Institut Agronomique Méditerranéen de Montpellier

Abstract Reducing pesticide use has become a goal shared by several European countries and a major issue in public policies due to the negative impacts of pesticides on the environment and on human health. However, since most of the agri-food sector relies on pesticides in these countries, substantially reducing pesticide use is a complex issue. To overcome this situation, we argue that agricultural research has a major role to play and must adopt a pesticide-free paradigm to expect a deep impact on pesticide use. In this article, we explain why this new paradigm is needed and outline research fronts that it will help address. These research fronts are related to five strategies: (1) redesigning cropping systems to enhance prophylaxis, (2) diversifying biocontrol strategies and associated business models, (3) broadening the scope of plant breeding to include functional biodiversity and evolutionary ecology concepts, (4) setting new goals for agricultural machinery and digital technologies, and (5) supporting development of public policies and private initiatives for the transition toward pesticide-free agri-food systems. The corresponding research activities must be managed conjointly to develop systemic and coupled innovations, which are essential for reducing pesticide use significantly. We therefore provide examples of cross-cutting objectives that combine these fronts while also highlighting the need for interdisciplinary research projects. By doing so, we provide an overall orientation for research to achieve sustainable agriculture.

BACKGROUND: Ultra-processed food (UPF) consumption has increased drastically worldwide and already represents 50%-60% of total daily energy intake in several high-income countries. In the meantime, the prevalence of overweight and obesity has risen continuously during the last century. The objective of this study was to investigate the associations between UPF consumption and the risk of overweight and obesity, as well as change in body mass index (BMI), in a large French cohort. METHODS AND FINDINGS: A total of 110,260 adult participants (≥18 years old, mean baseline age = 43.1 [SD 14.6] years; 78.2% women) from the French prospective population-based NutriNet-Santé cohort (2009-2019) were included. Dietary intakes were collected at baseline using repeated and validated 24-hour dietary records linked to a food composition database that included >3,500 different food items, each categorized according to their degree of processing by the NOVA classification. Associations between the proportion of UPF in the diet and BMI change during follow-up were assessed using linear mixed models. Associations with risk of overweight and obesity were assessed using Cox proportional hazard models. After adjusting for age, sex, educational level, marital status, physical activity, smoking status, alcohol intake, number of 24-hour dietary records, and energy intake, we observed a positive association between UPF intake and gain in BMI (β Time × UPF = 0.02 for an absolute increment of 10 in the percentage of UPF in the diet, P < 0.001). UPF intake was associated with a higher risk of overweight (n = 7,063 overweight participants; hazard ratio (HR) for an absolute increase of 10% of UPFs in the diet = 1.11, 95% CI: 1.08-1.14; P < 0.001) and obesity (n = 3,066 incident obese participants; HR10% = 1.09 (1.05-1.13); P < 0.001). These results remained statistically significant after adjustment for the nutritional quality of the diet and energy intake. Study limitations include possible selection bias, potential residual confounding due to the observational design, and a possible item misclassification according to the level of processing. Nonetheless, robustness was tested and verified using a large panel of sensitivity analyses. CONCLUSIONS: In this large observational prospective study, higher consumption of UPF was associated with gain in BMI and higher risks of overweight and obesity. Public health authorities in several countries recently started to recommend privileging unprocessed/minimally processed foods and limiting UPF consumption. TRIAL REGISTRATION: ClinicalTrials.gov NCT03335644 (https://clinicaltrials.gov/ct2/show/NCT03335644).

Abstract Increasing temperature due to global warming has emerged one of the gravest threats to rice production. This study examined the influence of high temperature and exogenously applied plant growth regulators on pollen fertility, anther dehiscence, pollen germination and metabolites synthesis in pollens of two rice cultivars ( IR ‐64 and Huanghuazhan (HHZ)). Plants were subjected to high day temperature ( HDT ), high night temperature ( HNT ) and control temperature ( CT ) in controlled growth chambers. Four different combinations of ascorbic acid (Vc), alpha‐tocopherol (Ve), brassinosteroids (Br), methyl jasmonates (MeJA) and triazoles (Tr) were used along with a nothing applied control. Our results depicted that high temperature severely reduced the pollen fertility, anther dehiscence, pollen retention, germination and metabolites synthesis in pollens of both rice cultivars. Nonetheless, exogenous application of various plant growth regulators assuaged the adverse effects of high temperature and Vc + Ve + MeJA + Br was found the best combination than the other treatments for every studied characteristic. The HNT posed more negative effects than the HDT . Variations were also apparent between cultivars and HHZ performed better than IR ‐64 under high‐temperature stress, with higher pollen fertility, better anther dehiscence, and greater pollen retention and germination rates. The greater tolerance of HHZ to high temperature was related with the higher synthesis of metabolites in this cultivar.

Leprosy is a debilitating infectious disease of human skin and nerves. Genetic factors of the host play an important role in the manifestation of disease susceptibility. The human NRAMP1 gene is a leprosy susceptibility candidate locus since its murine homologue Nramp1 (formerly Lsh/Ity/Bcg) controls innate resistance to Mycobacterium lepraemurium. In this study, 168 members of 20 multiplex leprosy families were genotyped for NRAMP1 alleles and 4 closely linked polymorphic markers. Highly informative haplotypes overlapping the NRAMP1 gene were constructed, and the haplotype segregation into leprosy-affected offspring was analyzed. It was observed that the segregation of NRAMP1 haplotypes into affected siblings was significantly nonrandom. This finding is consistent with the hypothesis that NRAMP1 itself is a leprosy susceptibility locus.

The processes underlying environmental, economic, and social unsustainability derive in part from the food system. Building sustainable food systems has become a predominating endeavor aiming to redirect our food systems and policies towards better-adjusted goals and improved societal welfare. Food systems are complex social-ecological systems involving multiple interactions between human and natural components. Policy needs to encourage public perception of humanity and nature as interdependent and interacting. The systemic nature of these interdependencies and interactions calls for systems approaches and integrated assessment tools. Identifying and modeling the intrinsic properties of the food system that will ensure its essential outcomes are maintained or enhanced over time and across generations, will help organizations and governmental institutions to track progress towards sustainability, and set policies that encourage positive transformations. This paper proposes a conceptual model that articulates crucial vulnerability and resilience factors to global environmental and socio-economic changes, postulating specific food and nutrition security issues as priority outcomes of food systems. By acknowledging the systemic nature of sustainability, this approach allows consideration of causal factor dynamics. In a stepwise approach, a logical application is schematized for three Mediterranean countries, namely Spain, France, and Italy.

BACKGROUND: In the NOVA classification system, descriptive criteria are used to assign foods to one of four groups based on processing-related criteria. Although NOVA is widely used, its robustness and functionality remain largely unexplored. We determined whether this system leads to consistent food assignments by users. METHODS: French food and nutrition specialists completed an online survey in which they assigned foods to NOVA groups. The survey comprised two lists: one with 120 marketed food products with ingredient information and one with 111 generic food items without ingredient information. We quantified assignment consistency among evaluators using Fleiss' κ (range: 0-1, where 1 = 100% agreement). Hierarchical clustering on principal components identified clusters of foods with similar distributions of NOVA assignments. RESULTS: Fleiss' κ was 0.32 and 0.34 for the marketed foods (n = 159 evaluators) and generic foods (n = 177 evaluators), respectively. There were three clusters within the marketed foods: one contained 90 foods largely assigned to NOVA4 (91% of assignments), while the two others displayed greater assignment heterogeneity. There were four clusters within the generic foods: three clusters contained foods mostly assigned to a single NOVA group (69-79% of assignments), and the fourth cluster comprised 28 foods whose assignments were more evenly distributed across the four NOVA groups. CONCLUSIONS: Although assignments were more consistent for some foods than others, overall consistency among evaluators was low, even when ingredient information was available. These results suggest current NOVA criteria do not allow for robust and functional food assignments.

Pulses display nutritional benefits and are recommended in sustainable diets. Indeed, they are rich in proteins and fibers, and can contain variable amounts of micronutrients. However, pulses also contain bioactive compounds such as phytates, saponins, or polyphenols/tannins that can exhibit ambivalent nutritional properties depending on their amount in the diet. We characterized the nutritional composition and bioactive compound content of five types of prepared pulses frequently consumed in France (kidney beans, white beans, chickpeas, brown and green lentils, flageolets), and specifically compared the effects of household cooking vs. canning on the composition of pulses that can be consumed one way or the other. The contents in macro-, micronutrients, and bioactive compounds highly varied from one pulse to another (i.e., 6.9 to 9.7 g/100 g of cooked product for proteins, 4.6 to 818.9 µg/100 g for lutein or 15.0 to 284.3 mg/100 g for polyphenols). The preparation method was a key factor governing pulse final nutritional composition in hydrophilic compounds, depending on pulse species. Canning led to a greater decrease in proteins, total dietary fibers, magnesium or phytate contents compared to household cooking (i.e., −30%, −44%, −33% and −38%, p &lt; 0.05, respectively, in kidney beans). As canned pulses are easy to use for consumers, additional research is needed to improve their transformation process to further optimize their nutritional quality.

BACKGROUND AND METHODS: The appearance of bluetongue virus (BTV) in 2006 within northern Europe exposed a lack of expertise and resources available across this region to enable the accurate morphological identification of species of Culicoides Latreille biting midges, some of which are the major vectors of this pathogen. This work aims to organise extant Culicoides taxonomic knowledge into a database and to produce an interactive identification key for females of Culicoides in the Western Palaearctic (IIKC: Interactive identification key for Culicoides). We then validated IIKC using a trial carried out by six entomologists based in this region with variable degrees of experience in identifying Culicoides. RESULTS: The current version of the key includes 98 Culicoides species with 10 morphological variants, 61 descriptors and 837 pictures and schemes. Validation was carried out by six entomologists as a blind trial with two users allocated to three classes of expertise (beginner, intermediate and advanced). Slides were identified using a median of seven steps and seven minutes and user confidence in the identification varied from 60% for failed identifications to a maximum of 80% for successful ones. By user class, the beginner group successfully identified 44.6% of slides, the intermediate 56.8% and the advanced 74.3%. CONCLUSIONS: Structured as a multi-entry key, IIKC is a powerful database for the morphological identification of female Culicoides from the Western Palaearctic region. First developed for use as an interactive identification key, it was revealed to be a powerful back-up tool for training new taxonomists and to maintain expertise level. The development of tools for arthropod involvement in pathogen transmission will allow clearer insights into the ecology and dynamics of Culicoides and in turn assist in understanding arbovirus epidemiology.

A sustainable diet is, by definition, nutritionally adequate, economically affordable, culturally acceptable, and environmentally respectful. Designing such a diet has to integrate different dimensions of diet sustainability that may not be compatible with each other. Among multicriteria assessment methods, diet optimization is a whole-diet approach that simultaneously combines several metrics for dimensions of diet sustainability. This narrative review based on 67 published studies shows how mathematical diet optimization can help with understanding the relations between the different dimensions of diet sustainability and how it can be properly used to identify sustainable diets. Diet optimization aims to find the optimal combination of foods for a population, a subpopulation, or an individual that fulfills a set of constraints while minimizing or maximizing an objective function. In the studies reviewed, diet optimization was used to examine the links between dimensions of diet sustainability, identify the minimum cost or environmental impact of a nutritionally adequate diet, or identify food combinations able to combine ≥2 sustainability dimensions. If some constraints prove difficult to fulfill, this signals an incompatibility between nutrient recommendations, over-monotonous food-consumption patterns, an inadequate supply of nutrient-rich foods, or an incompatibility with other dimensions. If diet optimization proves successful, it can serve to design nutritionally adequate, culturally acceptable, economically affordable, and environmentally friendly diets. Diet optimization results can help define dietary recommendations, tackle food security issues, and promote sustainable dietary patterns. This review emphasizes the importance of carefully choosing the model parameters (variables, objective function, constraints) and input data and the need for appropriate expertise to correctly interpret and communicate the results. Future research should make improvements in the choice of metrics used to assess each aspect of a sustainable diet, especially the cultural dimension, to improve the practicability of the results.

Dietary diversity has long been recognized as a key component of diet quality and many dietary diversity indicators (DDIs) have been developed. This systematic scoping review aimed to present a comprehensive inventory of DDIs and summarize evidence linking DDIs and dietary adequacy or health outcomes in adolescents and adults. Two search strategies were developed to identify peer-reviewed articles published in English up until June 2018 and were applied to Medline, Web of Science, and Scopus. A 2-stage screening process was used to select the studies to be reviewed. Four types of DDIs were identified among 161 articles, the majority of them belonging to the food group-based indicator type (n = 106 articles). Fifty studies indicated that DDIs were proxies of nutrient adequacy, but there was a lack of evidence about their relation with nutrients to limit. Associations between DDIs and health outcomes were largely inconsistent among 137 studies, especially when the outcomes studied were body weight (n = 60) and noncommunicable diseases (n = 41). We conclude that the ability of DDIs to reflect diet quality was found to be principally limited to micronutrient adequacy and that DDIs do not readily relate to health outcomes. These findings have implications for studies in low- and lower-middle-income economies where DDIs are often used to assess dietary patterns and overall diet quality.

This study proposes an effective method to map rice crops using the Sentinel-1 SAR (Synthetic Aperture Radar) time series over the Camargue region, Southern France. First, the temporal behavior of the SAR backscattering coefficient over 832 plots containing different crop types was analyzed. Through this analysis, the rice cultivation was identified using metrics derived from the Gaussian profile of the VV/VH time series (3 metrics), the variance of the VV/VH time series (one metric), and the slope of the linear regression of the VH time series (one metric). Using the derived metrics, rice plots were mapped through two different approaches: decision tree and Random Forest (RF). To validate the accuracy of each approach, the classified rice map was compared to the available national data. Similar high overall accuracy was obtained using both approaches. The overall accuracy obtained using a simple decision tree reached 96.3%, whereas an overall accuracy of 96.6% was obtained using the RF classifier. The approach, therefore, provides a simple yet precise and powerful tool to map paddy rice areas.

Phosphate-solubilizing bacteria (PSB) reduce the negative effects of soil calcification on soil phosphorus (P) nutrition. In this incubation study, we explored the ability of PSB (control and inoculated) to release P from different P sources [single super phosphate (SSP), rock phosphate (RP), poultry manure (PM) and farm yard manure (FYM)] with various soil lime contents (4.78, 10, 15 and 20%) in alkaline soil. PSB inoculation progressively enriched Olsen extractable P from all sources compared to the control over the course of 56 days; however, this increase was greater from organic sources (PM and FYM) than from mineral P sources (SSP and RP). Lime addition to the soil decreased bioavailable P, but this effect was largely neutralized by PSB inoculation. PSB were the most viable in soil inoculated with PSB and amended with organic sources, while lime addition decreased PSB survival. Our findings imply that PSB inoculation can counteract the antagonistic effect of soil calcification on bioavailable P when it is applied using both mineral and organic sources, although organic sources support this process more efficiently than do mineral P sources. Therefore, PSB inoculation combined with organic manure application is one of the best options for improving soil P nutrition.

Abstract Shifting from a linear to a circular bio‐economy requires new business models. The objective was getting insights into the uncharted research field of business model innovation for a circular and sustainable bio‐economy within the agrifood sector. Eight European cases valorising agricultural waste and by‐products by closing loops or cascading were studied regarding their innovation drivers and elements, via interviews, on‐site visits and secondary data. In this domain, the findings highlight that business model innovations are depending on the (i) macro‐environmental institutional‐legal conditions and market trends, (ii) driven by internal economic, environmental and/or social objectives, but especially strongly linked to (iii) other actors often from different sectors seeking synergies and (iv) value co‐creation via combined organisational and technological innovations. Business models for a circular bio‐economy thus depend on various action levels and need radical combined organisational and technological innovations for a most efficient usage of agricultural waste and by‐products. This also means new business configurations instead of linear innovation strategies currently still being dominant due to economic viability.

In many areas of the world, maintaining grapevine production will require adaptation to climate change. While rigorous evaluations of adaptation strategies provide decision makers with valuable insights, those that are published often overlook major constraints, ignore local adaptive capacity, and suffer from a compartmentalization of disciplines and scales. The objective of our study was to identify current knowledge of evaluation methods and their limitations, reported in the literature. We reviewed 111 papers that evaluate adaptation strategies in the main vineyards worldwide. Evaluation approaches are analyzed through key features (e.g., climate data sources, methodology, evaluation criteria) to discuss their ability to address climate change issues, and to identify promising outcomes for climate change adaptations. We highlight the fact that combining adaptation levers in the short and long term (location, vine training, irrigation, soil, and canopy management, etc.) enables local compromises to be reached between future water availability and grapevine productivity. The main findings of the paper are three-fold: (1) the evaluation of a combination of adaptation strategies provides better solutions for adapting to climate change; (2) multi-scale studies allow local constraints and opportunities to be considered; and (3) only a small number of studies have developed multi-scale and multi-lever approaches to quantify feasibility and effectiveness of adaptation. In addition, we found that climate data sources were not systematically clearly presented, and that climate uncertainty was hardly accounted for. Moreover, only a small number of studies have assessed the economic impacts of adaptation, especially at farm scale. We conclude that the development of methodologies to evaluate adaptation strategies, considering both complementary adaptations and scales, is essential if relevant information is to be provided to the decision-makers of the wine industry.

Both food safety and dietary behaviors are major contributors to the global burden of disease, especially in rapidly urbanising environments. The impact that food safety concerns have on dietary behaviors in low- and middle-income countries (LMICs) is insufficiently documented. Therefore, we examined whether food safety concerns influence consumer behaviors/diets in LMICs. A systematic review identified 46 relevant studies from 20 LMICs for inclusion. A socio-ecological food environment framework was used to map food safety factors that influence consumer behaviors (food acquisition/purchase, eating out of home, food preparation/storage) and diets (consumption of nutrient rich/poor foods). Several studies (n = 11) reported that despite food safety concerns, consumers could not always ensure that they consumed safe food; barriers were affordability, accessibility and appeal. Key concerns included fear of pesticides, fertilizers, hygiene in/around food outlets, unhygienic vendor practices and household storage/preparation methods. These concerns may reduce consumption of animal sourced food and fresh fruit and vegetables; and increase consumption of starchy staples and processed/packaged foods. Policies such as upgrading urban market infrastructure to enhance food safety, accompanied by nutrition and hygiene education, could lead to increased accessibility, affordability and appeal of safe, nutrient-rich foods. Thus, reducing the appeal of packaged/processed food as a means to mitigate food safety risk; thereby contributing to preventing foodborne disease and multiple forms of malnutrition.

The ability of Synthetic Aperture Radar (SAR) Sentinel-1 data to detect the main wheat phenological phases was investigated in the Bekaa plain of Lebanon. Accordingly, the temporal variation of Sentinel-1 (S1) signal was analyzed as a function of the phenological phases’ dates observed in situ (germination; heading and soft dough), and harvesting. Results showed that S1 data, unlike the Normalized Difference Vegetation Index (NDVI) data, were able to estimate the dates of theses phenological phases due to significant variations in S1 temporal series at the dates of germination, heading, soft dough, and harvesting. Particularly, the ratio VV/VH at low incidence angle (32–34°) was able to detect the germination and harvesting dates. VV polarization at low incidence angle (32–34°) was able to detect the heading phase, while VH polarization at high incidence angle (43–45°) was better than that at low incidence angle (32–34°), in detecting the soft dough phase. An automated approach for main wheat phenological phases’ determination was then developed on the western part of the Bekaa plain. This approach modelled the S1 SAR temporal series by smoothing and fitting the temporal series with Gaussian functions (up to three Gaussians) allowing thus to automatically detect the main wheat phenological phases from the sum of these Gaussians. To test its robustness, the automated method was applied on the northern part of the Bekaa plain, in which winter wheat is harvested usually earlier because of the different weather conditions. The Root Mean Square Error (RMSE) of the estimation of the phenological phases’ dates was 2.9 days for germination, 5.5 days for heading, 5.1 days soft dough, 3.0 days for West Bekaa’s harvesting, and 4.5 days for North Bekaa’s harvesting. In addition, a slight underestimation was observed for germination and heading of West Bekaa (−0.2 and −1.1 days, respectively) while an overestimation was observed for soft dough of West Bekaa and harvesting for both West and North Bekaa (3.1, 0.6, and 3.6 days, respectively). These results are encouraging, and thus prove that S1 data are powerful as a tool for crop monitoring, to serve enhanced crop management and production handling.

Recurrent food crises and global environmental change are critical issues that pushed food security and sustainability to the top of the policy agenda. Policy-makers need assessment tools that help them decide what actions they should take to achieve these goals. This paper proposes a new metric system assessing the sustainability of food systems and diets at a subnational level adapted to the context of the Mediterranean area. Recognizing the systemic dimension of sustainability, the proposed information system builds on a vulnerability/resilience conceptual framework and considers the interactions between a set of biophysical and socioeconomic drivers of vulnerability and a number of context-specific food and nutrition security issues. A three-round iterative Delphi survey was conducted to involve a number of selected experts in the indicator selection process. 18 indicators were finally identified for eight preselected causal models of vulnerability and resilience at the interactions between a set of four drivers of change (water depletion, biodiversity loss, food price volatility, and changes in food consumption patterns) and four food and nutrition security outcomes (nutritional quality of food supply, affordability of food, dietary energy balance, and satisfaction of cultural food preferences). Each interaction was disentangled in exposure, sensitivity and resilience. The exercise allowed discussion of a conceptual and dynamic framework for food systems, and identification of indicators that gather consensus among the expert community.

Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S1 data.

The stark observation of the co-existence of undernourishment, nutrient deficiencies and overweight and obesity, the triple burden of malnutrition, is inviting us to reconsider health and nutrition as the primary goal and final endpoint of food systems. Agriculture and the food industry have made remarkable advances in the past decades. However, their development has not entirely fulfilled health and nutritional needs, and moreover, they have generated substantial collateral losses in agricultural biodiversity. Simultaneously, several regions are experiencing unprecedented weather events caused by climate change and habitat depletion, in turn putting at risk global food and nutrition security. This coincidence of food crises with increasing environmental degradation suggests an urgent need for novel analyses and new paradigms. The sustainable diets concept proposes a research and policy agenda that strives towards a sustainable use of human and natural resources for food and nutrition security, highlighting the preeminent role of consumers in defining sustainable options and the importance of biodiversity in nutrition. Food systems act as complex social-ecological systems, involving multiple interactions between human and natural components. Nutritional patterns and environment structure are interconnected in a mutual dynamic of changes. The systemic nature of these interactions calls for multidimensional approaches and integrated assessment and simulation tools to guide change. This paper proposes a review and conceptual modelling framework that articulate the synergies and tradeoffs between dietary diversity, widely recognised as key for healthy diets, and agricultural biodiversity and associated ecosystem functions, crucial resilience factors to climate and global changes.

In the face of increasing agricultural demands and environmental concerns, the effective management of weeds presents a pressing challenge in modern agriculture. Weeds not only compete with crops for resources but also pose threats to food safety and agricultural sustainability through the indiscriminate use of herbicides, which can lead to environmental contamination and herbicide-resistant weed populations. Artificial Intelligence (AI) has ushered in a paradigm shift in agriculture, particularly in the domain of weed management. AI's utilization in this domain extends beyond mere innovation, offering precise and eco-friendly solutions for the identification and control of weeds, thereby addressing critical agricultural challenges. This article aims to examine the application of AI in weed management in the context of weed detection and the increasing impact of deep learning techniques in the agricultural sector. Through an assessment of research articles, this study identifies critical factors influencing the adoption and implementation of AI in weed management. These criteria encompass factors of AI adoption (food safety, increased effectiveness, and eco-friendliness through herbicides reduction), AI implementation factors (capture technology, training datasets, AI models, and outcomes and accuracy), ancillary technologies (IoT, UAV, field robots, and herbicides), and the related impact of AI methods adoption (economic, social, technological, and environmental). Of the 5821 documents found, 99 full-text articles were assessed, and 68 were included in this study. The review highlights AI's role in enhancing food safety by reducing herbicide residues, increasing effectiveness in weed control strategies, and promoting eco-friendliness through judicious herbicide use. It underscores the importance of capture technology, training datasets, AI models, and accuracy metrics in AI implementation, emphasizing their synergy in revolutionizing weed management practices. Ancillary technologies, such as IoT, UAVs, field robots, and AI-enhanced herbicides, complement AI's capabilities, offering holistic and data-driven approaches to weed control. Additionally, the adoption of AI methods influences economic, social, technological, and environmental dimensions of agriculture. Last but not least, digital literacy emerges as a crucial enabler, empowering stakeholders to navigate AI technologies effectively and contribute to the sustainable transformation of weed management practices in agriculture.