision models and rapid screening of numerous compounds in only 7 days, the study results suggest that hyperspectral technology combined with machine learning can contribute to the discovery of new herbicide MOA and help address the challenges associated with herbicide resistance. Although no public research to date has used hyperspectral technology to classify herbicide SOA, the successful evaluation of herbicide damage to crops provides hope to accelerate the progress of herbicide development.

Weed Detection Using Deep Learning: A Systematic Literature Review.

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

Current progress in Striga management.

The Striga, particularly S. he rmonthica, problem has become a major threat to food security, exacerbating hunger and poverty in many African countries. A number of Striga control strategies have been proposed and tested during the past decade, however, further research efforts are still needed to provide sustainable and effective solutions to the Striga problem. In this paper, we provide an update on the recent progress and the approaches used in Striga management, and highlight emerging opportunities for developing new technologies to control this enigmatic parasite.

Modeling the evolution of herbicide resistance in weed species with a complex life cycle.

A growing number of weed species have evolved resistance to herbicides in recent years, which causes an immense financial burden to farmers. An increasingly popular method of weed control is the adoption of crops that are resistant to specific herbicides, which allows farmers to apply the herbicide during the growing season without harming the crop. If such crops are planted in the presence of closely related weed species, it is possible that resistance genes could transfer from the crop species to feral populations of the wild species via gene flow and become stably introgressed under ongoing selective pressure by the herbicide. We use a density-dependent matrix model to evaluate the effect of planting such crops on the evolution of herbicide resistance under a range of management scenarios. Our model expands on previous simulation studies by considering weed species with a more complex life cycle (perennial, rhizomatous weed species), studying the effect of environmental variation in herbicide effectiveness, and evaluating the role of common simplifying genetic assumptions on resistance evolution. Our model predictions are qualitatively similar to previous modeling studies using species with a simpler life cycle, which is, crop rotation in combination with rotation of herbicide site of action effectively controls weed populations and slows the evolution of herbicide resistance. We find that ignoring the effect of environmental variation can lead to an over- or under-prediction of the speed of resistance evolution. The effect of environmental variation in herbicide effectiveness depends on the resistance allele frequency in the weed population at the beginning of the simulation. Finally, we find that degree of dominance and ploidy level have a much larger effect on the predicted speed of resistance evolution compared to the rate of gene flow.

Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning.

Crop and weed discrimination in natural field environments is still challenging for implementing automatic agricultural practices, such as weed control. Some weed control methods have been proposed. However, these methods are still restricted as they are implemented under controlled conditions. The development of a sound weed control system begins by recognizing the crop and the different weed plants presented in the field. In this work, a classification approach of Zea mays L. (Crop), narrow-leaf weeds (NLW), and broadleaf weeds (BLW) from multi-plant images are presented. Moreover, a large image dataset was generated. Images were captured in natural field conditions, in different locations, and growing stages of the plants. The extraction of regions of interest (ROI) is carried out employing connected component analysis (CCA), whereas the classification of ROIs is based on Convolutional Neural Networks (CNN) and compared with a shallow learning approach. To measure the classification performance of both methods, accuracy, precision, recall, and F1-score metrics were used. The best alternative for the weed classification task at early stages of growth and in natural corn field environments was the CNN-based approach, as indicated by the 97% accuracy value obtained.

Transformer-Based Weed Segmentation for Grass Management.

Weed control is among the most challenging issues for crop cultivation and turf grass management. In addition to hosting various insects and plant pathogens, weeds compete with crop for nutrients, water and sunlight. This results in problems such as the loss of crop yield, the contamination of food crops and disruption in the field aesthetics and practicality. Therefore, effective and efficient weed detection and mapping methods are indispensable. Deep learning (DL) techniques for the rapid recognition and localization of objects from images or videos have shown promising results in various areas of interest, including the agricultural sector. Attention-based Transformer models are a promising alternative to traditional constitutional neural networks (CNNs) and offer state-of-the-art results for multiple tasks in the natural language processing (NLP) domain. To this end, we exploited these models to address the aforementioned weed detection problem with potential applications in automated robots. Our weed dataset comprised of 1006 images for 10 weed classes, which allowed us to develop deep learning-based semantic segmentation models for the localization of these weed classes. The dataset was further augmented to cater for the need of a large sample set of the Transformer models. A study was conducted to evaluate the results of three types of Transformer architectures, which included Swin Transformer, SegFormer and Segmenter, on the dataset, with SegFormer achieving final Mean Accuracy (mAcc) and Mean Intersection of Union (mIoU) of 75.18% and 65.74%, while also being the least computationally expensive, with just 3.7 M parameters.

Molecular basis of strigolactone perception in root-parasitic plants: aiming to control its germination with strigolactone agonists/antagonists.

The genus Striga, also called "witchweed", is a member of the family Orobanchaceae, which is a major family of root-parasitic plants. Striga can lead to the formation of seed stocks in the soil and to explosive expansion with enormous seed production and stability once the crops they parasitize are cultivated. Understanding the molecular mechanism underlying the communication between Striga and their host plants through natural seed germination stimulants, "strigolactones (SLs)", is required to develop the technology for Striga control. This review outlines recent findings on the SL perception mechanism, which have been accumulated in Striga hermonthica by the similarity of the protein components that regulate SL signaling in nonparasitic model plants, including Arabidopsis and rice. HTL/KAI2 homologs were identified as SL receptors in the process of Striga seed germination. Recently, this molecular basis has further promoted the development of various types of SL agonists/antagonists as seed germination stimulants or inhibitors. Such chemical compounds are also useful to elucidate the dynamic behavior of SL receptors and the regulation of SL signaling.

Selective fertilization with phosphite allows unhindered growth of cotton plants expressing the ptxD gene while suppressing weeds.

Weeds, which have been the bane of agriculture since the beginning of civilization, are managed manually, mechanically, and, more recently, by chemicals. However, chemical control options are rapidly shrinking due to the recent rise in the number of herbicide-resistant weeds in crop fields, with few alternatives on the horizon. Therefore, there is an urgent need for alternative weed suppression systems to sustain crop productivity while reducing our dependence on herbicides and tillage. Such a development will also allay some of the negative perceptions associated with the use of herbicide-resistance genes and heavy dependence on herbicides. Transgenic plants expressing the bacterial phosphite dehydrogenase (ptxD) gene gain an ability to convert phosphite (Phi) into orthophosphate [Pi, the metabolizable form of phosphorus (P)]. Such plants allow for a selective fertilization scheme, based on Phi as the sole source of P for the crop, while offering an effective alternative for suppressing weed growth. Here, we show that, when P is supplied in the form of Phi, ptxD-expressing cotton (Gossypium hirsutum L.) plants outcompete, in both artificial substrates and natural soils from agricultural fields, three different monocot and dicot weed species intentionally introduced in the experiments, as well as weeds naturally present in the tested soils. Importantly, the ptxD/Phi system proved highly efficacious in inhibiting the growth of glyphosate-resistant Palmer amaranth. With over 250 weed species resistant to currently available herbicides, ptxD-transgenic plants fertilized with Phi could provide an effective alternative to suppressing the growth of these weeds while providing adequate nutrition to the crop.

Bioactive Diterpenes from the Brazilian Native Plant (Moquiniastrum pulchrum) and Their Application in Weed Control.

Even today, weeds continue to be a considerable problem for agriculture. The application of synthetic herbicides produces serious environmental consequences, and crops suffer loss of their activity due to the appearance of new resistant weed biotypes. Our aim is to develop new effective natural herbicides that improve the problem of resistance and do not harm the environment. This work is focused on a bioassay-guided isolation and the characterization of natural products present in Moquiniastrum pulchrum leaves with phytotoxic activity and its preliminary application in weeds. Moquiniastrum pulchrum was selected for two reasons: it is an abundant species in the Cerrado region (the second most important ecosystem in Brazil, after the Amazon)-the explanation behind its being a dominant species is a major focus of interest-and it has traditional employment in folk medicine. Six major compounds were isolated in this plant: one flavone and five diterpenes, two of which are described for the first time in the literature. Four of the six compounds exhibited phytotoxic activity in the bioassays performed. The results confirmed the phytotoxic potential of this plant, which had not been investigated until now.

Strigolactones, from Plants to Human Health: Achievements and Challenges.

Strigolactones (SLs) are a class of sesquiterpenoid plant hormones that play a role in the response of plants to various biotic and abiotic stresses. When released into the rhizosphere, they are perceived by both beneficial symbiotic mycorrhizal fungi and parasitic plants. Due to their multiple roles, SLs are potentially interesting agricultural targets. Indeed, the use of SLs as agrochemicals can favor sustainable agriculture via multiple mechanisms, including shaping root architecture, promoting ideal branching, stimulating nutrient assimilation, controlling parasitic weeds, mitigating drought and enhancing mycorrhization. Moreover, over the last few years, a number of studies have shed light onto the effects exerted by SLs on human cells and on their possible applications in medicine. For example, SLs have been demonstrated to play a key role in the control of pathways related to apoptosis and inflammation. The elucidation of the molecular mechanisms behind their action has inspired further investigations into their effects on human cells and their possible uses as anti-cancer and antimicrobial agents.