While recognizing the gaps in pesticide regulations that impact consumer safety, public health concerns associated with pesticide contamination of foods are pointed out. The strategies and research directions proposed to prevent and/or reduce pesticide adverse effects on human health and the environment are discussed. Special attention is paid to organophosphate pesticides, as widely applied insecticides in agriculture, veterinary practices, and urban areas. Biotic and abiotic strategies for organophosphate pesticide degradation are discussed from a food safety perspective, indicating associated challenges and potential for further improvements. As food systems are endangered globally by unprecedented challenges, there is an urgent need to globally harmonize pesticide regulations and improve methodologies in the area of food safety to protect human health.

Pesticide Use and Degradation Strategies: Food Safety, Challenges and Perspectives

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. 

Transforming weed management in sustainable agriculture with artificial intelligence: A systematic literature review towards weed identification and deep learning

Sensors, systems and algorithms of 3D reconstruction for smart agriculture and precision farming: A review

Artificial intelligence potential for net zero sustainability: Current evidence and prospects

Abstract The United Nations’ 17 Sustainable Development Goals stress the importance of global and local efforts to address inequalities and implement sustainability. Addressing complex, interconnected sustainability challenges requires a systematic, interdisciplinary approach, where technology, AI, and data-driven methods offer potential solutions for optimizing resources, integrating different aspects of sustainability, and informed decision-making. Sustainability research surrounds various local, regional, and global challenges, emphasizing the need to identify emerging areas and gaps where AI and data-driven models play a crucial role. The study performs a comprehensive literature survey and scientometric and semantic analyses, categorizes data-driven methods for sustainability problems, and discusses the sustainable use of AI and big data. The outcomes of the analyses highlight the importance of collaborative and inclusive research that bridges regional differences, the interconnection of AI, technology, and sustainability topics, and the major research themes related to sustainability. It further emphasizes the significance of developing hybrid approaches combining AI, data-driven techniques, and expert knowledge for multi-level, multi-dimensional decision-making. Furthermore, the study recognizes the necessity of addressing ethical concerns and ensuring the sustainable use of AI and big data in sustainability research. 

Assessing the current landscape of AI and sustainability literature: identifying key trends, addressing gaps and challenges

The generation of food production that meets the rising demand for food and ecosystem security is a big challenge. With the development of Artificial Intelligence (AI) models, there is a growing need to use them to achieve sustainable agriculture. The continuous enhancement of AI in agriculture, researchers have proposed many models in agriculture functions such as prediction,weed control, resource management, advance care of crops, and so on. This article evaluates on a systematic review of AI models in agriculture functions. It also reviews how AI models are used in identified sustainable objectives. Through this extensive review, this paper discusses considerations and limitations for building the next generation of sustainable agriculture using AI. 

How artificial intelligence uses to achieve the agriculture sustainability: Systematic review

Purpose: Small and Medium Enterprises (SMEs) are considered as the backbone of the economy, however they face many difficulties and challenges for their survival. Adapting to changing technology is one of the primary challenges SMEs face. Globally new technologies such as social media and e-commerce have become the trend in contemporary business activities in Business to Consumer (B2C) communications for SMEs. The study aims to find the factors affecting to the adoption of technology in SME sector in an emerging economy, Sri Lanka. 
Design: The study utilized Technology, Organizational, and Environmental (TOE) model. The data was collected from 102 SME owners in Western province Sri Lanka who are registered as SME sectors with less than 50 employees.  Qualitative data were collected from 7 SME owners through semi-structured interviews to evaluate new opportunities can gain via technology adaptation and to evaluate the impact of performance via technology adaptation in SME sector in Sri Lanka. 
Findings: Descriptive statistics revealed that there are substantial disparities in how organizations and the environment adapt to new technologies. The ordinal regression analysis emphasised that organizational and environmental factors significantly influence on adaption of technology. Content analysis revealed that SMEs have gained many new opportunities via adaption of technology such as expand their businesses from local to global using e-commerce platform and online money transactions. Social media has made direct business to business instant contact. Further, transparency of business transactions increase trust with suppliers and customers. 
Implications: The SME sector needs both new technology and updates existing technology status and guided towards modernization and continuous improvement process. Finding of this study will give policy makers and government officials on strengthening and arming SME sector with technology to face challenge and complete with local and international competition.

Exploring Constraints and Catalysts: A Comprehensive Analysis of Technology Adoption in Sri Lankan Small and Medium Enterprises

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How artificial intelligence uses to achieve the agriculture sustainability: Systematic review

Exploring Constraints and Catalysts: A Comprehensive Analysis of Technology Adoption in Sri Lankan Small and Medium Enterprises