Mrutyunjay Padhiary

Modern agriculture is increasingly leveraging digital technologies, particularly digital twins, to enhance productivity and sustainability. Digital twins create real-time virtual models of agricultural systems, allowing for improved decision-making in crop growth, soil health, and resource use by integrating IoT sensors, AI, machine learning, satellite imagery, and cloud analytics. Studies show that these technologies can boost crop yields by 10-25%, reduce water usage by 20-30%, and cut fertilizer application by 15-20% while preserving soil quality. The proposed architecture for agricultural digital twins includes data acquisition, modeling, predictive analytics, and decision support, with applications in crop simulation, soil monitoring, and precision irrigation. Nevertheless, challenges like high infrastructure costs and data interoperability hinder broader implementation. Ultimately, digital twins represent a transformative shift toward intelligent, sustainable agricultural practices.

Sweat Behind the Fiber: Uncovering Labour Burdens in India’s Cotton Economy, Automation and Robotics Opportunities

Precision agriculture utilizes data-driven, site-specific management practices to optimize inputs, including seeds, fertilizers, and irrigation, thereby enhancing yields, reducing costs, and minimizing environmental impacts. This review provides a comprehensive analysis of recent advancements in 3D printing, the Internet of Things (IoT), and artificial intelligence (AI), and their integration for improving farm management. Three-dimensional printing enables the fabrication of customized sensors and components for accurate soil and crop monitoring. IoT-based sensor networks support real-time data collection on key agricultural parameters, while AI and image-processing techniques deliver advanced analytics for early detection of nutrient deficiencies, diseases, and stress conditions. Collectively, these technologies drive the development of automated nutrient and resource management systems that enhance efficiency, sustainability, and decision-making in farming. The review also addresses current limitations, including cost, technical expertise, and durability challenges, while emphasizing prospects and the transformative potential of integrating digital technologies with agricultural practices to achieve environmentally friendly and resource-efficient farming.

Drying techniques play a crucial role in food preservation, ensuring quality, safety, and extended shelf life for agricultural products. This study reviews refractance window drying (RWD) as an energy‐efficient and sustainable approach for drying heat‐sensitive agricultural products while evaluating its potential integration with traditional drying methods to improve efficiency and reduce energy consumption. RWD, in comparison to conventional techniques, like drum drying, emphasizes advantages in energy efficiency, nutritional preservation, and drying time. RWD reduces energy consumption by 40%–50% and preserves more vitamins and antioxidants than traditional methods. It reduces drying time by 30%–40% compared to hot‐air oven drying. The study examines the impact of RWD on the product's quality parameters, including texture and color, which are crucial for both industrial and commercial products. The findings indicate that RWD is a reliable alternative to conventional drying methods in both laboratory and industrial environments. A comprehensive bibliometric analysis of a total of 110 peer‐reviewed research studies showed that RWD improved energy efficiency by requiring 30%–50% less energy than conventional methods. RWD retained a much higher percentage of quality, with vitamins and antioxidants preserving over 90% of their integrity, compared to 60%–80% for other methods. Furthermore, RWD markedly reduced microbial load, achieving a 3–4 log reduction that exceeds the effectiveness of the majority of different drying methods. Artificial intelligence and machine learning in practical data operations led to an efficiency improvement of 15%–20% in the drying process while enhancing product quality and reducing operational expenses. RWD is an efficient method for drying heat‐sensitive crops, providing significant advantages in energy efficiency, quality retention, and microbiological safety.

Bio-nanostructured composite materials, composed of biological and nanostructured elements, have added a new dimension to the latest agricultural developments. This chapter explores how these materials can be optimized using artificial intelligence to enhance soil fertility, structure, and ecosystem resilience. Precision design, performance prediction, and real-time monitoring of bio-nano composites, ensuring their targeted functionality in soil remediation, nutrient delivery, and pollution mitigation, are being enabled by AI algorithms, including machine learning and deep learning. AI-integrated systems might support frameworks for sustainable land management, forecast environmental effects, and replicate soil-microbe-nano synthesis. Smart nano-fertilizers, AI-assisted clean-up methods, and sensors in materials for checking soil quality demonstrate a shift toward smarter, more sustainable farming and caring for the environment. It also explores the ethical and hazardous aspects of AI-enabled bio-nano technologies and their potential impact on future-proof agri-environmental systems.

Each facet of everyday existence is utilizing the Internet of Things (IoT) to enhance the standard of existence. As the number of people in smart cities continues to rise, efficient waste administration has emerged as the main obstacle. The inefficiency of conventional waste administration platforms frequently results in needless travel, expensive operating expenses, trouble monitoring waste, and wasteful utilization of assets. Despite the fact that conventional waste administration in factories is typically dispersed, opaque, and unsustainable, the issue of commercial waste is one of the main causes of pollutants in the world. One of the most important managerial divisions for handling city garbage and providing an environmentally sustainable setting for city residents is smart waste management (SWM). To improve the SWM process and reduce waste creation, scientists typically use and create a variety of methods. However, there are nevertheless issues with garbage production administration. Therefore, to achieve the goal of minimizing waste, innovation that may include people is required. Since blockchain offers uses for time tracking, safe data exchanges, and automated incentive systems, it is now a suitable solution for SWM. An artificial intelligence (AI) garbage categorizing strategy that combines blockchain and IoT innovations is presented in this research. Data is sent from IoT-based bins to a centralized system, which employs blockchain technology to guarantee safe, open data preservation. Real-time trash classification by AI methods, such as machine learning (ML) and deep learning (DL), maximizes recycling and waste pickup. Data authenticity is guaranteed via blockchain, and filtering performance is improved using ML and DL methods. By using safe data administration and smart decision-making, the approach seeks to enhance long-term viability and SWM.