Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Characterization of Small-Scale Farmers and Assessment of Their Access to Crop Production Information in Selected Counties of Kenya

Small-scale farming accounts for 78% of total agricultural production in Kenya and contributes to 23.5% of the country’s GDP. Their crop production activities are mostly rainfed subsistence with any surplus being sold to bring in some income. Timely decisions on farm practices such as farm preparation and planting are critical determinants of the seasonal outcomes. In Kenya, most small-scale farmers have no reliable source of information that would help them make timely and accurate decisions. County governments have extension officers who are mandated with giving farmers advisory services to farmers but they are not able to reach most farmers due to facilitation constraints. The mode and format of sharing information is also critical since it’s important to ensure that it’s timely, well-understood and usable. This study sought to assess access to geospatial derived and other crop production information by farmers in four selected counties of Kenya. Specific objectives were to determine the profile of small-scale farmers in terms of age, education and farm size;to determine the type of information that is made available to them by County and Sub-County extension officers including the format and mode of provision;and to determine if the information provided was useful in terms of accuracy, timeliness and adequacy. The results indicated that over 80% of the farmers were over 35 years of age and over 56% were male. Majority had attained primary education (34%) or secondary education (29%) and most farmers in all the counties grew maize (71%). Notably, fellow farmers were a source of information (71%) with the frequency of sharing information being mostly seasonal (37%) and when information was available (43%). Over 66% of interviewed farmers indicating that they faced challenges while using provided information. The results from the study are insightful and helpful in determining effective ways of providing farmers with useful information to ensure maximum benefits.

Dual carbon goal and agriculture in China:Exploring key factors influencing farmers’behavior in adopting low carbon technologies

Identifying the factors influencing farmers’adoption of low-carbon technologies(FA)and understanding their impacts are essential for shaping effective agricultural policies amied at emission reduction and carbon sequestration in China.This study employs a meta-analysis of 122 empirical studies,delves into 23 driving factors affecting FA and addresses the inconsistencies present in the existing literature.We systematically examine the effect size,source of heterogeneity,and time-accumulation effect of the driving factors on FA.We find that significant heterogeneity in the factors influencing FA,except for farming experience,sources of heterogeneity from the survey zone,methodology model,technological attributes,report source,financial support,and the sampling year.Additionally,age,farming experience,and adoption cost negatively correlate with FA.In contrast,educational level,health status,technical training,economic and welfare cognition,land contract,soil quality,terrain,information accessibility,demonstration,government promotion,government regulation,government support,agricultural cooperatives member,peer effect,and agricultural income ratio demonstrate a positive correlation.Especially,demonstration and age show a particularly strong correlation.Finally,the effect of demonstration,age,economic and welfare cognition,farming experience,land contract,soil quality,information accessibility,government promotion,and support,as well as agricultural cooperative membership and peer effects on FA,are generally stable but exhibit varying degrees of attenuation over time.The effect of village cadre,family income,farm scale,gender,health status,technical training,and off-farm work on FA show notable temporal shifts and maintain a weak correlation with FA.This study contributes to shaping China’s current low-carbon agriculture policies across various regions.It encourages policymakers to comprehensively consider the stability of key factors,other potential factors,technological attributes,rural socio-economic context,and their interrelations.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Cholinesterase Activity as an Indicator of Health Risks among Kou Valley Farmers

Introduction: Pesticides are currently an essential component of agricultural production techniques for controlling pests and weeds. In Burkina Faso, non-compliance with good practice in the use of pesticides poses a real health problem for the population. This study examines the health risks associated with pesticide management in rice-growing areas. Material and Methods: A field survey was conducted in Bama, involving farmers, focusing on their socio-demographic characteristics, pesticide usage, and health effects. Cholinesterase levels were measured in subsample of farmers using a portable device. Data were analysed using Microsoft Excel, calculating means and percentages for various practices. Health consultations, protection methods, and pesticide management were studied. Erythrocyte acetylcholinesterase activity was compared before and after treatment. Data were categorised into classes based on inhibition levels, and correlation analyses determined relationships between variables such as age, years of experience, and cholinesterase activity. Results: The results indicate that rice cultivation is mainly carried out by a fairly young population, with nearly 63% being under the age of 50. Common poor practices in pesticide use include improper storage and reuse of leftover pesticides. Seven types of pesticides were identified, including organophosphates such as glyphosate, which was used in 26.7% of cases. This organophosphate has resulted in class B poisoning, causing a 30% - 50% reduction in erythrocyte cholinesterase activity. The health effects of pesticide use are felt by agricultural farmers through various symptoms of poisoning. Conclusion: To reduce the occurrence of pesticide poisoning, it is essential to launch information and awareness campaigns among the population and farmers to promote safe practices in pesticide use in Bama, Burkina Faso.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Research on Maneuver Decision-Making of Multi-Agent Adversarial Game in a Random Interference Environment

The strategy evolution process of game players is highly uncertain due to random emergent situations and other external disturbances.This paper investigates the issue of strategy interaction and behavioral decision-making among game players in simulated confrontation scenarios within a random interference environment.It considers the possible risks that random disturbances may pose to the autonomous decision-making of game players,as well as the impact of participants’manipulative behaviors on the state changes of the players.A nonlinear mathematical model is established to describe the strategy decision-making process of the participants in this scenario.Subsequently,the strategy selection interaction relationship,strategy evolution stability,and dynamic decision-making process of the game players are investigated and verified by simulation experiments.The results show that maneuver-related parameters and random environmental interference factors have different effects on the selection and evolutionary speed of the agent’s strategies.Especially in a highly uncertain environment,even small information asymmetry or miscalculation may have a significant impact on decision-making.This also confirms the feasibility and effectiveness of the method proposed in the paper,which can better explain the behavioral decision-making process of the agent in the interaction process.This study provides feasibility analysis ideas and theoretical references for improving multi-agent interactive decision-making and the interpretability of the game system model.

Local Criteria Used by Farmers to Evaluate the Agronomic Performance and the Fertilizing Capacity of Cowpea Varieties: Diversity, Variability and Proximal Relation with Agronomic Measurements in Contrasted Sahelian Locations

When contributing to participatory research, farmers usually appreciate the performance of cowpea varieties using qualitative scores. The score they attribute to each variety are based on local knowledge. The specific criteria they individually use to attribute a score are not well described. The objectives of this work were to: 1) identify and describe exhaustively the local criteria used by farmers to measure the agronomic performance of cowpea;2) assess the variability and statistical structure of these farmer criteria across local contexts;3) and analyze the association between these farmer criteria and the classical agronomic measurement. To achieve these objectives, an augmented block design was implemented across fifteen locations in the regions of Maradi, Dosso and Tillabéri, representing a diversity of local contexts. From a set of 36 cowpea varieties, fifteen varieties were sown per location, including five varieties (controls) common to all locations. In each location, two replicates were sown in randomized Fisher’s blocks. After agronomic measurement and participatory evaluation (scoring of varieties by farmers), a group survey (focus group) was conducted in each location to identify the criteria considered by farmers to found their discretional scoring of varieties during the participatory evaluation. The analysis of the data identified, across locations, thirteen criteria defined by farmers to characterize the agronomic performance of cowpea. Some of these criteria were different according to location. Farmers ranked the three varieties with the best performance for each agronomical trait (Top 3 varieties). A comparison of the farmer ranking with the ranking based on agronomic measurements revealed similarity and complementary between both methods. This study highlighted the importance of considering both local and scientific knowledge in local varietal evaluations.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

Navigating Market Choices: Understanding Carrot Market Outlet Selection among Smallholder Farmers in Kenya

This research delves into the hurdles and strategies aimed at augmenting the market involvement of smallholder carrot farmers in Nakuru County, Kenya. Employing a Multinomial Logit (MNL) model, it scrutinizes the factors influencing the selection of marketing outlets among carrot farmers. The findings unveil that a significant majority (81%) of surveyed farmers actively participate in diverse market outlets, encompassing the farm gate, cleaning point, local market, external market, and export market. Notably, pivotal buyers include aggregators, brokers, wholesalers, retailers, and consumers, with transactions predominantly occurring at the farm level. Additionally, the analysis discerns substantial influences of socio-economic characteristics, experiential factors, and geographical proximity on farmers’ choices of market outlets. Specifically, gender, age, land size, farming experience, and distance to markets emerge as critical determinants. Moreover, the study delves into the examination of market margins along the carrot value chain, shedding light on the potential profitability of carrot farming in the region. Remarkably, higher average gross margins are identified in export and external markets, signaling lucrative prospects for farmers targeting these segments. However, disparities in profit distribution between farmers and traders underscore the necessity for interventions to ensure equitable value distribution throughout the value chain. These findings underscore the imperative for tailored interventions to tackle challenges and foster inclusive agricultural development. Strategies such as farmer organizations, contracting, and vertical integration are advocated to enhance market access and profitability for smallholder carrot farmers. Thus, this study enriches our comprehension of the dynamics within carrot value chains and provides valuable insights for policymakers and development practitioners aiming to uplift rural livelihoods and bolster food security.

Climatic and non-climatic factors driving the livelihood vulnerability of smallholder farmers in Ahafo Ano North District,Ghana

Smallholder farmers in Ahafo Ano North District,Ghana,face multiple climatic and non-climatic issues.This study assessed the factors contributing to the livelihood vulnerability of smallholder farmers in this district by household surveys with 200 respondents and focus group discussions(FGDs)with 10 respondents.The Mann–Kendall trend test was used to assess mean annual rainfall and temperature trends from 2002 to 2022.The relative importance index(RII)value was used to rank the climatic and non-climatic factors perceived by respondents.The socioeconomic characteristics affecting smallholder farmers’perceptions of climatic and non-climatic factors were evaluated by the binary logistic regression model.Results showed that mean annual rainfall decreased(P>0.05)but mean annual temperature significantly increased(P<0.05)from 2002 to 2022 in the district.The key climatic factors perceived by smallholder farmers were extreme heat or increasing temperature(RII=0.498),erratic rainfall(RII=0.485),and increased windstorms(RII=0.475).The critical non-climatic factors were high cost of farm inputs(RII=0.485),high cost of healthcare(RII=0.435),and poor condition of roads to farms(RII=0.415).Smallholder farmers’perceptions of climatic and non-climatic factors were significantly affected by their socioeconomic characteristics(P<0.05).This study concluded that these factors negatively impact the livelihoods and well-being of smallholder farmers and socioeconomic characteristics influence their perceptions of these factors.Therefore,to enhance the resilience of smallholder farmers to climate change,it is necessary to adopt a comprehensive and context-specific approach that accounts for climatic and non-climatic factors.

A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying capacity.This approach can capture the imprecision and ambiguity often present in human perception.With the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making algorithms.By implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

Researching the Impact of the Quality of Village Governance on Farmers’Well-Being:From the Perspectives of Income and Occupational Differentiation

The goal of village governance is to improve the well-being of farmers,so this study aims to measure the impact the quality of village governance on the well-being of farmers.It also examines the heterogeneity of this impact across different farmer groups from the perspectives of income levels and occupational differentiation.To this end,this study developed an indicator system based on survey data collected from 1,442 farmers in the Sichuan,Shaanxi,and Gansu provinces,as well as the Ningxia Hui autonomous region.Multiple linear regression models were then used to analyze this data,and the findings revealed that improvements in the quality of village governance significantly increased the well-being of farmers.Specifically,primary-level empowerment and capacity building were shown to contribute the most to the enhancement of the farmers’well-being,followed by social inclusion,and social cohesion was found to have only a minimal effect.In terms of income levels,improving the quality of village governance benefited middle-income farmers the most,followed by low-income farmers,and it had the least effect on high-income farmers.In terms of occupations,full-time farmers gained the most from improvements in the quality of village governance,followed by off-farm farmers,with part-time farmers benefiting the least.Based on these findings,this study suggests that policymakers should improve the quality of village governance to enhance the well-being of farmers,focusing on the impact that level of income and occupational differentiation have on village governance.

UAV maneuvering decision-making algorithm based on deep reinforcement learning under the guidance of expert experience

Autonomous umanned aerial vehicle(UAV) manipulation is necessary for the defense department to execute tactical missions given by commanders in the future unmanned battlefield. A large amount of research has been devoted to improving the autonomous decision-making ability of UAV in an interactive environment, where finding the optimal maneuvering decisionmaking policy became one of the key issues for enabling the intelligence of UAV. In this paper, we propose a maneuvering decision-making algorithm for autonomous air-delivery based on deep reinforcement learning under the guidance of expert experience. Specifically, we refine the guidance towards area and guidance towards specific point tasks for the air-delivery process based on the traditional air-to-surface fire control methods.Moreover, we construct the UAV maneuvering decision-making model based on Markov decision processes(MDPs). Specifically, we present a reward shaping method for the guidance towards area and guidance towards specific point tasks using potential-based function and expert-guided advice. The proposed algorithm could accelerate the convergence of the maneuvering decision-making policy and increase the stability of the policy in terms of the output during the later stage of training process. The effectiveness of the proposed maneuvering decision-making policy is illustrated by the curves of training parameters and extensive experimental results for testing the trained policy.

Cattle Production and Lumpy Skin Disease of Smallholder Farmers in Svay Rieng Province

The cattle production in Romeas Haek district,Svay Rieng province,was developed remarkably,since there were some households raising cattle in large number,more than 20 heads.The grazing method of cattle was tethering and free grassing in the commune grass,since the natural grass and rice straw were the main source of cattle feed,however some of them supplemented their cattle with crop-byproducts,planting grass or concentrated feed.For the majority,BCS(Body Condition Scoring)of the cattle in those areas was 3 accounting for 52%-73.33%,but it was found the thin cattle has low BCS of 2 accounting for 11.11%to 40.00%and also fat cattle has BCS of 4 up to 27.78%.However,the average BCS varied with village which ranged from 2.68 to 3.17,but is not different in term of commune and sex of cattle.Besides facing with shortage of feed,the experience with diseases was also another concern,since 85.71%to 95.83%of the households faced this problem last year.Among the clinical signs found,lumpy skin was 100%,i.e.no one escaped from this problem,thus making them aware of vaccinating their cattle against some diseases such as FMD(Foot and Mouth Disease),HS(Hemorrhagic Septicemia)and LSD(Lumpy Skin Disease),but mostly vaccination was used against FMD.Because the LSD has occurred in these areas since 2020,then the seroprevalence was found in high positive up to 72.62%by Enzyme-Linked Immunosorbent Assay(ELISA)test.This positive rate was also varying by villages,ranging from 53.3%to 94.44%.But there was no association between positive cattle with commune,BCS,sex and age of cattle.In conclusion,the cattle production in Romeas Haek district,Svay Rieng province had developed markedly,however the shortage of feed and disease occurrence are the main challenges for farmer cattle producers,especially LSD outbreak last year.However,most of the farmers have been aware of the benefit of vaccination and had vaccinated their cattle against those main disease,such as FMD,HS and LSD.

Influence of social media on maternal decision-making and breastfeeding practices

Breastfeeding practices are influenced by multifactorial determinants including individual characteristics,external support systems,and media influences.This commentary emphasizes such complex factors influencing breastfeeding practices.Potential methodological limitations and the need for diverse sampling in studying breastfeeding practices are highlighted.Further research must explore the interplay between social influences,cultural norms,government policies,and individual factors in shaping maternal breastfeeding decisions.

Decision-Making and Management of Self-Care in Persons with Traumatic Spinal Cord Injuries: A Preliminary Study

Patients and physicians understand the importance of self-care following spinal cord injury (SCI), yet many individuals with SCI do not adhere to recommended self-care activities despite logistical supports. Neurobehavioral determinants of SCI self-care behavior, such as impulsivity, are not widely studied, yet understanding them could inform efforts to improve SCI self-care. We explored associations between impulsivity and self-care in an observational study of 35 US adults age 18 - 50 who had traumatic SCI with paraplegia at least six months before assessment. The primary outcome measure was self-reported self-care. In LASSO regression models that included all neurobehavioral measures and demographics as predictors of self-care, dispositional measures of greater impulsivity (negative urgency, lack of premeditation, lack of perseverance), and reduced mindfulness were associated with reduced self-care. Outcome (magnitude) sensitivity, a latent decision-making parameter derived from computationally modeling successive choices in a gambling task, was also associated with self-care behavior. These results are preliminary;more research is needed to demonstrate the utility of these findings in clinical settings. Information about associations between impulsivity and poor self-care in people with SCI could guide the development of interventions to improve SCI self-care and help patients with elevated risks related to self-care and secondary health conditions.

Farmers’ Knowledge and Awareness towards the Use of Agrochemicals in Vegetable Cultivation, Fako Cameroon

Agrochemicals are contemporary, omnipresent tool used in vegetable cultivation. Farmers’ knowledge and awareness of the proper usage of agrochemicals are critical for mitigating the negative effects on human health. This cross-sectional study was aimed at assessing the usage knowledge, risk awareness of toxicological and chemical classes, proper handling and use practices for agrochemicals homologated for use in vegetable farming, and the occurrence of health-related symptoms as a result of exposure among these farmers. The study included 93 vegetable growers from agricultural hotspot towns in Fako, southwest Cameroon. The field study, ran from November 2021 to December 2023, using a questionnaire to collect information on farmers demographic, and their knowledge of pesticide classes, and the related risk of associated with the handling of agrochemicals. Results show that all vegetable farmers, particularly those engaged in agribusiness, employ pesticide inputs to maximize production. Six pesticides, two fertilizer types, and one unknown substance were identified. While 23 active compounds were found, the most utilized were abamectin, emamectin (10.46%), dimethoate (9.30%,) and ethoprophos (8.13%). Two active chemicals, dimethoate and methalaxyl, are illegal yet remain in circulation. Toxicological classes I and II, with the greatest harmful effect on human health, were the most commonly utilized (64.27%). Thirty-nine percent of farmers never use personal protection equipment when working with agrochemicals, demonstrating a significant gap in knowledge and awareness of agrochemicals and their various applications and handling procedures in the field. The government should implement an intensive specialized educational program for on-field farmers with incentives in order to promote sustainable agriculture methods that ensure environmental and human safety.