Plant diseases present a major risk to worldwide food security and the sustainability of agriculture, leading to substantial economic losses and hindering rural livelihoods. Conventional methods for disease detection, including visual inspection and laboratory-based techniques, are limited in their scalability, efficiency, and accuracy. This paper addresses the critical problem of accurately detecting and diagnosing plant diseases using advanced machine learning techniques, specifically vision transformers (ViTs), to overcome these limitations. ViTs leverage self-attention mechanisms to capture intricate patterns in plant images, enabling accurate and efficient disease classification. This paper reviews the literature on deep learning techniques in agriculture, emphasizing the growing interest in ViTs for plant disease detection. Additionally, it presents a comprehensive methodology for training and evaluating ViT models for plant disease classification tasks. Experimental results demonstrate the effectiveness of ViTs in accurately identifying various plant diseases across a balanced 55 classes dataset, highlighting their potential to revolutionize precision agriculture and promote sustainable farming practices.

Considering the main pillars of sustainable development, this paper proposes a new multi-objective model to design a sustainable closed-loop location routing supply chain network taking the pharmaceutical products life cycle as a case study. This model aims at generating economic gains, increasing the social responsibility of companies in terms of job creation and reducing the risk arising from the transport of end of Life products (medical waste from the expiry of pharmaceutical products and their use in the healthcare centres). The multi-objective model expressed as mixed integer linear program was solved by an exact approach, this resolution allowed us to take strategic and operational decisions. Besides, the tuning of objective’s weight helps us to analyse the trade-off possibility between the different objectives and to highlight the impact of social responsibility on the design of closed-loop routing supply chain networks.

This research investigates the synergistic relationship between Lean Manufacturing and Industry 4.0, crucial paradigms in modern industry, through a literature review of 60 scientific articles published from January 2013 to November 2023. The study reveals a tripartite relationship: Industry 4.0 technologies amplify Lean Manufacturing’s efficiency; Lean Manufacturing principles facilitate Industry 4.0 adoption; and their interaction fosters mutual enhancement, indicating a dynamic interplay. The results show three main trends: combining Industry 4.0 technologies, especially the Internet of Things, with Lean Manufacturing principles to make things more efficient and productive; highlighting the important role of Lean Manufacturing and Industry 4.0 in tackling sustainability issues and encouraging eco-friendly practices; and combining Lean principles with Industry 4.0 as a step toward Industry 5.0. Challenges include the need for a cultural transformation to align Lean’s simplicity with Industry 4.0’s complexity, resolve compatibility issues between legacy systems and new technologies, enhance data management and cybersecurity, address skill deficiencies through staff training, and ensure supplier and partner alignment. This research enriches academic and industrial discourse by presenting a novel outlook on the convergence of lean manufacturing and Industry 4.0, highlighting its significance for future works.

This review investigates the application of Artificial Intelligence (AI), deep learning (DL), and the Internet of Things (IoT) in water resource management, focusing on distribution optimization, demand prediction, and water quality enhancement. The study synthesizes findings from 2015 to 2024, encompassing experimental and applied research published in English or French in recognized scientific outlets. By analyzing the prevalent algorithms, IoT technologies, and their impacts, this systematic review highlights research gaps and proposes directions for future work. The results show significant advancements in predictive analytics and real-time monitoring through AI and the IoT. However, challenges remain in scalability, interdisciplinary integration, and contextual adaptation.

Solid waste management (SWM) poses a major global challenge with significant environmental implications. The integration of artificial intelligence (AI) and information and communication technology (ICT) has emerged as a promising solution to revolutionise waste management practices. This systematic literature review, which examines the application of AI and ICT in SWM over the past 5 years (2018–2023) and analyses 152 research papers, explores their integration at various stages. In the production phase, AI-driven predictive models have outperformed traditional methods, improving waste forecasting accuracy and facilitating recycling initiatives. In waste collection, AI and ICT enable real-time route optimisation, dynamic scheduling, and sensor-based monitoring, enhancing service delivery while reducing operational costs. Furthermore, AI-powered technologies have revolutionised waste sorting, precisely identifying and segregating recyclables from mixed waste streams, thereby increasing recycling rates and alleviating the burden on landfills. The article also identifies the constraints and challenges associated with these technologies and discusses potential strategies to address them. The main objective of this review is to provide guidance to SWM researchers interested in utilising these technologies within their field. In addition, it aims to enrich the ongoing conversation about sustainable waste management by offering insights into current practices and future trends.

To promote sustainable development, adopting circular economy principles is crucial for preserving natural resources and ensuring environmental continuity. Among solid waste management strategies, composting plays a significant role by converting biodegradable waste into eco-friendly biofertilizers. Traditional composting methods, which rely on open-window techniques, face challenges in controlling critical physico-chemical parameters such as temperature, humidity, and gaseous emissions. Additionally, these methods require significant labor and over 100 days to achieve compost maturity. To address these issues, we propose an intelligent, automated composting system leveraging the Internet of Things (IoT) and wireless sensor networks (WSNs). This system integrates sensors for real-time monitoring of key parameters: DS18b20 for waste temperature, HD-38 for humidity, DHT11 for ambient conditions, and MQ sensors for detecting CO₂, NH₃, and CH₄. Controlled by an ESP32 microcontroller unit (MCU), the system employs a mixer and heating elements to optimize waste degradation based on sensor feedback. Data transmission is managed using the MQTT protocol, allowing real-time monitoring via a cloud-based platform (ThingSpeak). Furthermore, the degradation process was analyzed during the first 24 h, and a recurrent neural network (RNN) algorithm was employed to predict the time required for reaching optimal compost maturity, ensuring an efficient and sustainable solution.