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Spray systems in agriculture serve essential roles in the precision application of pesticides, fertilizers, and water, contributing to effective pest control, nutrient management, and irrigation. These systems enhance efficiency, reduce labor, and promote environmentally friendly practices by minimizing chemical waste and runoff. The efficacy of a spray is largely determined by the characteristics of its droplets, including their size and velocity. These parameters are not only pivotal in assessing spray retention, i.e., how much of the spray adheres to crops versus becoming environmental runoff, but also in understanding spray drift dynamics. This study introduces a real-time deep learning-based approach for droplet detection and tracking which significantly improves the accuracy and efficiency of measuring these droplet properties. Our methodology leverages advanced AI techniques to overcome the limitations of previous tracking frameworks, employing three novel deep learning-based tracking methods. These methods are adept at handling challenges such as droplet occlusion and varying velocities, ensuring precise tracking in real-time potentially on mobile platforms. The use of a high-speed camera operating at 2000 frames per second coupled with innovative automatic annotation tools enables the creation of a large and accurately labeled droplet dataset for training and evaluation. The core of our framework lies in the ability to track droplets across frames, associating them temporally despite changes in appearance or occlusions. We utilize metrics including Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) to quantify the tracking algorithm’s performance. Our approach is set to pave the way for innovations in agricultural spraying systems, offering a more efficient, accurate, and environmentally responsible method of applying sprays and representing a significant step toward sustainable agricultural practices.

Spray systems in agriculture serve essential roles in the precision application of pesticides, fertilizers, and water, contributing to effective pest control, nutrient management, and irrigation. These systems enhance efficiency, reduce labor, and promote environmentally friendly practices by minimizing chemical waste and runoff. The efficacy of a spray is largely determined by the characteristics of its droplets, including their size and velocity. These parameters are not only pivotal in assessing spray retention, i.e., how much of the spray adheres to crops versus becoming environmental runoff, but also in understanding spray drift dynamics. This study introduces a real-time deep learning-based approach for droplet detection and tracking which significantly improves the accuracy and efficiency of measuring these droplet properties. Our methodology leverages advanced AI techniques to overcome the limitations of previous tracking frameworks, employing three novel deep learning-based tracking methods. These methods are adept at handling challenges such as droplet occlusion and varying velocities, ensuring precise tracking in real-time potentially on mobile platforms. The use of a high-speed camera operating at 2000 frames per second coupled with innovative automatic annotation tools enables the creation of a large and accurately labeled droplet dataset for training and evaluation. The core of our framework lies in the ability to track droplets across frames, associating them temporally despite changes in appearance or occlusions. We utilize metrics including Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) to quantify the tracking algorithm's performance. Our approach is set to pave the way for innovations in agricultural spraying systems, offering a more efficient, accurate, and environmentally responsible method of applying sprays and representing a significant step toward sustainable agricultural practices. [ABSTRACT FROM AUTHOR]

Galaxy clusters are unique laboratories for studying astrophysical processes and their impact on halo gas kinematics. Despite their importance, the full complexity of gas motion within and around these clusters remains poorly known. This paper is part of a series presenting the first results from the new TNG-Cluster simulation, a suite comprising 352 high-mass galaxy clusters including the full cosmological context, mergers and accretion, baryonic processes and feedback, and magnetic fields. Studying the dynamics and coherence of gas flows, we find that gas motions in galaxy cluster cores and intermediate regions are largely balanced between inflows and outflows, exhibiting a Gaussian distribution centered at zero velocity. In the outskirts, even the net velocity distribution becomes asymmetric, featuring a double peak where the second peak reflects cosmic accretion. Across all cluster regions, the resulting net flow distribution reveals complex gas dynamics. These are strongly correlated with halo properties: at a given total cluster mass, unrelaxed, late-forming halos with fewer massive black holes and lower accretion rates exhibit a more dynamic behavior. Our analysis shows no clear relationship between line-of-sight and radial gas velocities, suggesting that line-of-sight velocity alone is insufficient to distinguish between inflowing and outflowing gas. Additional properties, such as temperature, can help break this degeneracy. A velocity structure function (VSF) analysis indicates more coherent gas motion in the outskirts and more disturbed kinematics toward halo centers. In all cluster regions, the VSF shows a slope close to the theoretical models of Kolmogorov (∼1/3), except within 50 kpc of the cluster centers, where the slope is significantly steeper. The outcome of TNG-Cluster broadly aligns with observations of the VSF of multiphase gas across different scales in galaxy clusters, ranging from ∼1 kpc to megaparsec scales. [ABSTRACT FROM AUTHOR]