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BACKGROUND: The wettability of target crop surfaces affects pesticide wetting and deposition. The structure and properties of the leaf surface of litchi leaves undergo severe changes after infestation by Aceria litchii (Keifer). The objective of this study was to systematically investigate the surface texture and wettability of litchi leaves infested. RESULTS: Firstly, the investigation focused on the surface structure and physicochemical properties of litchi leaves infested with Aceria litchii. Subsequently, different levels of Contact Angle (CA) were measured individually on the infested litchi leaves. Lastly, Surface Free Energy (SFE) and its polar and dispersive components were calculated using the Owens‐Wendt‐Rabel‐Kaelble (OWRK) method. The outcomes revealed distinctive 3D surface structures of the erineum at various stages of mycorrhizal growth. At stage NO. 1, the height of the fungus displayed a peaked appearance, with the skewness value indicating a surface characterized by more crests. In contrast, at stages NO. 2 and NO. 3, the surface appeared relatively flat. Furthermore, post‐infestation of litchi leaves, the CA of droplets on the abaxial surface of diseased leaves exhibited an increase, while the SFE value on the abaxial surface of leaves decreased significantly, in contrast to the abaxial surface of healthy leaves. CONCLUSION: The infestation behavior of Aceria litchii changed the surface structure and chemistry of litchi leaves, which directly affected the CA value of foliar liquids and the SFE value of leaves, changing the surface wettability of litchi leaves from hydrophobic to superhydrophobic. This study provides useful information for improving the wetting and deposition behavior of liquid droplets on the surface of infested leaves. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Plant height is regarded as a key indicator that is crucial for assessing the crop growth status and predicting yield. In this study, an advanced method based on solid-state LiDAR technology is proposed, which is specifically designed to accurately capture the phenotypic characteristics of plant height during the maize growth cycle. By segmenting the scanned point cloud of maize, detailed point cloud data of a single maize plant were successfully extracted, from which stem information was accurately measured to obtain accurate plant height information. In this study, we will concentrate on the analysis of individual maize plants. Leveraging the advantages of solid-state LiDAR technology in precisely capturing phenotypic information, the data processing approach for individual maize plants, as compared to an entire maize community, will better restore the maize's original growth patterns. This will enable the acquisition of more accurate maize plant height information and more clearly demonstrate the potential of solid-state LiDAR in capturing detailed phenotypic information. To enhance the universality of the research findings, this study meticulously selected key growth stages of maize for data validation and comparison, encompassing the tasseling, silking, and maturity phases. At these crucial stages, 20 maize plants at the tasseling stage, 40 at the flowering stage, and 40 at the maturity stage were randomly selected, totaling 100 samples for analysis. Each sample not only included actual measurement values but also included plant height information extracted using point cloud technology. The observation period was set from 20 June to 20 September 2021. This period encompasses the three key growth stages of maize described above, and each growth stage included one round of data collection, with three rounds of data collection each, each spaced about a week apart, for a total of nine data collections. To ensure the accuracy and reliability of the data, all collections were performed at noon when the natural wind speed was controlled within the range of 0 to 1.5 m/s and the weather was clear. The findings demonstrate that the root mean square error (RMSE) of the maize plant height data, procured through LiDAR technology, stands at 1.27 cm, the mean absolute percentage error (MAPE) hovers around 0.77%, and the peak R2 value attained is 0.99. These metrics collectively attest to the method's ongoing high efficiency and precision in capturing the plant height information. In the comparative study of different stem growth stages, especially at the maturity stage, the MAPE of the plant height was reduced to 0.57%, which is a significant improvement compared to the performance at the nodulation and sprouting stage. These results effectively demonstrate that the maize phenotypic information extraction method based on solid-state LiDAR technology is not only highly accurate and effective but is also effective on individual plants, which provides a reliable reference for applying the technique to a wider range of plant populations and extending it to the whole farmland. [ABSTRACT FROM AUTHOR]