To explore the feasibility of using UAV-LiDAR for measuring the leaf area index (LAI) of crop canopies, we employed UAV-LiDAR to scan sugarcane canopies during the tillering and elongation stages, acquiring canopy point cloud data. Subsequently, features such as average row height, projected row area, point cloud density at different canopy layers, and the ratios between these parameters were extracted. Three feature selection methods-partial least squares regression (PLSR), XGBoost feature importance (XGBoost-FI), and random forest-recursive feature elimination (RF-RFE)-were adopted to evaluate and identify the optimal input variables for modeling. With these selected variables, LAI inversion models were developed based on random forest (RF) and adaptive boosting (AdaBoost) algorithms, and their performance was assessed. Among the extracted features, the projected row area S_p and the total row point count C_total exhibited strong correlations with LAI, with correlation coefficients of 0.73 and 0.72, respectively. The AdaBoost-based LAI inversion model, using the projected row area S_p, average height H_avg, mid-layer point cloud density C_m, and total row point count C_total as input variables, achieved the best performance, with a coefficient of determination (R_v²) of 0.713 and a root mean square error (RMSE_v) of 0.25 on the validation set. This study provides an effective method for high-throughput acquisition of LAI in field crops, offering valuable scientific support for sugarcane field management and breeding efforts.
Plant Leaves/growth & development* ; Saccharum/growth & development* ; Algorithms ; Unmanned Aerial Devices ; Remote Sensing Technology/methods* ; Crops, Agricultural/growth & development*

In order to explore the feasibility of planting sweet sorghum in sugarcane growing area to prolong milling duration for bioethanol production, 15 varieties were sown monthly from March to September in Liuzhou of Central Guangxi Zhuang Autonomous Region. Yields of fresh stem, grain and leave were documented. The results showed that all varieties grew well when sown from March to August, but could not get mature when sown after late September. The high fresh stem yields were observed for the varieties Sart and PT3-S, 79.28 t/hm2 and 78.58 t/hm2 for single growing season, and 157.95 t/hm2 and 155.25 t/hm2 for two growing seasons. Ripening began from the end of June to late December, making the feed stock available for ethanol production from July to the end of December, even January next year.
Agriculture ; methods ; China ; Ethanol ; metabolism ; Feasibility Studies ; Plant Stems ; metabolism ; Saccharum ; growth & development ; metabolism ; Seasons ; Sorghum ; growth & development ; metabolism

Brix weight per stool (BW) of sugarcane is a complex trait, which is the final product of a combination of many components. Diallel cross experiments were conducted during a period of two years for BW and its five component traits, including stalk diameter (SD), stalk length (SL), stalk number (SN), stalk weight (SW), and brix scale (BS) of sugarcane. Phenotypic data of all the six traits were analyzed by mixed linear model and their phenotype variances were portioned into additive (A), dominance (D), additive x environment interaction (AE) and dominance x environment interaction (DE) effects, and the correlations of A, D, AE and DE effects between BW and its components were estimated. Conditional analysis was employed to investigate the contribution of the components traits to the variances of A, D, AE and DE effects of BW. It was observed that the heritabilities of BW were significantly attributed to A, D and DE by 23.9%, 30.9% and 28.5%, respectively. The variance of A effect for BW was significantly affected by SL, SN and BS by 25.3%, 93.7% and 17.4%, respectively. The variances of D and DE effects for BW were also significantly influenced by all the five components by 5.1%(85.5%. These determinants might be helpful in sugarcane breeding and provide valuable information for multiple-trait improvement of BW.
Alleles ; Body Weight ; Inheritance Patterns ; genetics ; Phenotype ; Saccharum ; anatomy & histology ; genetics ; growth & development