In precision agriculture, robotic arms exhibit significant technical advantages, such as enhancing operational precision and efficiency, reducing labor costs, and supporting environmental sustainability. This paper provides a comprehensive overview of the application of ground-based robotic arms in precision agriculture, analyzing the hardware and software aspects and current application status across various agricultural settings, and discussing challenges and prospects in this field. First, this paper explores precision agriculture and agricultural robotic arms, highlighting their critical roles in enhancing agricultural efficiency and automation. Further, it addresses the challenges plaguing the practical applications of robotic arms and compares innovative robotic arm technologies with traditional models to establish a foundation for understanding these advancements in modern agriculture. Additionally, this paper analyses the hardware of robotic arms, including rigid and flexible manipulators, drivers, end-effectors, sensors, and controllers, emphasizing the importance of innovation and optimization for improved performance. For the software systems, this paper focused on classic workflows and advanced algorithms for perception, motion planning, and control, as these are essential for the precise and adaptable functioning of robotic arms in diverse agricultural environments. Furthermore, this paper reviews the research and application status of robotic arms across various settings, including greenhouses (e.g., ground planting, desktop planting, and vertical planting), fields (e.g., dry fields, moist, and paddy fields), and orchards (e.g., fruit tree orchards, vineyard orchards, and ground-level orchards) to demonstrate their broad applicability and efficient operational capabilities in diverse conditions. Lastly, this paper discusses the challenges and prospects of robotic arms, emphasizing the significance of integrating disciplines, such as agronomy and biomimetics, big data, artificial intelligence, digital twinning, and human–machine interaction. Advancements in these areas are pivotal for the progress of robotic arm technology and for introducing innovative, efficient solutions to precision agriculture. In summary, this review reveals the immense potential of the application of robotic arms in precision agriculture. With ongoing technological advancements, these robotic arms are expected to play an increasingly crucial role in future agricultural production, making substantial contributions to achieving more efficient, innovative, and sustainable farming practices, heralding a new era in agricultural technology. This paper will serve as a valuable guide for researchers and practitioners, offering comprehensive insights into the use of robotic arms in precision agriculture and providing essential knowledge for advancing the field. 

Robotic arms in precision agriculture: A comprehensive review of the technologies, applications, challenges, and future prospects

The application of Deep Learning models in fruit analysis has garnered significant attention due to its potential to revolutionize the agricultural sector and enhance crop monitoring. This paper presents a comprehensive review of recent research efforts in fruit analysis using Deep Learning techniques. The study delves into model selection, dataset characteristics, evaluation metrics, challenges, and future directions in this domain. Various model architectures, including classical Convolutional Neural Networks (CNNs) and advanced detection models like R-CNN and YOLO, have been explored for tasks ranging from fruit classification to detection. Evaluation metrics such as precision, recall, F1-score, and mean Average Precision (mAP) have been commonly used to assess model performance. Challenges, including data scarcity, labeling complexities, variations in fruit characteristics, and computational efficiency, have been identified and discussed. The paper also presents an overview of available datasets, encompassing both proprietary and publicly accessible sources. Future research directions involve exploring enhanced data augmentation, multi-modal integration, knowledge transfer across species, robustness in dynamic environments, improved computational efficiency, and practical integration of models into real-world agricultural systems. This review provides valuable insights for researchers and practitioners aiming to leverage Deep Learning for fruit analysis in the pursuit of sustainable agriculture and food production.

Analysis of Fruit Images with Deep Learning: A Systematic Literature Review and Future Directions

Lipid nanoparticles (LNPs) have emerged as promising carriers for delivering therapeutic agents, including mRNA-based immunotherapies, in various biomedical applications. The use of LNPs allows for efficient delivery of drugs, resulting in enhanced targeted delivery to specific tissues or cells. These LNPs can be categorized into several types, including liposomes, solid lipid nanoparticles, nanostructured lipid carriers, and lipid-polymer hybrid nanoparticles. The preparation of LNPs involves the manipulation of their structural, dimensional, compositional, and physical characteristics via the use of different methods in the industry. Lipids used to construct LNPs can also be derived from various biological sources, such as natural lipids extracted from plants, animals, or microorganisms. This review dives into the different types of LNPs and their preparation methods. More importantly, it discusses all possible biological sources that are known to supply lipids for the creation of LNPs. Natural lipid reservoirs have surfaced as promising sources for generating LNPs. The use of LNPs in drug delivery is expected to increase significantly in the coming years. Herein, we suggest some environmentally friendly and biocompatible sources that can produce lipids for future LNPs production.

Exploring the landscape of Lipid Nanoparticles (LNPs): A comprehensive review of LNPs types and biological sources of lipids

XAI-FruitNet: An Explainable Deep Model for Accurate Fruit Classification

This research proposes a sustainable municipal solid waste management (MSWM) system to address population growth, consumerism, and resource scarcity. It introduces a two-phase decision structure combining multi-attribute decision-making (MADM) tools. Phase one develops a hybrid risk-oriented fuzzy MADM tool for selecting optimal waste-to-energy technologies, considering environmental and technological factors. Phase two uses a fuzzy bi-objective multi-period mixed-integer linear programming model to design the MSWM supply chain efficiently under uncertainty. A case study in North Tehran demonstrates the framework’s practical applicability and effectiveness in real-world scenarios, highlighting that effective source separation boosts recycling, reduces costs, and provides social benefits. Sensitivity analyses offer insights to enhance waste segregation practices. The study emphasizes integrating economic, environmental, and social sustainability in waste management decision-making. By offering a novel, holistic approach, this research addresses existing gaps in systematic decision-making processes and provides a robust tool for municipalities to optimize strategies under uncertainty.

A sustainable network design for municipal solid waste management considering waste-to-energy conversion under uncertainty

A review of major trends, opportunities, and technical challenges in biodiesel production from waste sources

Energy transition towards electric vehicle technology: Recent advancements

Performance Analysis of Path Planning Algorithms for Fruit Harvesting Robot

Pakistan has been in a state of severe energy crisis since 2006-2007. Not much progress has been done since 2008 to cater the required energy needs. This is a major hindrance in the economy, leading to prolonged hours of load shedding. According to Energy Experts Group constituted by Economic Advisory Council in 2009 in Pakistan, it is estimated that the energy demand for Pakistan by the end of 2030 will increase by 64%. Steps therefore need be taken not only to install new power plants but also to implement policies which are energy optimized. According to survey, more than 50 % of energy in Pakistan is used in buildings. From amongst the buildings, a major portion of energy is utilized in office buildings. Special incentives should be given to companies by the government whose buildings are energy optimized.  Surveys and studies have shown that office buildings that have the HVAC (heating, ventilation and air conditioning) system optimized by energy conservation methods; have shown considerable amount of saving of electricity cost. A case study has been carried of an office building where the current electricity utilization is analyzed and optimization techniques are suggested. Further, calculations are done to show that if suggested methods are implemented for system optimization, energy saving of about 33.25% can be achieved.  Pakistan is currently facing crippling effects by the hands of energy crisis. If similar energy saving can be done by other office buildings on a larger scale considerable contribution can be made for the country to emerge from one of the most dreadful energy crisis ever.

HVAC Optimization of Energy Management: Case Study of an Office Building


 In order to meet the growing agricultural demands, modern machinery needs to be deployed and cheaper energy supply needs to be ensured. With advancements in artificial intelligence, fruit harvesting robots can improve both the quality and productivity of fruit picking and increase orange fruit exports. However, to ensure effective working of the fruit harvesting robot, an energy source that is both efficient and cost-effective is necessary. This paper aims at designing of a solar energy system for a lightweight fruit harvesting robot for orange orchards in Pakistan and conducting a feasibility study for the deployment of the robot for remote agricultural land. The site for fruit harvesting robot employment is decided by irradiance using random forest regression. Solar system sizing is done based on the design and energy requirements of the fruit harvesting robot. The Homer Pro software is used for simulation of the system to analyse the potential of using solar system for fruit harvesting robot in Sargodha, Pakistan. The results show that compared to hybrid system, a stand-alone system is a more cost effective, reliable, and efficient option with a payback time of 3.36 years and levelized cost of energy being $0.085 per unit kWh. This study proves that solar energy is a viable and cheaper solution for using modern agricultural machineries, like fruit harvesting robot, in remote areas in developing countries, like Pakistan, to enhance productivity and improving quality of the produce.

Design and Analysis of a Solar Energy System for a Fruit Harvesting Robot in Pakistan

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