A Point-Cloud Segmentation Network Based on SqueezeNet and Time Series for Plants

The phenotyping of plant growth enriches our understanding of intricate genetic characteristics, paving the way for advancements in modern breeding and precision agriculture. Within the domain of phenotyping, segmenting 3D point clouds of plant organs is the basis of extracting plant phenotypic parameters. In this study, we introduce a novel method for point-cloud downsampling that adeptly mitigates the challenges posed by sample imbalances. In subsequent developments, we architect a deep learning framework founded on the principles of SqueezeNet for the segmentation of plant point clouds. In addition, we also use the time series as input variables, which effectively improves the segmentation accuracy of the network. Based on semantic segmentation, the MeanShift algorithm is employed to execute instance segmentation on the point-cloud data of crops. In semantic segmentation, the average Precision, Recall, F1-score, and IoU of maize reached 99.35%, 99.26%, 99.30%, and 98.61%, and the average Precision, Recall, F1-score, and IoU of tomato reached 97.98%, 97.92%, 97.95%, and 95.98%. In instance segmentation, the accuracy of maize and tomato reached 98.45% and 96.12%. This research holds the potential to advance the fields of plant phenotypic extraction, ideotype selection, and precision agriculture.

Solovyeva E. On the taxonomic position of the enigmatic genus Tonkinodentus Schileyko, 1992 (Chilopoda, Scolopendromorpha): the first molecular data

The taxonomic position of the monotypic Vietnamese genus Tonkinodentus Schileyko, 1992 (for T.lestes Schileyko, 1992) has been considered in the light of the first obtained molecular data. Both molecular (28S rRNA) and morphological data support the position of this extraordinary eye-less genus within the family Scolopendridae Leach, 1814, a sighted clade, and thus suggests the polyphyly of blind scolopendromorphs. The species diagnosis has been amended and color images of T.lestes provided for the first time.