Editorial: Agricultural innovation in the age of climate change: a 4.0 approach

摘要

The contributions demonstrated that Agriculture 4.0 is a feasible and effective approach, encompassing various applications and scenarios. Specifically, Wang et al. investigated one of the most relevant and foundational elements of Agriculture 4.0, that is, the advancements in robotics required for enabling precision agriculture, which must overcome several issues related, for example, to terrain estimation (Vulpi et al., 2021). Specifically, the authors introduced a fuzzy backstepping controller, harmonising the trailer's trajectory with the tractor's, and ensuring higher precision in operations such as seeding and fertilisation, thus improving operational costs and the overall yield. A complementary contribution involving AI was proposed by Divyanth et al., who proposed a system based on single-stage object detectors to deal with post-harvest challenges in pathogen detection in potatoes, identifying potato eyes in a high-throughput scenario, and automating a traditionally labour-intensive process for disease management.Another relevant aspect concerns the accessibility to modern technology, which is often a constraint when dealing with the main users of the proposed advancements: breeders and farmers. To this end, Won et al. demonstrated the potential of using standard, low-cost devices, such as smartphones, for non-destructive crop monitoring. Specifically, the authors established a meaningful correlation between the leaf colour of lettuce, captured in the RGB colour space, and the plant's fresh weight across different nutritional treatments, thus opening the door for low-cost tools for crop health assessment and yield prediction. In another approach to output optimisation, Cao et al. proposed a simulated environment to enhance greenhouse strawberry yields by optimising the patterns of honeybee pollination. Their work revealed that specific interplanting strategies, along with staggering planting times, can significantly improve crosspollination and fruit weight by mitigating competition among flowers for pollinators, thus demonstrating how numerical models can be used to understand and manage complex biological systems, providing valuable and effective guidance for breeders.Finally, Zhang et al. explored a complementary problem solvable by the adoption of modern digital solutions, that is, using data analytics and intelligence to address the economic volatility that is inherently built into the agricultural sector, and which is of special interest in dynamic scenarios such as the ones we are currently living in. This research dealt with predicting agricultural commodity prices, using a BiLSTMbased hybrid model to provide highly accurate price forecasts for key commodities, including ginger, garlic, pork, and soybean futures. This predictive power can be an incredibly useful asset for stabilising the market side, thus optimising the supply chain and supporting political decision-makers in their policymaking.Together, all these different research paint a compelling picture of what Agriculture 4.0 can offer to the community, highlighting how digital innovation offers a diverse toolkit to address long-standing and emerging challenges. The research community operates with a wide range of applications, from soil management to precision control of field robots, focusing on intelligent automation, yield optimisation via accessible devices, simulation-based optimisation and adaptation, and data-driven forecasting of exogenous and economic trends. Therefore, the contributions underscore the transformative potential of Agriculture 4.0 in building a more effective, resilient, and sustainable agricultural landscape, tailored to navigate the challenges and complexities we will face in this century.