Recognizing ripe tomatoes is a crucial aspect of tomato picking. To ensure the accuracy of inspection results, You Only Look Once version 9 (YOLOv9) has been explored as a fruit detection algorithm. To tackle the challenge of identifying tomatoes and the low accuracy of small object detection in complex environments, we propose a ripe tomato recognition algorithm based on an enhanced YOLOv9-C model. After collecting tomato data, we used Mosaic for data augmentation, which improved model robustness and enriched experimental data. Improvements were made to the feature extraction and down-sampling modules, integrating HGBlock and SPD-ADown modules into the YOLOv9 model. These measures resulted in high detection performance with precision and recall rates of 97.2% and 92.3% in horizontal and vertical experimental comparisons, respectively. The module-integrated model improved accuracy and recall by 1.3% and 1.1%, respectively, and also reduced inference time by 1 ms compared to the original model. The inference time of this model was 14.7 ms, which is 16 ms better than the RetinaNet model. This model was tested accurately with mAP@0.5 (%) up to 98%, which is 9.6% higher than RetinaNet. Its increased speed and accuracy make it more suitable for practical applications. Overall, this model provides a reliable technique for recognizing ripe tomatoes during the picking process.

Background: Banana plant height is an important trait for horticultural practices and semi-dwarf cultivars show better resistance to damages by wind and rain. However, the molecular mechanisms controlling the pseudostem height remain poorly understood. Herein, we studied the molecular changes in the pseudostem of a semi-dwarf banana mutant Aifen No. 1 (Musa spp. Pisang Awak sub-group ABB) as compared to its wild-type dwarf cultivar using a combined transcriptome and metabolome approach. Results: A total of 127 differentially expressed genes and 48 differentially accumulated metabolites were detected between the mutant and its wild type. Metabolites belonging to amino acid and its derivatives, flavonoids, lignans, coumarins, organic acids, and phenolic acids were up-regulated in the mutant. The transcriptome analysis showed the differential regulation of genes related to the gibberellin pathway, auxin transport, cell elongation, and cell wall modification. Based on the regulation of gibberellin and associated pathway-related genes, we discussed the involvement of gibberellins in pseudostem elongation in the mutant banana. Genes and metabolites associated with cell wall were explored and their involvement in cell extension is discussed. Conclusions: The results suggest that gibberellins and associated pathways are possibly developing the observed semi-dwarf pseudostem phenotype together with cell elongation and cell wall modification. The findings increase the understanding of the mechanisms underlying banana stem height and provide new clues for further dissection of specific gene functions.

Canopy volume is a crucial biological parameter for assessing tree growth, accurately estimating forest Above-Ground Biomass (AGB), and evaluating ecosystem stability. Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) are advanced precision mapping technologies that capture highly accurate point clouds for forest digitization studies. Despite advances in calculating canopy volume, challenges remain in accurately extracting the canopy and removing gaps. This study proposes a canopy volume extraction method based on an improved PointNeXt model, fusing ALS and TLS point cloud data. In this work, improved PointNeXt is first utilized to extract the canopy, enhancing extraction accuracy and mitigating under-segmentation and over-segmentation issues. To effectively calculate canopy volume, the canopy is divided into multiple levels, each projected into the xOy plane. Then, an improved Mean Shift algorithm, combined with KdTree, is employed to remove gaps and obtain parts of the real canopy. Subsequently, a convex hull algorithm is utilized to calculate the area of each part, and the sum of the areas of all parts multiplied by their heights yields the canopy volume. The proposed method’s performance is tested on a dataset comprising poplar, willow, and cherry trees. As a result, the improved PointNeXt model achieves a mean intersection over union (mIoU) of 98.19% on the test set, outperforming the original PointNeXt by 1%. Regarding canopy volume, the algorithm’s Root Mean Square Error (RMSE) is 0.18 m³, and a high correlation is observed between predicted canopy volumes, with an R-Square (R²) value of 0.92. Therefore, the proposed method effectively and efficiently acquires canopy volume, providing a stable and accurate technical reference for forest biomass statistics.