Algal-bacterial consortium mediated system offers effective removal of nitrogen nutrients and antibiotic resistance genes.

The sulfonamide antibiotic resistance genes (ARGs) especially sul1 was identified as the dominant in eutrophic water. The performance of Chlorella vulgaris-B. licheniformis consortium toward sul1 removal, total nitrogen (TN) removal, and the mechanism of sul1 removal was investigated.

The effect of carbon-based copper nanocomposites on Microcystis aeruginosa and the movability of antibiotic resistance genes in urban water.

The presence of Microcystis aeruginosa (M. aeruginosa) can affect the transference of antibiotic resistance genes (ARGs), and the presence of carbon-based copper nanocomposites (CCN) can affect the growth of M. aeruginosa.

Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review.

The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultural applications. However, machine vision and its precise positioning still have many technical difficulties, making it difficult for most harvesting robots to achieve true commercial applications.

Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field.

Fruit detection in real outdoor conditions is necessary for automatic guava harvesting, and the branch-dependent pose of fruits is also crucial to guide a robot to approach and detach the target fruit without colliding with its mother branch. To conduct automatic, collision-free picking, this study investigates a fruit detection and pose estimation method by using a low-cost red⁻green⁻blue⁻depth (RGB-D) sensor. A state-of-the-art fully convolutional network is first deployed to segment the RGB image to output a fruit and branch binary map.

Recognition and Matching of Clustered Mature Litchi Fruits Using Binocular Charge-Coupled Device (CCD) Color Cameras.

Recognition and matching of litchi fruits are critical steps for litchi harvesting robots to successfully grasp litchi. However, due to the randomness of litchi growth, such as clustered growth with uncertain number of fruits and random occlusion by leaves, branches and other fruits, the recognition and matching of the fruit become a challenge. Therefore, this study firstly defined mature litchi fruit as three clustered categories.

Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components.

The automatic fruit detection and precision picking in unstructured environments was always a difficult and frontline problem in the harvesting robots field. To realize the accurate identification of grape clusters in a vineyard, an approach for the automatic detection of ripe grape by combining the AdaBoost framework and multiple color components was developed by using a simple vision sensor. This approach mainly included three steps: (1) the dataset of classifier training samples was obtained by capturing the images from grape planting scenes using a color digital camera, extracting the effective color components for grape clusters, and then constructing the corresponding linear classification models using the threshold method; (2) based on these linear models and the dataset, a strong classifier was constructed by using the AdaBoost framework; and (3) all the pixels of the captured images were classified by the strong classifier, the noise was eliminated by the region threshold method and morphological filtering, and the grape clusters were finally marked using the enclosing rectangle method.