Timely identification and forecast of maize tasseling date (TD) are very important for agronomic management, yield prediction, and crop phenotype estimation. Remote sensing-based phenology monitoring has mostly relied on time series spectral index data of the complete growth season. A recent development in maize phenology detection research is to use canopy height (CH) data instead of spectral indices, but its robustness in multiple treatments and stages has not been confirmed. Meanwhile, because data of a complete growth season are needed, the need for timely in-season TD identification remains unmet. This study proposed an approach to timely identify and forecast the maize TD. We obtained RGB and light detection and ranging (LiDAR) data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments. After CH estimation, the feature points (inflection point) from the Logistic curve of the CH time series were extracted as TD. We examined the impact of various independent variables (day of year vs. accumulated growing degree days (AGDD)), sensors (RGB and LiDAR), time series denoise methods, different feature points, and temporal resolution on TD identification. Lastly, we used early CH time series data to predict height growth and further forecast TD. The results showed that using the 99th percentile of plot scale digital surface model and the minimum digital terrain model from LiDAR to estimate maize CH was the most stable across treatments and stages (R²: 0.928 to 0.943). For TD identification, the best performance was achieved by using LiDAR data with AGDD as the independent variable, combined with the knee point method, resulting in RMSE of 2.95 d. The high accuracy was maintained at temporal resolutions as coarse as 14 d. TD forecast got more accurate as the CH time series extended. The optimal timing for forecasting TD was when the CH exceeded half of its maximum. Using only LiDAR CH data below 1.6 m and empirical growth rate estimates, the forecasted TD showed an RMSE of 3.90 d. In conclusion, this study exploited the growth characteristics of maize height to provide a practical approach for the timely identification and forecast of maize TD.

Maize (Zea mays L.) is a globally significant crop that plays a crucial role in feeding the world’s growing population. Among its various traits, plant height is particularly important as it affects yield, lodging resistance, ecological adaptability, and other important factors. Traditional methods for measuring plant height often lack cost-efficiency and accuracy. In this study, a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) was employed to collect point cloud data from 270 doubled haploid (DH) lines. This innovative application of UAV-based LiDAR technology was explored for high-throughput phenotyping in maize breeding trials. High-density genetic maps were constructed, and plant height was assessed at both single-plant and row scales across multiple developmental stages and genetic backgrounds. The findings revealed that for many varieties and small areas, single-plant-scale estimation accuracy was superior to row-scale estimation, with R² values of 0.67 vs. 0.56 and RMSE values of 0.12 m vs. 0.17 m, respectively. Two high-density genetic maps were constructed based on SNP markers. In Sanya and Xinxiang, the F₁DH and F₂DH populations identified 12 and 20 QTLs (quantitative trait loci) for plant height, respectively. This study successfully identified and validated QTLs associated with plant height, thereby revealing novel genetic loci and candidate genes. This research highlights the potential of UAV-based remote sensing to advance precision agriculture by enabling efficient, large-scale phenotyping and gene discovery in maize breeding programs.

Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield. Biomass is also a key trait in increasing grain yield by crop breeding. The aims of this study were (ⅰ) to identify the best vegetation indices for estimating maize biomass, (ⅱ) to investigate the relationship between biomass and leaf area index (LAI) at several growth stages, and (ⅲ) to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network (DNN) algorithm. The results showed that biomass was associated with all vegetation indices. The three-band water index (TBWI) was the best vegetation index for estimating biomass and the corresponding R², RMSE, and RRMSE were 0.76, 2.84 t ha⁻¹, and 38.22% respectively. LAI was highly correlated with biomass (R² = 0.89, RMSE = 2.27 t ha⁻¹, and RRMSE = 30.55%). Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm (R² = 0.83, RMSE = 1.96 t ha⁻¹, and RRMSE = 26.43%). Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices (R² = 0.91, RMSE = 1.49 t ha⁻¹, and RRMSE = 20.05%). Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass. Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices (R² = 0.87, RMSE = 1.84 t ha⁻¹, and RRMSE = 24.76%). The DNN algorithm was effective in improving the estimation accuracy of biomass. It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region.