The social and ethical impacts of artificial intelligence in agriculture: mapping the agricultural AI literature

Confronting the data deluge: How artificial intelligence can be used in the study of plant stress

The advent of the genomics era enabled the generation of high-throughput data and computational methods that serve as powerful hypothesis-generating tools to understand the genomic and gene functional basis of plant stress resilience. The proliferation of experimental and analytical methods used in biology has resulted in a situation where plentiful data exists, but the volume and heterogeneity of this data has made analysis a significant challenge. Current advanced deep-learning models have displayed an unprecedented level of comprehension and problem-solving ability, and have been used to predict gene structure, function and expression based on DNA or protein sequence, and prominently also their use in high-throughput phenomics in agriculture. However, the application of deep-learning models to understand gene regulatory and signalling behaviour is still in its infancy. We discuss in this review the availability of data resources and bioinformatic tools, and several applications of these advanced ML/AI models in the context of plant stress response, and demonstrate the use of a publicly available LLM (ChatGPT) to derive a knowledge graph of various experimental and computational methods used in the study of plant stress. We hope this will stimulate further interest in collaboration between computer scientists, computational biologists and plant scientists to distil the deluge of genomic, transcriptomic, proteomic, metabolomic and phenomic data into meaningful knowledge that can be used for the benefit of humanity.

Artificial intelligence and sustainable development during the pandemic: An overview of the scientific debates

The current work aims to analyze the main themes related to artificial intelligence (AI) and sustainable development during the pandemic period. This study provides an overview of the specialized literature related to AI and sustainability from the beginning of the pandemic through 2023. The present paper analyses scientific literature emphasizing both artificial intelligence's positive and negative impacts on sustainable development objectives (SDGs). To conduct the research, we employed bibliometric analysis and text-mining techniques to identify the major themes in the literature indexed in the Web of Science and Scopus databases. Firstly, we used descriptive measures to identify the authors' impact, the article production by country, the main keywords used, and other descriptive data. We further used data reduction methods based on co-word analysis (such as multiple correspondence analysis) on authors' keywords to show patterns in the themes explored in the literature. Bibliometric analysis was supplemented by text mining using Latent Dirichlet allocation (LDA) and structural topic modeling on abstracts to provide a comprehensive view of scientific debates on AI and sustainable development. Our research has identified various themes in the literature related to AI and sustainable development. These themes include social sustainability, health-related issues, AI technologies for energy efficiency, sustainability in industry and innovation, IoT technologies for smart and sustainable cities, urban planning, technologies for education and knowledge production, and the impact of technologies on SDGs. We also found that there is a significant positivity bias in the literature when discussing the impact of AI on sustainable development. Despite acknowledging certain risks, the literature tends to focus on the potential benefits of AI across various sectors. In addition, the analysis shows a growing emphasis on energy efficiency, which is facilitated by the use of AI technologies. Our study contributes to a better understanding of current scholarly discussion trends and emerging scientific avenues regarding AI and sustainable development. It also highlights the areas where research is needed and the implications for practitioners and policymakers.

Agro-industrial waste management employing benefits of artificial intelligence

By 2050, the world's population is predicted to reach over 9 billion, which requires 70% increased production in agriculture and food industries to meet demand. This presents a significant challenge for the agri-food sector in all aspects. Agro-industrial wastes are rich in bioactive substances and other medicinal properties. They can be used as a different source for manufacturing products like biogas, biofuels, mushrooms, and tempeh, the primary ingredients in various studies and businesses. Increased importance is placed on resource recovery, recycling, and reusing (RRR) any waste using advanced technology like IoT and artificial intelligence. AI algorithms offer alternate, creative methods for managing agro-industrial waste management (AIWM). There are contradictions and a need to understand how AI technologies work regarding their application to AIWM. This research studies the application of AI-based technology for the various areas of AIWM. The current work aims to discover AI-based models for forecasting the generation and recycling of AIWM waste. Research shows that agro-industrial waste generation has increased worldwide. Infrastructure needs to be upgraded and improved by adapting AI technology to maintain a balance between socioeconomic structures. The study focused on AI's social and economic impacts and the benefits, challenges, and future work in AIWM. The present research will increase recycling and reproduction with a balance of cost, efficiency, and human resources consumption in agro-industrial waste management.

A management framework for sudden water pollution: A systematic review output

Numerous sudden water pollution (SWP) incidents have occurred frequently in recent years, constituting a potential risk to human, socio-economic, and ecological health. This paper systematically reviews the current literature, with the view to establishing a management framework for SWP incidents. Only 39 of the 327 downloaded articles were selected, and the ROSES protocol was utilized in this review. The results indicated industries, mining sites, and sewage treatment plants as key SWP contributors through accidental leakages, traffic accidents, illegal discharge, natural disasters, and terrorist attacks. These processes also presented five consequences, including the contamination of drinking water sources, disruption of drinking water supply, ecological damage, loss of human life, and agricultural water pollution. Meanwhile, five mitigation strategies included reservoir operation, real-time monitoring, early warning, and chemical and biological treatments. Although an advancement in mitigation strategies against SWP was observed in this review, previous studies reported only a few prevention strategies. Considering that this review provided an SWP-based management framework and a hydrodynamic model selection guideline, which provide a foundation for implementing proactive measures against the SWP. These guidelines and the SWP-based management framework require practical field trials for future studies. PRACTITIONER POINTS: Sudden water pollution increases with industrial growth but decrease with awareness. Human and ecosystem health and social economy are the endpoint receptacles. Mitigation strategies include reservoir dispatch, early warning, and treatments. DPSIR model forms the basis for proving proactive measures against sudden pollution. This review provides a guideline for the selection hydrodynamic models application.

AI ethics on the road to responsible AI plant science and societal welfare

The swiftness of artificial intelligence (AI) progress in plant science begets relevant ethical questions with significant scientific and societal implications. Embracing a principled approach to regulation, ethics review and monitoring, and human-centric interpretable informed AI (HIAI), we can begin to navigate our voyage towards ethical and socially responsible AI.

Agricultural biotechnology for sustainable food security

Of late, global food security has been under threat by the coronavirus disease 2019 (COVID-19) pandemic and the recent military conflict in Eastern Europe. This article presents the objectives of the Sustainable Development Goals and the European Green Deal related to achieving food security and sustainable development in European Union (EU) agriculture, taking the aforementioned threats into account. In addition, it discusses the future of plant agricultural biotechnology and artificial intelligence (AI) systems, considering their potential for reaching the goal of food security. Paradoxically, the present challenging situation may allow politicians and stakeholders of the EU to realize opportunities and use the potential of the biotechnology sector.

An Artificial Neural Network-Based Pest Identification and Control in Smart Agriculture Using Wireless Sensor Networks

Despite living in a rural country, farmers in India face several challenges. Every year, they suffer significant losses due to agricultural insect infestation. These losses are primarily the result of inadequate field surveillance, crop diseases, and ineffective pesticide management. We need cutting-edge technology that is constantly evolving to maintain control over such major concerns responsible for output reductions year after year. Wireless sensor networks address all of these issues; in fact, wireless sensor network technology is quickly becoming the backbone of modern precision agriculture. We propose a strategy for pest monitoring using wireless sensor networks in this study by simply recognizing insect behaviour using various sensors. We proposed a rapid and accurate insect detection and categorization approach based on five important crops and associated insect pests. This method examines insect behaviour by collecting data from sensors placed in the field. The results show that the proposed work improves the accuracy of the existing work by 3.9 percent.

Exploring linkages between food security and economic growth: a Systematic mapping literature review

Food security can be achieved by being carried out simultaneously alongside the economy's growth at the macro level. While many countries worldwide carry out economic growth policies to improve food security, the causal relationship between economic growth and food security is still debated. This study uses a systematic mapping review to analyze the relationship between food security (FS) and economic growth (EG) using a systematic mapping review. There are 26 previous research results from 780 articles obtained. Database from google scholar, ScienceDirect, Elsevier, and JSTOR with a limited date range on published information from 2004-2021. The result shows an empirical gap in the relationship between FS dan EG with 76.92% supporting the correlative relationship between FS and EG, while the other 19.23% claimed that there is no correlation, and 3.85% (one study) explored the relationship between EG and FI (Food Insecurity). Furthermore, 11 studies explained that EG has a positive effect on FS; one study stated that it has a negative impact, and another one hurts Food insecurity. Meanwhile, seven studies revealed that FS has a positive effect on economic growth, one study on the contrary, and two studies explained it has no effect. Availability and GDP per Capita variables were mainly used in describing the relationship between FS and EG.

Ethical aspects of AI robots for agri-food; a relational approach based on four case studies

These last years, the development of AI robots for agriculture, livestock farming and food processing industries is rapidly increasing. These robots are expected to help produce and deliver food more efficiently for a growing human population, but they also raise societal and ethical questions. As the type of questions raised by these AI robots in society have been rarely empirically explored, we engaged in four case studies focussing on four types of AI robots for agri-food ‘in the making’: manure collectors, weeding robots, harvesting robots and food processing robots which select and package fruits, vegetables and meats. Based on qualitative interviews with 33 experts engaged in the development or implementation of these four types of robots, this article provides a broad and varied exploration of the values that play a role in their evaluation and the ethical questions that they raise. Compared to the recently published literature reviews mapping the ethical questions related to AI robots in agri-food, we conclude that stakeholders in our case studies primarily adopt a relational perspective to the value of AI robots and to finding a solution to the ethical questions. Building on our findings we suggest it is best to seek a distribution of tasks between human beings and robots in agri-food, which helps to realize the most acceptable, good or just collaboration between them in food production or processing that contributes to realizing societal goals and help to respond to the 21 century challenges.