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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 removal efficiency of exogenous ARGs plasmids carrying sul1 reached (97.2 ± 2.3)%. The TN removal rate reached (98.5 ± 1.2)%. The enhancements of carbon metabolism, nitrogen metabolism, aminoacyl-tRNA biosynthesis, and glycoproteins had significant influences on sul1 and TN removals, under the premise of normal growth of algae and bacteria. The quantitative polymerase chain reaction (qPCR) results suggested that the absolute abundances of sul1 were low in algal-bacterial systems (0 gene copies/mL) compared with individual systems ((1 × 10 6  ± 15) gene copies/mL). The duplication of sul1 was inhibited in algal cells and bacterial cells. The algal-bacterial consortium seems to be a promising technology for wastewater treatment with a potential to overcome the eutrophication and ARGs challenges., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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. However, the effect of CCN on M. aeruginosa and ARGs is not fully understood. In this study, metagenomic sequencing was employed to analyze the movability of ARGs, their potential transfer, and possible hosts in photobioreactor treating urban water. The results uggested that 20 mg/L of CCN changed the composition and abundance of microorganisms in urban water, significantly promoted the flocculation of M aeruginosa, and decreased the composing proportion of Cyanophyta sp. and M aeruginosa. The results indicated that 20 mg/L of CCN significantly decreased the absolute abundance and ARGs proportions which mediated by plasmids (32.7 %). Furthermore, the lower co-occurrence probability of ARGs and mobile genetic elements (MGEs) suggested that 20 mg/L of CCN weakened the movability potential of ARGs mediated by MGEs such as plasmids. Among the 452 metagenome-assembled genomes (MAGs), 95 MAGs belonging to 41 bacterial categories were identified as possible ARG hosts. These results will provide insights into the control of harmful cyanobacteria and the management of ARGs in urban water., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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. This article reports the application and research progress of harvesting robots and vision technology in fruit picking. The potential applications of vision and quantitative methods of localization, target recognition, 3D reconstruction, and fault tolerance of complex agricultural environment are focused, and fault-tolerant technology designed for utilization with machine vision and robotic systems are also explored. The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. The future challenges brought about by recognition and localization success rates are identified: target recognition in the presence of illumination changes and occlusion environments; target tracking in dynamic interference-laden environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots. In the end, several open research problems specific to recognition and localization applications for fruit harvesting robots are mentioned, and the latest development and future development trends of machine vision are described., (Copyright © 2020 Tang, Chen, Wang, Luo, Li, Lian and Zou.)

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. Based on the fruit binary map and RGB-D depth image, Euclidean clustering is then applied to group the point cloud into a set of individual fruits. Next, a multiple three-dimensional (3D) line-segments detection method is developed to reconstruct the segmented branches. Finally, the 3D pose of the fruit is estimated using its center position and nearest branch information. A dataset was acquired in an outdoor orchard to evaluate the performance of the proposed method. Quantitative experiments showed that the precision and recall of guava fruit detection were 0.983 and 0.948, respectively; the 3D pose error was 23.43° ± 14.18°; and the execution time per fruit was 0.565 s. The results demonstrate that the developed method can be applied to a guava-harvesting robot.

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. Then an approach for recognition and matching of clustered mature litchi fruit was developed based on litchi color images acquired by binocular charge-coupled device (CCD) color cameras. The approach mainly included three steps: (1) calibration of binocular color cameras and litchi image acquisition; (2) segmentation of litchi fruits using four kinds of supervised classifiers, and recognition of the pre-defined categories of clustered litchi fruit using a pixel threshold method; and (3) matching the recognized clustered fruit using a geometric center-based matching method. The experimental results showed that the proposed recognition method could be robust against the influences of varying illumination and occlusion conditions, and precisely recognize clustered litchi fruit. In the tested 432 clustered litchi fruits, the highest and lowest average recognition rates were 94.17% and 92.00% under sunny back-lighting and partial occlusion, and sunny front-lighting and non-occlusion conditions, respectively. From 50 pairs of tested images, the highest and lowest matching success rates were 97.37% and 91.96% under sunny back-lighting and non-occlusion, and sunny front-lighting and partial occlusion conditions, respectively., Competing Interests: The authors declare no conflict of interest.

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. Nine hundred testing samples were used to verify the constructed strong classifier, and the classification accuracy reached up to 96.56%, higher than other linear classification models. Moreover, 200 images captured under three different illuminations in the vineyard were selected as the testing images on which the proposed approach was applied, and the average detection rate was as high as 93.74%. The experimental results show that the approach can partly restrain the influence of the complex background such as the weather condition, leaves and changing illumination., Competing Interests: The authors declare no conflict of interest.