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As the global population is expected to reach 10 billion by 2050, the agricultural sector faces the challenge of achieving an increase of 60% in food production without using much more land. This paper explores the potential of Artificial Intelligence (AI) to bridge this "land gap" and mitigate the environmental implications of agricultural land use. Typically, the problem with using AI in such agricultural sectors is the need for more specific infrastructure to enable developers to design AI and ML engineers to deploy these AIs. It is, therefore, essential to develop dedicated infrastructures to apply AI models that optimize resource extraction in the agricultural sector. This article presents an infrastructure for the execution and development of AI-based models using open-source technology, and this infrastructure has been optimized and tuned for agricultural environments. By embracing the MLOps culture, the automation of AI model development processes is promoted, ensuring efficient workflows, fostering collaboration among multidisciplinary teams, and promoting the rapid deployment of AI-driven solutions adaptable to changing field conditions. The proposed architecture integrates state-of-the-art tools to cover the entire AI model lifecycle, enabling efficient workflows for data scientists and ML engineers. Considering the nature of the agricultural field, it also supports diverse IoT protocols, ensuring communication between sensors and AI models and running multiple AI models simultaneously, optimizing hardware resource utilization. Surveys specifically designed and conducted for this paper with professionals related to AI show promising results. These findings demonstrate that the proposed architecture helps close the gap between data scientists and ML engineers, easing the collaboration between them and simplifying their work through the whole AI model lifecycle. [ABSTRACT FROM AUTHOR]

The coupling coordination of agricultural technology innovation with food security is of great significance for high-quality agricultural development. By identifying the coupling coordination relationship between the two systems and the influencing factors, this paper aims to promote the virtuous cycle of coordinated development between regional agriculture and technology, as well as accelerate the realisation of high-quality development of Chinese agriculture. Therefore, this paper explores the spatial and temporal coupling characteristics of the two using the entropy value method, coupling coordination degree model, and exploratory spatial data analysis, and it screens for important influencing factors using the grey correlation model. The main results show that ① the coupling coordination relationship between agricultural technology innovation and food security in China is at a dissonant stage, but the value of the coupling coordination degree increases from 0.2076 to 0.3437 during the period of study, and the level of coordination gradually improves. ② The degree of coupling coordination in the provincial space exhibits a distribution pattern of "high in the east and low in the west". The areas of high value are primarily situated in the provinces of Shandong, Jiangsu, and other provinces along the southeastern coast of China, while the areas of low value are mainly located in the provinces of Qinghai, Ningxia, and other provinces in inland northwest China. ③ The Moran's index of provincial coupling coordination is greater than 0, showing a certain positive correlation, and there is a significant pattern of spatial aggregation. ④ The correlation coefficients between the influencing factors and the degree of coupling coordination are all greater than 0.35, indicating a moderate or high correlation, but the significance of technological support capacity and food distribution security increased over time. [ABSTRACT FROM AUTHOR]

This document, titled "Smart Strategies and Technologies for Sustainability and Biodiversity in Herbaceous and Horticultural Crops," discusses the current trends in modern farming systems that focus on improving the sustainability and biodiversity of agroecosystems. The document highlights various innovative agronomical strategies and new technologies that can help farmers reduce chemical inputs, preserve soil and water quality, decrease emissions, prevent pollution, and lower energy demand. The special issue presented in the document includes 12 papers, with 5 focusing on tree crops/orchards and 7 on herbaceous crops. The papers cover topics such as innovative techniques for tree crops, weed control methods, conservation agriculture, organic farming, cover crops, intercropping, and precision farming technologies. The document concludes that these strategies and technologies can effectively enhance sustainability and biodiversity in herbaceous and horticultural crops in different farming scenarios. [Extracted from the article]

The application of intelligent mobile robots in agriculture has emerged as a new research frontier, with the integration of autonomous navigation technology and intelligent agricultural robots being the key to the widespread adoption of smart agricultural machinery. This paper investigates comprehensive coverage path planning for tracked lawnmowers within orchard environments and addresses challenges related to task allocation and battery life. Firstly, in this study, the motion model of the tracked lawnmower was initially simplified based on assumptions about the orchard environment. Force analyses were conducted on each of its motion mechanisms. For the known orchard environment, a grid-based mapping technique was employed to model the orchard environment. Then, in order to improve the algorithm speed and reduce the number of turns during the lawnmower's traversal, the A* search algorithm was enhanced by combining the method of robot cluster traversal in the orchard environment. Finally, the improved method was simulated and verified in the MATLAB platform to investigate the influence of the number of lawnmower clusters on the path planning in the connected and non-connected orchards. Furthermore, two sets of on-site field trials were meticulously designed to validate the reliability, practicality, and efficacy of the simulation experiments. [ABSTRACT FROM AUTHOR]

Global agricultural production is facing unprecedented challenges as the environment becomes increasingly polluted. Governments, scientists, companies and farmers are beginning to focus on appropriate environmental remediation and sustainable agricultural technologies and practices. Innovative environmental adaptation/remediation technologies have been developed and validated, including physical/chemical remediation, green sorbents and bioremediation. The development of environmental remediation technologies has provided additional tools and methods for global agri-environment and food security. The aim of this Special Issue is to bring together 21 cutting-edge research papers covering the latest developments in soil conditions, inorganic pollution, organic pollution, remediation technologies and monitoring methods. The four themes of the Special Issue are "Improvement of agricultural soil properties", "Remediation of potentially toxic element pollution", "Remediation of organic pollution" and "Ecosystem and crop assessment". Based on the results of this Special Issue, we find that combining the latest environmental pollution problems with advanced remediation technologies, continuously promoting technological innovation and policy support, and developing integrated new technologies for environmental protection will be future areas of research for sustainable agro-environmental development. [ABSTRACT FROM AUTHOR]