In recent years, precision agriculture has been introducing groundbreaking innovations in the field, with a strong focus on automation. However, research studies in robotics and autonomous navigation often rely on controlled simulations or isolated field trials. The absence of a realistic common benchmark represents a significant limitation for the diffusion of robust autonomous systems under real complex agricultural conditions. Vineyards pose significant challenges due to their dynamic nature, and they are increasingly drawing attention from both academic and industrial stakeholders interested in automation. In this context, we introduce the TEMPO-VINE dataset, a large-scale multi-temporal dataset specifically designed for evaluating sensor fusion, simultaneous localization and mapping (SLAM), and place recognition techniques within operational vineyard environments. TEMPO-VINE is the first multi-modal public dataset that brings together data from heterogeneous LiDARs of different price levels, AHRS, RTK-GPS, and cameras in real trellis and pergola vineyards, with multiple rows exceeding 100 m in length. In this work, we address a critical gap in the landscape of agricultural datasets by providing researchers with a comprehensive data collection and ground truth trajectories in different seasons, vegetation growth stages, terrain and weather conditions. The sequence paths with multiple runs and revisits will foster the development of sensor fusion, localization, mapping and place recognition solutions for agricultural fields. The dataset, the processing tools and the benchmarking results will be available at the dedicated webpage upon acceptance.

We used a single RTX 2080 Ti GPU. While T5-small underperforms its larger version, T5-11B, the latter has 11 billion parameters. Gabon and South Africa, are ranked 119th and 121st, respectively . ANSWER: 119th HUMAN: What is Gabon ' s ranking? ColdGAN: What is Gabon ' s rank on the HDI?

We used a single RTX 2080 Ti GPU. While T5-small underperforms its larger version, T5-11B, the latter has 11 billion parameters. Gabon and South Africa, are ranked 119th and 121st, respectively . ANSWER: 119th HUMAN: What is Gabon ' s ranking? ColdGAN: What is Gabon ' s rank on the HDI?

Abstract-- Accurate localisation is critical for mobile robots in structured outdoor environments, yet LiDAR-based methods often fail in vineyards due to repetitive row geometry and perceptual aliasing. We propose a semantic particle filter that incorporates stable object-level detections, specifically vine trunks and support poles into the likelihood estimation process. Detected landmarks are projected into a bird's eye view and fused with LiDAR scans to generate semantic observations. A key innovation is the use of semantic walls, which connect adjacent landmarks into pseudo-structural constraints that mitigate row aliasing. T o maintain global consistency in headland regions where semantics are sparse, we introduce a noisy GPS prior that adaptively supports the filter . Experiments in a real vineyard demonstrate that our approach maintains localisation within the correct row, recovers from deviations where AMCL fails, and outperforms vision-based SLAM methods such as RT AB-Map. Accurate localisation is a critical component of mobile robot navigation in outdoor environments [1].

ABSTRACT Purpose: This study examines the role of Artificial Intelligence (AI) in enhancing sustainability and efficiency w ithin the wine industry. It focuses on AI - driven intelligent management in viticulture, wine production, and enotourism. Need for the Study: As the wine industry faces environmental and economic challenges, AI offers innovative solutions to optimize resource use, reduce environmental impact, and improve customer engagement. Understanding AI's potential in sustainable winemaking is crucial for fostering responsible and efficient industry practices. Methodology: The research is based on a questionnaire survey conducted among Polish winemakers, combined with a comprehensive analysis of AI methods applicable to viticulture, production, and tourism. Key AI technologies, including predictive analytics, machine learning, and computer vision, are explored . Findings: AI enhances vineyard monitoring, optimizes irrigation, and streamlines production processes, contributing to sustainable resource manageme nt. In enotourism, AI - powered chatbots, recommendation systems, and virtual tastings personalize consumer experiences. The study underscores AI's impact on economic, environmental, and social sustainability, supporting local wine enterprises and cultural h eritage. Practical Implications: AI in winemaking and enotourism can lead to more efficient, sustainable operations that benefit producers and consumers. AI - driven solutions promote responsible tourism, enhance wine tourism experiences, and ensure the indu stry's long - term viability . Keywords: Artificial Intelligence, Sustainable Development, AI - Driven Management, Viticulture, Wine Production, Enotourism, Wine Enterprises, Local Communities JEL codes: A13, A14, C55, D81, L66, L83, M31, O33, Q01, Q13, Q16, Z32 1. INTRODUCTION Sustainability in the wine industry encompasses environmental stewardship, economic viability, and social responsibility. Sustainable viticulture aims to minimize environmental impacts while maintaining product quality.

This study explores the use of immersive virtual reality (VR) as a control interface for agricultural robots in vineyard disease detection and treatment. Using a Unity-ROS simulation, it compares three agents: a human operator, an immersive VR-controlled robot, and a non-immersive VR-controlled robot. During the scanning phase, humans perform best due to agility and control speed. However, in the treatment phase, immersive VR robots outperform others, completing tasks up to 65% faster by using stored infection data and optimized path planning. In yield-map-based navigation, immersive robots are also 38% faster than humans. Despite slower performance in manual scanning tasks, immersive VR excels in memory-guided, repetitive operations. The study highlights the role of interface design and path optimization, noting limitations in simulation fidelity and generalizability. It concludes that immersive VR has strong potential to enhance efficiency and precision in precision agriculture.

Robust robot navigation in outdoor environments requires accurate perception systems capable of handling visual challenges such as repetitive structures and changing appearances. Visual feature matching is crucial to vision-based pipelines but remains particularly challenging in natural outdoor settings due to perceptual aliasing. We address this issue in vineyards, where repetitive vine trunks and other natural elements generate ambiguous descriptors that hinder reliable feature matching. We hypothesise that semantic information tied to keypoint positions can alleviate perceptual aliasing by enhancing keypoint descriptor distinctiveness. To this end, we introduce a keypoint semantic integration technique that improves the descriptors in semantically meaningful regions within the image, enabling more accurate differentiation even among visually similar local features. We validate this approach in two vineyard perception tasks: (i) relative pose estimation and (ii) visual localisation. Across all tested keypoint types and descriptors, our method improves matching accuracy by 12.6%, demonstrating its effectiveness over multiple months in challenging vineyard conditions.

"It's not going to completely replace the human element of putting your boot into the vineyard, and that's one of my favorite things to do," he said. "But it's going to be able to allow you to work more smartly, more intelligently and in the end, make better decisions under less fatigue." Gamble said he anticipates using the tech as much as possible because of "economic, air quality and regulatory imperatives." Autonomous tractors, he said, could help lower his fuel use and cut back on pollution. As AI continues to grow, experts say that the wine industry is proof that businesses can integrate the technology efficiently to supplement labor without displacing a workforce.

Efficient crop production requires early detection of pest outbreaks and timely treatments; we consider a solution based on a fleet of multiple autonomous miniaturized unmanned aerial vehicles (nano-UAVs) to visually detect pests and a single slower heavy vehicle that visits the detected outbreaks to deliver treatments. To cope with the extreme limitations aboard nano-UAVs, e.g., low-resolution sensors and sub-100 mW computational power budget, we design, fine-tune, and optimize a tiny image-based convolutional neural network (CNN) for pest detection. Despite the small size of our CNN (i.e., 0.58 GOps/inference), on our dataset, it scores a mean average precision (mAP) of 0.79 in detecting harmful bugs, i.e., 14% lower mAP but 32x fewer operations than the best-performing CNN in the literature. Our CNN runs in real-time at 6.8 frame/s, requiring 33 mW on a GWT GAP9 System-on-Chip aboard a Crazyflie nano-UAV. Then, to cope with in-field unexpected obstacles, we leverage a global+local path planner based on the A* algorithm. The global path planner determines the best route for the nano-UAV to sweep the entire area, while the local one runs up to 50 Hz aboard our nano-UAV and prevents collision by adjusting the short-distance path. Finally, we demonstrate with in-simulator experiments that once a 25 nano-UAVs fleet has combed a 200x200 m vineyard, collected information can be used to plan the best path for the tractor, visiting all and only required hotspots. In this scenario, our efficient transportation system, compared to a traditional single-ground vehicle performing both inspection and treatment, can save up to 20 h working time.

The next phase in the fight against invasive spotted lanternflies (Lycorma delicatula) in the United States might just involve man's best friend. New research from Cornell University found that trained dogs were better than humans at detecting the lanternfly eggs that spend the winter in some landscapes, particularly forested areas. The findings are detailed in a study published December 26, 2024 in the journal Ecosphere. The spotted lanternfly is native to China, and was first detected in Pennsylvania in 2014. Since then, it has spread to at least 17 other states primarily in the eastern United States.