Research on density grading of hybrid rice machine-transplanted blanket-seedlings based on multi-source unmanned aerial vehicle data and mechanized transplanting test.

• The first time to grade density of the hybrid rice machine-transplanted blanket-seedling before transplanting. • Reliability of improved extremely randomized trees of grading the seedling under various sowing rates. • Effectiveness of seedling density grading for improved field planting performances. Due to the influence of seed conditions and environmental factors on growing process of rice seedlings, there is a significant difference in the density of rice seedlings during planting, thereby, each row of multiple rows transplanter cannot take the accurate number of seedlings in transplanting and thus affect the performance of mechanized transplanting. Accurately grading the density of Hybrid Rice Machine-Transplanted Blanket-Seedlings (HRMTBS) before transplanting can improve mechanized transplanting performance by using seedlings of the same density level in the multiple rows of the transplanter. First, based on Unmanned Aerial Vehicle（UAV） multi-source data, the Vegetation Indices (VI) of spectral images and the Texture Features (TF) of RGB images were extracted in this paper. Then, an Improved Extremely Randomized Trees (IERT) algorithm, which introduces bootstrap sampling, random selection of feature subsets, and optimal node splitting, promoted the speed and achieved accurate grading of HRMTBS's density before mechanized transplanting. By selecting the optimal kernel for TF extraction, the effects of Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Extremely Randomized Trees (ERT), and IERT algorithm on seedling density grading were compared and analyzed. HRMTBS's density grading was conducted under different seeding rates of 50 g/tray, 70 g/tray, and 90 g/tray, which was used to verify the generalization ability of the IERT algorithm. Furthermore, based on the maximum information coefficient method, VIs and TFs were optimized, which simplified the classification model and significantly improved classification speed. The experimental results showed that the optimal kernel for TF extraction is 3 × 3. The IERT algorithm is superior to SVM, XGBoost, and ERT under three different seeding rates, with overall density grading accuracy of 95.4 %, 92.3 %, and 90.1 %, respectively. Refining VIs from spectral images and TFs information from RGB images effectively improves the algorithm's accuracy. Based on the IERT algorithm, the overall grading accuracy of three different seeding rates increased by 9.4 %, 8.0 % and 7.6 %, and 6.5 %, 5.7 % and 5.1 % for classification using multi-source data compared to using only spectral images (VIs) or RGB images (TFs), respectively. Under the condition of ensuring classification accuracy, the optimized TFs and VIs reduced the number of input model features from 18 to 5 and optimized the model structure, thus increasing the classification speed of three different seeding rates by 121.12 %, 96.83 % and 103.09 %, respectively. At last, field planting tests were conducted, and the performance of mechanized transplanting when using graded density of HRMTBS was effectively improved. In the mechanized transplanting test using the graded density of the HRMTBS of 50 g/tray, 70 g/tray, and 90 g/tray, the average miss planting rate was decreased by 8.28 %, 4.67 %, and 3.19 %, respectively. The average qualification rates reached 93.33 %, 91.06 %, and 82.22 %, with an increase of 10.00 %, 14.89 %, and 8.89 % compared with mechanized transplanting using the un-graded HRMTBS. The average uniformity increased by 14.09 %, 12.04 %, and 12.83 %, respectively. The research results can provide a reference for the precise and intelligent mechanized transplanting of hybrid rice. [ABSTRACT FROM AUTHOR]